This is the London that is about to take the mantle of financial capital of the world away from Amsterdam. This is the London that is the seat of an expanding British empire that is about to become the largest the world has ever seen. Shipping is the economy and a single voyage to the West Indies can make a merchant’s year, or ruin him. Insurance barely existed as an industry - there were no major insurance firms, regulators, actuarial tables. All these men had was the question of when the next ship would come home, and the information exchanged at Lloyd’s Coffee House was the closest thing they had to an answer.

In the corner would sit a handful of businessmen listening to the conversations around them, trying to get some signal about which ships would make it and which wouldn’t. They began making deals with shippers. These deals became an early form of shipping insurance, and as their business became larger, Edward Lloyd began charging them to sit at the tables in the coffee house to conduct that business.

This continued on for several decades, and eventually there was so much shipping business being conducted in Lloyd’s Coffee House that in 1734, they started publishing Lloyd’s List, a daily newspaper. Readers would learn about the day’s departures and arrivals, the cargo on each ship, where other country’s fleets were sailing, and where pirates had been seen - straight from the captains and sailors themselves. That information was so valuable that many shipping merchants were willing to pay a subscription for it. Nearly three centuries later, they still do.

That small coffee house on the Thames became Lloyd’s of London, one of the largest insurance markets in the world. It was built on the gossip exchanged there. The practice has a name, and 340 years later, it’s still the greatest edge in finance.

Scuttlebutt - originally Navy slang for the water cask on a ship’s deck where sailors gathered to trade news and rumors - like the office water cooler of the 1800s.

In 1958, Philip Fisher published Common Stocks and Uncommon Profits, one of the all-time great books on investing. He created a framework for a style of equity investing called the “scuttlebutt method”.

Fisher’s concept was to approach investing as if you were one of those merchants back at Lloyd’s Coffee House in the 1680s. If you’re interested in making an investment in a company, talk to everyone you possibly can who might know something about the business and its industry. Talk to the customers, competitors, suppliers, employees and former employees. You have to go beyond the financials, beyond the data - you need the anecdotes, the rumors, the scuttlebutt.

Warren Buffett has talked about Fisher’s type of investing a lot, including in 1998 when he credited it for his famous investment in American Express in the 1960s. He once said his investment style was 85% Ben Graham and 15% Philip Fisher. In the 2018 annual meeting, Buffett explained the scuttlebutt method was used by Charlie Munger in his famous investment in Costco, and by Ted Weschler and Todd Combs who took over Berkshire after Charlie and Warren.

Philip Fisher didn’t invent scuttlebutt investing, as people had already been doing it for hundreds of years - and probably even longer than that. But he did give it a name.

In today’s modern world of institutional investing, the scuttlebutt method is not really spoken about anymore. Not because it went away, not because scuttlebutt is a silly old-timey word (let’s be honest, it is).

It’s not spoken about because it won. The scuttlebutt method has become the foundation of how all fundamental investors do diligence on potential investments.

The modern phrase for the scuttlebutt method is primary research. The qualitative kind, which involves talking to lots of people who may have a perspective on a market you’re looking at. That’s the default setting. It’s what every serious investor does before writing a check. It’s what every corporate development team does before making an acquisition. What competitive intelligence teams do to inform their view of a market. What product teams do before launching in a new category.

Hedge funds run quarterly channel checks. PE firms build huge diligence teams to interview customers before a buy-out. Every major enterprise has corporate development and corporate strategy teams made up of people who could just as easily be running due diligence at a PE firm.

The specifics vary, but the method doesn’t. Every one of those processes is Philip Fisher’s scuttlebutt method, institutionalized and rebranded for modern investing as primary research.

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A career of scuttlebutt

The summer of 2016 was the first time I did real investment research. I was working as an intern at Folger Hill Asset Management, a spin-off of Point72. The PM I worked for asked me to research Advance Auto Parts, the auto parts retailer, and bring him my point of view on the stock after two weeks of research. He gave me four research reports on the stock - from Goldman Sachs, JP Morgan, Morgan Stanley, and Cleveland Research. I asked him what this Cleveland Research company was, and he explained to me that even though I’d never heard of them, they were his favorite research provider on Wall Street for the stocks he covered.

I ended up joining Cleveland Research after college as a sell-side equity analyst, and it was my first exposure to enterprise tech companies. I covered businesses like Cisco, Palo Alto Networks, VMware, Arista, Splunk, Okta, and others. Cleveland was and is unique on Wall Street because they do one thing and one thing only - primary research. They build personal relationships with people in their covered companies’ ecosystems including customers, suppliers, competitors, ex-employees, and anyone close enough to the business to see it clearly.

Every quarter I’d call the same people, and ask the same questions. How’s business going? What did you think of this new product? What are you hearing from customers? How do you expect next quarter to go? What are you hearing from the management team that’s misaligned with what you’re seeing on the ground?

We would then synthesize those calls and write reports from the information we gathered, and sell it to hedge funds and asset managers that traded the stocks we covered.

At the time, I didn’t know I was running a playbook that dated back to a London coffee house from the 1680s.

After two years at Cleveland Research, I joined Alex Zukin at RBC, where I brought the primary research method I learned at Cleveland Research to the stocks his team covered. We ended up being ranked #1 on Wall Street by Institutional Investor in 2020, and we were slated to be on the UiPath IPO, which is when I met Brandon Deer and Daniel Dines.

In my first interview with Brandon, he asked me how I did research on the stocks I covered. I explained to him that I became friends with lots of people in the company’s ecosystem, which for UiPath included their major channel partners like the big 4 consulting firms. What started as a job interview became him asking me about what I was hearing from them. He ended up hiring me to help build Crew Capital with him and Daniel.

From public stocks to early-stage venture, the one through-line of my career has been figuring out what’s happening on the ground and using that to inform my investment thesis.

Information is the oldest edge in finance, 340 years after Edward Lloyd opened his coffee house. What’s changed is how it gets obtained and by whom.

Three eras of primary research

The scuttlebutt era ( ? - 1998). Since at least the 17th century, but probably since the beginning of human commercial activity, people used information to get an edge in business. In the 20th century, that meant Philip Fisher-style information gathering. You built personal relationships and worked your rolodex trying to gather anecdotes to paint a picture of a business. It took a career to build relationships with the people who were in the know. If you couldn't, you didn't have the signal.

The expert network era (1998 - 2024). GLG launches in 1998 and productizes scuttlebutt. Any corporation or investment firm can use GLG to procure very specific expert personas and get them on an hour long phone call. Calls run upwards of $1,000-3,000 and a multi-billion dollar industry is born with Guidepoint, Third Bridge, AlphaSights, and more.

The era peaked when GLG filed to go public in October 2021 at the top of the ZIRP cycle, then pulled the IPO months later. Meanwhile, Tegus was already changing the shape of the industry, growing from $13M in ARR at the start of 2021 to $100M by the end of 2022 by inverting the privacy model. Instead of keeping expert calls private, Tegus clients opted into sharing their transcripts with every other Tegus client. That shared library became so valuable that AlphaSense acquired them for $930M in 2024.

Expert networks solved access. They didn’t solve synthesis. A human still had to run every call, take notes, and make sense of twenty 20-page transcripts. You could buy more calls. You couldn’t compound what you learned from them. Every call was a one-time transaction, consumed by one team, stored in their heads and in their notebooks.

Worst of all, there was no way to approach expert calls with any rigor. You could ask a competitor’s executive how the landscape was changing, but you couldn’t quantify it or measure it repeatedly over time. You had to go off their tone and your gut, just like the merchants at Lloyd’s Coffee House did.

That’s changing with AI, and with Qualitate.

Investing in Qualitate

In January 2024, a friend in venture forwarded me a deal he’d passed on. The email said “Qualitate AI - captures voice data from thousands of technology buyers across 400+ technology markets”. Something clicked. The founder was trying to use AI to do the thing I’d spent my career running by hand. After meeting Sagar Kadakia and coming to a shared vision for the company, I led Qualitate’s first financing round. I wrote more about the story last week.

Two years after I first invested in the idea of Qualitate, Sagar has built the company into a multi-million-dollar-revenue business serving some of the largest tech companies, private equity firms, hedge funds, and asset managers. Their team is now 25 people, all working in-person from their office in New York.

Here’s how it works.

• Data platform. Qualitate’s AI runs thousands of expert conversations in parallel, transcribes them, and aggregates them into a single queryable dataset. Customers ask it questions the way they’d ask a research analyst. How have agentic coding tools impacted build-vs-buy decisions at large enterprises in 2026? Qualitate scans the full dataset and returns an answer grounded in direct quotes from specific transcripts, with the underlying conversations one click away.

• Custom AI Moderator. Customers can also run their own custom research through Qualitate’s AI moderator. It generates surveys, makes a plan, interviews experts, and synthesizes the insights from all of the interviews. Research teams can massively scale up their operations without increasing their overhead.

• Agentic workflows. Customers can embed Qualitate’s data into the tools they already use, triggering workflows across their business. For example, interviewing an expert, interviewing an employee, analyzing a data room, generating an investment memo. All of it powered by the largest structured database of primary research in the world.

The Continuous Intelligence Era (2024 - ?)

Qualitate has ushered in the third era of primary research, the continuous intelligence era. If the scuttlebutt era was built on relationships, and the expert network era was built on access, then the continuous intelligence era is built on data.

Each era of primary research was defined by a different scarce resource, and each new era built a layer on top of the one before it.

In the scuttlebutt era, the scarce resource was the relationship. A sell-side analyst’s edge was the rolodex he spent a decade building. The CIO at a Fortune 500, the head of sales at a channel partner, the former engineer at the largest competitor. Those relationships took years to build and they didn’t transfer from one investor to another.

The expert network era commoditized the relationship. GLG didn’t build the rolodex for you, instead they just rented you one that was larger than you could ever build on your own. The unit of value became the expert call, a billable hour with a specific person who knew a specific thing. That is what the expert network industry was built to sell.

Qualitate doesn’t commoditize the expert or the call. Both are irreplaceable. A great expert who gives great information is still a high signal source of alpha. The structure around the calls is what changes. The unit of consumption is no longer a single call or transcript. It’s the aggregate - all of the calls packaged into a structured dataset that is queryable in natural language. Every future conversation builds on the last one, and the insights compound.

That is the scarce resource of the third era - the data itself - the continuously growing corpus of structured insights, and the AI that sits on top of all that context.

Unlike the relationship or the expert call, the dataset compounds. More conversations make the dataset larger. The larger dataset makes the AI sharper. The sharper AI asks better questions in the next conversation, which feed the dataset, which sharpen the AI. Each layer makes the one above it harder to replicate.

Qualitate will win the third era by owning the research workflows that come after the call. The platform, the dataset, and the AI trained on top of it get packaged into a product that answers any question about any market.

As the models get better and the dataset gets larger, the AI will get better at identifying what answers don’t exist yet, and what questions to ask them. And that intelligence will live inside the workflows of every firm that used to buy expert calls one at a time.

If the expert networks of Era Two sold hours, then Qualitate sells the accumulated intelligence of all of them.

Joining Qualitate full-time

The small coffee house on the Thames is now a lot bigger.

The edge is still there - it’s just no longer a phone call with a channel partner in Chicago or an hour-long GLG call. Now it is an always-on AI, building a primary intelligence database that expands with every conversation.

After two years of supporting Sagar and the Qualitate team, I’ve joined full-time as VP of Operations. It’s my first operating role in my career, after five years of venture investing and four years of sell-side equity research.

I’ve spent the last ten years running diligence processes by hand, the tried-and-true Philip Fisher playbook that every fundamental investor uses. I’m going to spend the next ten helping build the AI that powers that playbook.

It’s my first operating role in my career, after five years of venture investing and before that four years as an equity research analyst on Wall Street covering software.

Here’s how it happened.

In January of 2024 a friend of mine in the venture industry told me about a deal he saw but decided to pass on. He forwarded me an email - “Qualitate AI - captures voice data from thousands of technology buyers across 400+ technology markets”.

It may not have been interesting to my friend, but something in my head clicked…Voice data. Thousands of buyers. 400+ markets. Someone was trying to use AI to conduct due diligence, which I had spent my career doing as both a venture investor and public markets analyst.

That someone was Sagar Kadakia, Qualitate’s founder and CEO. In Sagar’s view, the expert call wasn’t the product. It was the wedge to build something bigger - an AI system continuously interviewing experts across the economy, aggregating their insights into the world’s largest structured database of primary intelligence, and compounding with every new conversation.

Three weeks later I wrote the first check into the company, a $1.2M pre-seed investment that I led at Crew Capital. I and my team at Crew re-invested multiple times, including after introducing Sagar to my friend Jesse Beyroutey at IA Ventures, who led the seed round last year.

Fast forward two years from when I led Qualitate’s inception round, when the company was merely an idea in Sagar’s head, and they have grown revenue 30x over the last year, serve dozens of customers, and have over 350,000 minutes of AI-led conversations on their platform. Customers include $100B+ market cap companies, asset managers with trillions in AUM, and 6 of the top 15 PE firms. And their team is now 25 people, all working in-person from their office in New York.

Here’s how Qualitate’s technology works.

• Data platform. Qualitate’s AI runs thousands of expert conversations in parallel, transcribes them, and aggregates them into a single queryable dataset. Customers ask it questions the way they’d ask a research analyst. How have agentic coding tools impacted build-vs-buy decisions at large enterprises in 2026? Qualitate scans the full dataset and returns an answer grounded in direct quotes from specific transcripts, with the underlying conversations one click away.

• Custom AI Moderator. Customers can also run their own custom research through Qualitate’s AI moderator. It generates surveys, makes a plan, interviews experts, and synthesizes the insights from all of the interviews. Research teams can massively scale up their operations without increasing their overhead.

• Agentic workflows. Customers can embed Qualitate’s data into the tools they already use, automatically triggering workflows across their business. For example, interviewing an expert, interviewing an employee, analyzing a data room, generating an investment memo. All of it powered by the largest structured database of primary research in the world.

For ten years, I’ve run these diligence processes by hand. I’m excited to spend the next ten helping to build the AI that runs these processes, freeing up research teams at corporations and investment firms to do higher-value work.

I’ll have a longer piece coming on Infinite Runway this weekend, covering my thesis on the market, the new category that Qualitate is creating, and how its AI will reshape existing primary research workflows.

For today, thank you to everyone who’s supported me in this journey - Sagar, the Qualitate team, my partners from Crew, the founders I work with, and you, Infinite Runway’s readers.

Sebastian had grown up in the Dominican Republic, come to Miami as a teenager for high school, and that’s where he met Michael. They became close friends, and after college linked up to start Rilla together, along with their cofounders Chris and Lukasz.

Sebastian had no technical skills. As he put it, he never had a real job - despite being an econ and data science grad from NYU. After college he started doing standup in NYC, which he explained was like building software, because you have to keep iterating and getting better with each iteration.

Sebastian, Michael, Chris, and Lukasz had been grinding on their startup for nearly 3 years by the time my partner Brandon and I met them in the fall of 2022. Something had clearly started to work though in the last few months. We did a four week diligence sprint and saw the market was wide open with no competitors, customers loved the product, and the metrics had just started to inflect.

But what I remember most from that dinner was that Sebastian could sell. Not in the polished suit-and-tie way, but rather in the way that only someone who’s stood alone on stage and bombed enough times to learn how to hold a room can sell.

When Brandon and I left that dinner, he looked at me and said, you think that Sebastian guy is a little crazy? I said yes…and we have to do this deal.

Rilla was the first time I ever led a financing round in a startup. It was the biggest check I had pushed since I joined Crew. And not only was it personally my first time leading an investment, but it was the first time our firm Crew Capital had ever led an investment.

Until then, Crew Capital was an angel fund writing small checks alongside of lead investors like Sequoia, Accel, and IVP - some of the best venture capital firms in the world, which we had strong relationships with from UiPath, the company that my partners Daniel and Brandon had built. But those firms weren’t in on this deal with us. We were on our own for the first time, with only the diligence we did and the conviction we developed in Rilla’s founders, who had come from the unlikeliest of places.

Since then Rilla has become one of the fastest growing startups in NYC, and is the category-defining company in sales coaching software for the physical world. And it is the best investment I made in my career as a venture capitalist.

Last week I closed an incredible chapter at Crew Capital, stepping back from what was a 5-year run at the firm - starting as an associate and leaving as a partner after having helped build the firm from the ground up.

This essay is about what venture capital taught me, why the venture industry matters, and why I chose to leave it.

The “Job”

Venture capital may be the best job in the world.

At its best, practicing venture capital is about spending time meeting with some of the smartest people in the world, listening to their ideas for a business they are so enamored by that they’ve chosen to throw away any semblance of balance in their life, any professional cachet they’ve established, to go off and do this thing that has an overwhelming likelihood of failure.

Founders do all of that with the knowledge that almost any alternative professional use of their time would be more financially rewarding on a risk-adjusted basis, while also being less stressful. Yet they pursue it anyway, because of a dogged passion and a belief their idea has a chance to improve the lives of their potential customers - even in ways that might seem small and boring to outsiders.

And your job - really the entire job of a VC - is to find those people, decide you believe in them, and give them the capital to go try.

If you’re thinking to yourself - please, I know venture capitalists and the job is far more cold emailing, gossiping, deal chasing, and momentum investing than any of what Dylan just described - trust your instincts.

Most of the time, the job is a far cry from any of that.

During my time as a VC, I saw a few thousand startups a year, met with around 600 each year, and invested in 4-10 annually. Run the math - 99% of the job is telling people “no”, which never became easier for me even after 5 years.

That plus a lot of cold emails, constant following up, and sitting through pitches where we both knew it wasn’t a fit but were too polite to end the meeting early. And then there is my “anti-portfolio”, or the deals I passed on that turned out to be great. It’s much larger than I’d like to admit - but I’ll save that for another post.

But when it works, and you meet a founder whose story and vision “clicks” - it is incredible. For a curious person, there are few greater sources of intellectual stimulation than learning about the frontier of technology from those on the cutting-edge. Calling it work is probably an injustice to actual work that doesn’t involve just doing things like networking, reading, and thinking.

That is the personal experience of the job, but as a student of the game of venture, the thing I came to appreciate is how much more important the system is than any one person’s experience.

From New Bedford to Silicon Valley

As Jeff Bezos put it, the primary reason why America’s economy is so dynamic, why the big tech companies are all here, why there is so much entrepreneurial success in the US, is the system of risk capital.

That system of risk capital Bezos talks about is the modern venture capital industry. It’s a system of norms, relationships, systems, and behaviors that funnels excess capital into unlikely ideas with asymmetric payoffs.

One of the incredible and underrated things about this system is that it’s existed for far longer than most people - even in the industry - realize or give it credit for.

VC: An American History is an incredible book that traces the system of risk capital back to the early 19th century. The first industry that this system gave rise to? Whaling.

Whale oil and whalebone were in massive demand during the industrial era of the mid 1800s in America. But catching a whale involved significant risk - small crews had to catch huge whales in freezing waters, risking drowning, capsizing, and worse. And it was expensive to buy a boat and pay a crew to risk their lives.

Whaling agents acted as intermediaries between investors with excess capital seeking high, uncorrelated returns. They charged fees and a share of the profits in return for coordinating with a ship captain and his crew. Whaling agents sought to build relationships with the best captains, engaged in repeat business with them, and built a portfolio of whaling excursions to spread risk. The best ones earned persistent returns over time, and those returns were governed by a power law - many expeditions would end up returning nothing, but the most successful returned life-changing payouts.

Sound familiar?

That system of risk capital transformed the center of the whaling industry, New Bedford, Massachusetts, into what the NY Times described as “probably the wealthiest place in America”, with an average per capita wealth of $35k in 1853, equivalent to $1M today.

The same factors that built the risk capital system around whaling - a cultural affinity toward entrepreneurial risk, the pursuit of capital gains, and an embrace of the lopsided outcome dispersion - are the same ones that undergird the risk capital system that Benno Schmidt first called “venture capital” in 1946.

And the results speak for themselves. The five most valuable companies in the S&P 500 - NVIDIA, Alphabet, Apple, Microsoft, and Amazon - which collectively account for nearly 30% of the S&P 500, all received venture capital financing in their early days.

If capitalism is the engine that created the modern economy, then the system of risk capital is the set of pistons converting the energy of excess capital into the mechanical motion of innovation that moves the world forward.

The Cost of Depth

If you’ve made it this far and are asking yourself how much longer is he going to go on saying venture capital is the greatest thing ever - I’m done.

There are plenty of issues with the industry.

However many of the issues associated with the industry are actually caused by its enormous impact on the economy. VC has been so spectacularly successful since its modest origins that a byproduct of that success has been massive growth in the number of firms, the amount of capital, and the number of people working in VC. That growth is an output of the industry's success, not an input, but it has real consequences.

Unlike many people, including many VC’s, I do not believe there is too much capital in venture. I don’t think fewer startups should be funded.

But there are certainly too many sources of capital. Too many venture firms with undifferentiated pitches crowding into the same deals, which drives up valuations, and exacerbates cyclicality. In the most heady times, like the ZIRP era of 2021 or the dotcom bubble (and maybe today?) - that leads to a misallocation of capital into relatively unproductive uses.

Worse, it leads to companies getting over-funded. As Bill Hewlett of Hewlett Packard said, “companies often die of indigestion, not starvation.” Taking on too much capital can lead companies to over-hire, lose focus, and provide a worse customer experience. Any investor who was active in 2021 saw this happen to companies in their portfolio.

And yet. The fact that we have rockets that can land themselves, computers that can reason in natural language, and are on the cusp of energy abundance - is in large part attributable to the fact that there is so much capital willing to be put to work behind the entrepreneurs dedicating their lives to these ideas.

Indeed, much of the growth of the VC industry in the last few years has been a direct reaction to the capital requirements of the AI labs. And even then they’ve outgrown the industry and turned towards raising from the mega cap tech companies.

The system is imperfect. But the imperfections are worth it for a risk capital ecosystem deep enough to fund the ideas that change the world, and magnetic enough to attract people from everywhere who want to build them.

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Elevated Ambitions

One of the best parts about my time in venture is the relationships with the founders we invested in.

The founder-VC relationship is unique. A lot of people try to analogize - but it’s not a friendship, it’s not a boss-employee relationship, it’s not an agent/client relationship. Any metaphor misses it.

At its core, the best version of the relationship is a shared vision. Before the investment, a mind-meld takes place where you align on the hypothesis for an outcome you both want to see in the world, and you both believe the startup can deliver if it executes effectively. The VC provides capital, a network of relationships, and serves as an accountability partner as the founder works towards testing that shared hypothesis. And along the way, as Ed Sim puts it, the best VCs know when to cheer on, when to chill out, and when to challenge the founders they work with.

Tyler Cowen once wrote, “At critical moments in time, you can raise the aspirations of other people significantly…simply by suggesting they do something better or more ambitious than what they might have in mind. It costs you relatively little to do this, but the benefit to them, and to the broader world, may be enormous. This is in fact one of the most valuable things you can do with your time and with your life.”

What Cowen doesn’t say, and what I learned over 5 years of doing this, is that the best version of this runs in both directions, at least when it comes to investors and founders. The best investors raise the ambition of their founders. The best founders raise the ambition of their investors. You push each other, and make each other sharper, more honest, more demanding…better.

The founders I worked with made me a better investor, a clearer thinker, and a more convicted partner to the next founder I worked with.

The Other Side

So. If it’s so great, why’d I leave?

Because at the end of the day, venture capital is portfolio management. You construct a portfolio of investments, hoping that some pan out, while knowing that most won’t. You diversify. Then once you get greater signal about which companies have the potential to be big, you double down and deploy your fund’s reserve capital into them. That reserve allocation is one of the things most people don’t realize separates the venture greats like Peter Thiel and Fred Wilson from everyone else.

Even before I was an investor, and I was working as an analyst on Wall Street covering software stocks, developing conviction in an idea was never the hard part for me when the data and my intuition told me it was the right call to make.

I have that conviction today. I believe that AI is going to be a decade-plus tech super-cycle, and that it will last longer and be more disruptive - even than most investors give it credit for. I think the models will keep getting better faster than people expect, even if we don’t get full ASI. I believe the rollout across industries will take years. And along the way, there will be macro cycles - but many, many great companies will get founded and funded.

Five years of watching founders bet everything on what they believe taught me more about conviction than any job ever could.

I want to concentrate into one company, and get hands-on building experience at a time where the very definition of what it means to build is changing. I’ll be sharing more soon.

For now, I’m grateful to my partners for empowering me to help build Crew with them, for the amazing LPs who allowed us to steward their capital, for the founders I’ve worked with who trusted me to be part of their company’s story, and for five incredible years in venture.

“That was one of the most impactful quotes from a roundtable my team at Crew Capital and I hosted with 30 founders and execs from our portfolio companies. Our guest was a product leader at one of the major AI labs, responsible for overseeing one of their most impactful products, loved by millions of users.”

Last week my team and I hosted a virtual roundtable with a product leader at one of the major AI labs, responsible for overseeing one of their most impactful AI products, loved by millions of users.

Last week, I wrote about how the AI labs are building task automation for every type of knowledge work at an industrial scale, what it means for software companies and for knowledge workers.

This week, in the second part of this series, I want to focus on what it means for the people actually building AI products. Because if there's one thing I took away from speaking with the PM behind one of the most widely adopted AI products in the world, it's that the craft of building great products hasn’t gone away, but what that craft requires has fundamentally changed.

Model Taste is the New Product Taste

The conversation opened with the idea that the most important skill for product builders in AI today is not traditional product intuition.

Rather, it’s a deep, hands-on sense of what the AI models are good at. Product managers and product leaders need to understand what the current model generation is actually good at, what the last generation couldn’t do, and what the next one might unlock for their product.

In the past, PM’s would be expected to have a deep understanding of their users and what they want to accomplish with their product. They understood the user’s lived experience inside their product, and sought ways to improve it by building new features.

Today, that simply isn’t enough.

AI models are getting so good, so quickly that PMs who don’t have a sense for model capabilities will see their product fall behind competitors who do.

Our product leader guest explained that in 2026, product teams need to commit to radical flexibility - a willingness to throw away several weeks of work and start over when a new model release redefines what’s possible in a feature the engineering team just finished building. That can be really hard - try telling an engineer who just shipped a new feature that a new model made it obsolete. It’ll go about as well as you expect.

And yet, the right answer might mean killing that feature entirely. A PM who understands that builds for the world that exists in the future, while the one who keeps the feature that the latest AI model just obviated is building for a world that existed in the past.

The only way to develop that sense though is to be constantly talking to - and building with - the models. Becoming a tinkerer is more important than ever when it comes to shipping AI.

For founders who consider themselves to also be the head of product, carving out precious time to develop model taste isn’t optional. As an investor, it’s clear when founders have it or don’t - can they speak to the new capabilities in their product from the latest AI model release, or what can’t their product do yet but is on the roadmap as the models improve?

And it extends beyond founders to the PMs they hire, as shipping product in 2026 isn’t the same as shipping it in 2016, let alone 2024.

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Eval-Driven Development

How can you measure if your company’s AI product is hitting the mark with customers?

If you follow AI news, you’ll have seen model providers talk about how their AI stacks up in benchmarks, which are standardized tests to compare models against each other on generic tasks like math or coding.

Benchmarks are like an SAT test - helpful in making rough comparisons, but just like an SAT score can’t tell you which human will succeed in a job, benchmarks don’t tell you whether a model can actually do the specific job your product needs it to do.

As our guest put it, benchmarks have become a waste of time. The only thing that matters is an evaluation, or an eval - a test you build yourself tailored to the exact tasks that the AI needs to perform in your product.

But creating evals is hard. Very hard. What does “good” look like when AI is generating an output, and the judgement of that output is subjective? What is a good investment memo? A good marketing plan? A good product roadmap?

Many AI product teams skip this step. They try the latest AI model in their product, the output looks fine, they ship it. Even at the AI lab our guest worked at, he said most product teams - internal and external - were not great at building evals.

He suggested a very tactical path forward for AI teams.

• Create a spreadsheet where each row is a task you expect users to accomplish, and each column is a step in the workflow or a generated output.

• For each output, create a rubric for what “good” looks like.

• Then generate outputs, grade them against the rubric, and diagnose why/where failures happened. Was there a prompting problem? An agent context issue? A data gap? A model limitation?

• Fix it, and try again.

Don’t focus on any particular threshold, but rather continuous improvement. Make your AI product progressively better through your judgement and intuition, and hold yourself accountable to the eval you built.

Everyone in and around AI is searching for and talking about moats.

And while it may sound too subjective to matter - evaluating your AI product’s output in a rigorous, task-specific, systematic way can be a moat, especially when everyone else is shipping on vibes like they’re Rick Rubin.

The Delegation Problem

One of the most honest admissions from the product leader in our discussion - and remember this is someone who helped lead one of the most popular AI products in the world today - came around discussing the challenges his company faced around getting users to move beyond treating their AI as a search engine, and instead getting it to actually take tasks on their behalf.

He broke down three barriers that AI companies will face in getting users to delegate workflows to the AI.

1. User education. Model capabilities are progressing so rapidly, the vast majority of users don’t know what the models are currently capable of. Many users are on free or lower-tier models, and don’t realize the upper threshold of work that frontier models can take on.

2. Trust. Going from data retrieval, or even asking a model for feedback on an idea or work output - is very different from trusting it to take an action on your behalf. Most users, especially those who aren’t the most tech savvy in the first place, simply do not yet trust AI to take actions on their behalf.

3. Delegation. Most people aren’t good at delegating anyway, even if it’s to other humans. Delegation is a learned skill. Since most people aren’t managers, they’re individual contributors, that means most haven’t honed delegation. People have a hard time delegating tasks to their AI.

If you’re at an AI company, and you’re struggling with how to go from getting the 10% of power users at your customers to the other 90%, then you’ll understand these problems intimately.

The solutions we discussed in the conversation focused around:

1. Abstraction. Make your product simple enough to accomplish a workflow that any employee at your customer can understand it.

2. Progressive disclosure. Let the power users who want to go deeper into your product click beyond the abstraction layer. But don’t compromise the user interface to address multiple personas that need different things from your product. Keep a clean UX and let power users go deeper, but make sure you give them enough power that you win over the early adopters.

3. Facilitate evangelization. Create a method for your users to share their wins with each other. Get your power users to create a company Slack channel showcasing their use cases, so that their colleagues can learn from them. Another example is building an online community with shared templates and tutorials.

The adoption curve of AI tools mirrors any other technology, just with a more compressed timeline. Companies shipping AI software products need to win the first 10% of power users, and let them pull in the other 90%.

Hiring PMs in 2026

As a VC, I’ve worked with founders on hiring product leaders, and know how challenging it can be especially given all of the changes in building products we’ve discussed already. It was reassuring to hear the product leader we spoke with talk about how much hiring evolved at the AI lab he worked at even just over the course of 2025.

His AI lab’s initial view was that every PM needed to understand both traditional enterprise software and AI. That meant hiring people who had experience at a software company and then moved to an AI startup. The venn diagram of overlap was small, making it hard for them to hire.

After a year of that, they realized their hiring strategy should be more nuanced, depending on which team was hiring the PM. Teams focused on building traditional enterprise features like permissions, integrations, IT auditability - they didn’t need to have the same AI expertise as the ones working on core AI agent features.

For those AI-focused PMs, they wanted to see much more than just someone who was vibe coding prototypes to speed up engineering velocity on their team. They looked for PMs who were so deep in the models they identified failure cases in AI outputs, built their own eval systems, had experience in improving outputs with those eval systems, debugged agent transcripts, and knew how to build software to address model failure - not merely prompt around it. In short, they wanted PMs at the cutting-edge of the ‘scaffolding layer’, as our guest put it.

He said the best PMs were not just using Claude Code to ship prototypes and put PRs in sandboxes, but actually reviewing the changes the AI was making as they shipped with it. Looking at every file update, every PR, asking questions, and building intuition and model taste.

So the PM role is splitting, and not every product leader needs to be an AI expert, but the ones closest to the AI need to be much more than vibe coders. Companies need to know which track they’re hiring for.

Building an AI-first Culture

A fundamental thesis I have about the economy today is that as AI models continue to improve, the delta between the companies with an AI-first culture and those who are slow to adopt AI will widen.

The S&P 500 of 10 years from now is going to feature companies who were aggressive in putting AI at the center of their company. And the startups who do this will outcompete their peers who don’t.

That doesn’t mean every employee at every company needs to be an AI expert - far from it. But companies need to implement cultures and practices that emphasize AI as a core operating lever, not an R&D project.

We discussed a few tactics companies can use that actually work:

Celebrate internal tool builders. Historically, being an engineer or a PM focused on building internal tools wasn’t as celebrated as building for customers. If you were an engineer at Meta or Google, you wanted to work on a product used by billions of people around the world, not a new engineering portal that fosters alignment across your R&D team. That is changing. At the major AI labs, people who build internal tools are celebrated. Their products are showcased at all-hands meetings. They get promotions. People are expected to allocate 10% of their time to building systems that their teammates can use internally.

Recalibrate hiring expectations. As we talked about, while not every product leader needs to be an AI expert, the ones who are shipping core AI features do need to be deep in the models.

Redeploy PMs as internal AI workflow consultants. For larger startups with many different functional teams, this was the most effective approach our guest said he’d seen. Startups can take a cohort of their most forward-thinking PMs and make their sole job to be going from team to team identifying repetitive workflows and building agents to solve them. Finance, recruiting, sales ops, etc. Not a one-off hackathon, but a repeatable process that is a structural commitment to embedding AI across the business.

The Shifting Ground

Building and investing in AI startups in 2026 is nothing if not disorienting. The speed of AI model development, the shifting competitive landscape, and the insatiable customer demand for AI create a confluence of factors that makes this one of the most exciting and most unsettling times to be working in technology.

I have to acknowledge the insights in this piece come from one product leader’s experience at one AI lab. The AI labs have near unlimited compute, world-class researchers, large customer bases, and huge balance sheets. Most startups don’t operate with the same resources. The urgency of these changes depends on how close your product is to the AI models themselves. And, the urgency may be even further reduced if the pace of model development slows.

But that’s not the bet I’d make, because no matter how the future evolves, what won’t change is that the underlying craft of building great products will continue to separate the best companies from the rest.

And yet the craft of building products has evolved. Product taste has become model taste. Evals are the new metrics. The classic product org of PMs + engineers + designers is changing as everyone has the ability to ship code. The companies that build a culture around embracing these shifts - celebrating internal tool builders, redeploying PMs as AI consultants, pushing every employee to develop model fluency - will compound their advantage every quarter.

The founders, startups, and investors who view the instability/uncertainty caused by AI as a feature rather than a bug will be the ones who build something durable, even if its foundation was built on what felt like quicksand.

Share Infinite Runway

A publicly-traded venture fund with just under half its assets in Anthropic, Databricks, and OpenAI?

Up 15x in a week. At least before falling back down 70% from its peak.

Meanwhile, rumors abound on Friday as Anthropic apparently leaked the drop of its latest model, “Mythos” (couldn’t they have picked a less ominous name?). Rumored to cost $10B to run, the model is allegedly in a new class of models beyond Anthropic’s state of the art Opus models.

The rumors around Mythos sent stocks down on Friday, with the S&P500 down -1.7%, and software down over -3.5% for the day. Are we living through Citrini 2028?

To paraphrase The Razor’s Edge blog post this week - the market has basically given up trying to figure out how AI is going to reshape the economy - but whatever it is, it’s decided the impact is going to be BIG.

And yet, for as much as the AI labs and their products - Google’s Gemini, Anthropic’s Claude, and OpenAI’s ChatGPT - have come to dominate the economy and our digital lives, we know remarkably little about how they think about the major questions facing the software industry and startup ecosystem.

Last week my team and I hosted a virtual roundtable with a product leader at one of the major AI labs, responsible for overseeing one of their most impactful AI products, loved by millions of users.

The goal: Get an insider’s perspective on how the AI labs are thinking about building new products, driving customer adoption, and what it all means for the early-stage tech ecosystem.

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RL Environments & the AI Labs

The first major takeaway from our conversation: The AI labs are currently mapping the knowledge work economy to reinforcement learning (RL) environments. If I lost you with that sentence, stay with me.

A reinforcement learning environment is essentially a sandbox in which AI can be trained on a particular task through iterative feedback. For example, say an AI lab wanted to build an AI agent with the ability to write investment memos for private equity firms.

• First, they would fine-tune the model on thousands of real investment memos, so the AI learns the proper format, structure, and style.

• Then, just like an investment associate, the AI is given a set of financial data on a company and is asked to build its own investment memo.

• Once the AI generates the investment memo, the AI lab has humans with experience in private equity grade the memo and give feedback - “the investment recommendation is too short”, “it missed a key risk”, “the style is wrong”, or “this factor was not included in the analysis”.

• Then the process is repeated: Give the AI financial data on a company, ask the AI to generate an investment memo, ask human graders to judge the investment memo, and give the feedback back to the AI. Again, and again, and again.

This is the simplified version of a process known as reinforcement learning with human feedback (RLHF).

In practice, today the most advanced RL environments go further. They don’t just ask a model to generate a document and have humans grade it. They actually put the AI agent in a simulated workspace where it has to take dozens of actions, like pulling data from an SEC filing, build a model in an Excel spreadsheet, synthesize information a pitch deck, and generate a final investment memo. It’s like dropping an analyst into a virtual office and seeing if they can do the job.

The concept of the RL “environment” is that idea of that virtual space - like a knowledge worker’s desktop computer setup - in which the AI agent has access to the internet, files, data, and tools that a human would have in doing their job.

• An RL environment for building an agent that could shop on behalf of humans would involve setting up a virtual online store where the agent could browse, add items to a cart, and check out.

• An RL environment for a legal AI agent would involve placing it in a simulated due diligence workflow. Reviewing contracts, flagging non-standard clauses, cross-referencing terms against a regulatory database, and producing a summary memo for a partner to review.

• For a marketing AI agent, an RL environment could consist of giving the AI a product brief, access to a simulated ad platform, and asking it to write copy, select audience segments, set bid parameters, and launch a campaign. And then grading it on whether the campaign hit its target KPIs.

The Industrialization of Task Automation

Reinforcement learning is one of the cutting edge techniques in training AI that’s enabled it to get as good at software development as most human engineers.

Software development was the first knowledge work / human domain that AI has become that good at, in part because there is so much code readily available that can be used as data to train the AI.

More simply, data is the raw material for training AI.

And the AI labs are spending a LOT on data.

Check out the post below, which went viral on X last week. The numbers are from Claude, so their accuracy is unclear. But most of the debate in the comments is that these numbers - $3B on the low end spent by the AI labs on buying data - are too low.

But what is all this data they’re buying? Very specific, domain-focused data that can be used to build RL environments.

As I mentioned, software engineering was only the first domain that the AI labs focused on automating. Today, if you ask cutting-edge software companies what percentage of their code is produced by AI, you’ll likely hear upwards of 80-90%.

So what comes after software? Everything.

The AI labs pay companies like Mercor to find domain experts who can help them generate domain-specific data, review the AI model’s output in a specific domain, and help the AI models close the gap from being 60% as good as a human to 95% as good as a human. A quick glance at Mercor’s homepage shows them recruiting experts to generate data in domains ranging from “Hebrew Professional Voice Actor” to “Physics PhD Experts” to “UK based B2B Financial Advisors”.

Anthropic already has plugins for financial services - my earlier example of AI writing private equity memos wasn’t hypothetical. They aren’t replacing a private equity associate yet, but over the course of this year, the incremental junior hire will be increasingly weighed against the out-of-the-box capabilities provided by the major AI labs.

The same process that made AI as good as most software engineers at writing code is now being replicated across the knowledge work economy - anywhere that has data to train on.

The product leader we spoke with was crystal clear. If a task produces an output that can be defined with a rubric and graded by an expert, then it’s in the crosshairs of automation from the AI labs.

The AI labs are coloring in the map of the knowledge work economy. They’re building RL environments one job function at a time, until no space on the map of the economy is left blank. Not dozens of tasks, but hundreds, if not thousands.

If that sounds like a research project - it’s not. Think of it more like an industrialization program. The volume, quality, and breadth of what AI models can do is going to explode this year as a result.

So if you’re a knowledge worker reading this - a VP of marketing, a lawyer, a product designer, a consultant, an accountant - the question isn’t whether or not your job function will eventually be colored in.

The AI labs don’t announce the RL environments they’re building and which specific capabilities the models are going to get good at next - they’re just shipping. One day your AI agent can’t do your job, the next day you wake up and Anthropic shipped a Claude Cowork plugin that can handle half your workflows.

The people who will thrive in their jobs over the next decade aren’t waiting for that day. They’re living inside of Claude, ChatGPT, and Gemini, and building an intuition for how AI can help them at work and where it falls short.

AI fluency is already leading to a divide among workers, as recent data from Anthropic shows: users who've been on Claude for six months or more have a 10% higher success rate in their conversations with it, even after controlling for the type of work they're doing.

The early adopters are pulling ahead, and the gap will grow.

The SaaS Selloff is an Overreaction, but the Warning is Real

The industrialization of task automation is the backdrop by which the entire “is software dead?” debate needs to be understood.

In The Great SaaS Crash of 2026, my core thesis was that software is not dead, but software companies that don’t rearchitect for an agent-first world will be.

I put two of the major software bear cases to our product leader guest directly:

1) If everyone can vibe code their software, why would they buy from a vendor?

2) If agents become the front door for knowledge work, do software companies shift from being valuable workflow systems, to fading into the background as mere data providers?

His answers at once confirmed what I’ve argued in blog posts previously, and created even more concern for me as I look at the landscape of incumbent software vendors.

On the vibe coding bear case

Vibe coding has become so powerful that most of us in the tech industry have that one friend who loves talking about the projects he’s vibe coding. Yes, if you ask my friends, I am that guy - but that’s a story for another post.

Our guest cited examples from his own network - one friend who built his own lightweight Bloomberg terminal to track stocks, and another who built an SAT tutoring app for his kid.

He explained that while those are interesting examples, they reflect the top 1% of power users.

Most users of software can’t actually conceive of how a product could be dramatically better for their needs. Even if someone intuitively feels that a product is clunky or overly complicated, translating that dissatisfaction into a design spec is an entirely different skill. And if you can’t articulate your vision of a better product, you’ll end up rebuilding whatever you currently use - wasting resources instead of just paying a vendor like HubSpot to provide that software app for you.

The bottleneck in building great software was never technical ability, but rather imagination - and you can’t prompt your way past that (at least, for now).

So the eternal buy vs. build question boils down to whether its a simple, self-contained use case where the app doesn’t do much. For those use cases, “build” will absolutely win more today than it used to.

But most large, valuable software companies don’t provide products that fall into that description. They are deeply embedded in workflows, have proprietary data, tacit knowledge from the users, and have been built to handle the thousands of edge cases that exist in organizational design across large enterprises. They’re not going to be vibe coded away.

The front door bear case

But what happens if ChatGPT and Claude become the front door to software applications, the same way that Google became the front door to websites in the 2000s?

Here, our guest was more bearish on software than I expected.

He explained that ephemeral user interfaces - dynamic UIs that appear inside of general chat agents on the fly - are coming this year. In fact, Anthropic just announced recently that Claude has new capabilities around building custom visuals. I bet OpenAI will follow suit soon.

Today, if you give Claude data and tell it to build you a chart - it can create a beautiful visualization for you.

But it goes further than charts. He described a future where AI agents will be able to generate interactive interfaces with familiar features. Buttons, input fields, and patterns that look and feel like the SaaS apps their users are already familiar with. If a user doesn’t know how to delegate a task to the AI, the agent will generate a UI to make it intuitive. For example, a project management board to help delegate work across a team.

These functional interfaces pull live data from the software systems underneath via MCP connectors, and then disappear when the user is done.

As the product leader we spoke with put it, the days of having 10 tabs open with different software applications are likely coming to an end. AI agents will increasingly become the front door in which knowledge work gets done.

Also importantly - our guest suggested that context windows that are functionally infinite are likely coming soon. The AI labs are working on models with much better memory and capabilities around how to use a knowledge base. If AI agents not only can access data in software and create ephemeral user interfaces, but also have perfect memory of everything a user does in every tool - that creates the potential for agents to handle far more complex multi-tool workflows.

The implication here that the market is likely pricing in with software companies trading at rock-bottom multiples is that if the user interaction layer moves from software applications to AI agents, then software companies are at risk of losing their end-user relationship. They become infrastructure, and while their data and ability to handle edge-cases still have value, it’s likely a lower value than the front-and-center position that enterprise software companies have had over the last 30 years.

When Google became the front door to the internet, websites didn't disappear. But it didn't matter how beautiful your website was or how strong your brand. Google captured distribution, discovery, and monetization. You were subservient to the interface layer. The same structural dynamic is threatening to play out between AI agents and software companies - only faster.

How incumbent software providers can position themselves

The opportunity for incumbent software vendors is to build the AI workflows that their users are demanding.

But most are blowing the opportunity.

Our guest described how most software vendors are tacking on a chatbot to their software application and calling it AI. He cited Salesforce as an example.

But what if the end user of software applications becomes an agent, not a human?

On that topic, he shared a story that every software executive should hear. He explained how the company standardized their communication on a collaboration software vendor. When they wanted to integrate their AI agents into the collaboration tool so that their human employees could talk to AI, the vendor shut it down - restricting them from using AI to search, retrieve information, or automate workflows inside of the tool.

The AI lab’s reaction wasn’t to work around the software vendor’s restrictions, but instead to ask themselves whether they should just build their own internal version. This is one of the key risks software bears have been warning about.

Some vendors like Workday are starting to charge customers to connect different systems, including AI, to their data. This makes sense - only if the software is fully optimized and open for AI agents to use the platform, on equal footing with their human counterparts.

If they don’t accept agents as first-class users in their tools, then software vendors will open themselves up to competitors who will - either existing vendors or internally-built tooling.

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What This Means

The AI labs are simultaneously getting better at doing the work of knowledge workers and becoming the place where said work gets done.

For companies in the software/AI ecosystem - both established and startups alike - the only response is to build a system that gets more valuable/effective as the models get better. Because as Anthropic just teased with Mythos - they’re about to get a lot better.

For knowledge workers, AI labs are building the infrastructure to come for every routine part of your job. The people who embrace the AI models frontier capabilities as a productivity tool will be 10x as productive. Early adopters are pulling ahead, and the gap is widening.

I opened this piece by noting that the market has essentially decided AI’s impact is going to be enormous, even if it can’t articulate how. After sitting across from someone who’s helped build one of the products at the center of that transformation, it seems like the market is right. But the way it unfolds will be more targeted, more structural, and faster than many realize.

This is Part 1 of a two-part series. Part 2 will cover what our conversation revealed about how to build AI products, and how startups can embrace AI to get ahead.

As of this writing, Block’s stock is up 13% today. Why? Two reasons I’ll explore in this post.

Bloat & Block

Here’s CEO Jack Dorsey’s email to the company - it’s worth clicking on and reading in full.

Jack frames the decision simply:

Twitter and news media are blowing up with the narrative that AI is taking people’s jobs.

The truth is that with the announced cuts, Block is trimming down to roughly 6,000 employees, still 50% larger than the 3,800 employees in its pre-covid, pre-hiring spree state.

As all bubbles do, the 2021 ZIRP era got the best of many of us. Block included. They grew from 3,800 to 13,000 employees between 2019 and 2023, and they’ve been trimming and restructuring since then. Now this is Block ripping the bandaid off.

And it follows a trend that other major tech companies pursued over the last few years in an effort to get fit. Meta laid off nearly 20% of its workforce across cuts in 2023 & 2024. Amazon, Google, Spotify, and others did too.

The difference is that in 2023, companies called it “efficiency”. In 2026, they’re calling it AI.

Leverage & Action

Going back to the beginning of this post - the market rewarded Block’s stock for two reasons.

The first reason: Leverage.

As I wrote in my 10 themes for 2026, AI is the corporate equivalent of a GLP-1 - it helps companies lose weight (cut costs) and reduce cravings (eliminate redundant hiring), while retaining muscle (growing revenue).

Healthier profits = leverage. Fewer people and growing revenue means more free cashflow.

Block guided to $3.66 of EPS vs. consensus at $3.22, a meaningful beat supported by expectations of operating leverage this year.

Meanwhile they grew gross profit 24% Y/Y to $2.87B in 4Q, well ahead of guidance of $2.75B (19% Y/Y).

On top of that, Cash App monthly active users grew from 59M, up from 57M last quarter - breaking 6 quarters in a row of no growth.

And don’t forget, this is all happening against a backdrop of Block trading at a very cheap multiple of 12x 2027 EPS prior to today’s move in the stock.

In sum, Block’s top-line is doing well, but the bottom-line needed to be improved. Now Block is making a commitment to investors that it will be - a $2M gross profit per employee target, 4x their $500k gross profit per employee level from 2019-2024.

Everyone is talking about AI, but the market has one metric to rely on that proves whether or not companies are actually embracing it internally, and that is operating leverage.

But the stock market’s reaction reveals something deeper.

The second reason for the move in Block’s stock: Action. Drastic action.

The Citrini report that came out at the beginning of the week painted a scenario where by 2028, AI’s success wreaks havoc on the economy due to rising white collar unemployment. I tweeted my own pushback against that scenario, as did many others who argued the piece's inconsistencies and self-serving co-author who was short the stocks mentioned. Either way, the debate from it showed there are real investor jitters about companies that will be slowly boiled as AI reshapes their industry - only once they realize what’s happened, they’re cooked.

So if you’re a tech CEO, taking action to ensure that isn’t your company is important.

However, without the AI framing, cutting 40% of your workforce looks like you’re going into a PE-style, cash-generating “maintenance mode” of low-growth but high profits.

With the AI framing, cutting 40% of your workforce looks like vision.

The thing is, whether this is about cutting a bloated cost structure or embracing AI, it doesn’t matter. AI gave Jack a narrative framework to do what needed doing - get his company ready for the AI era.

And it may be that the market is rewarding that decisiveness as much as the numbers, regardless of which story - AI or cutting bloat - is true.

The delta between companies that aggressively embed AI into their operations, and those that are slow to do so, will widen every year. Investors are sending a clear signal that now is the time to get aggressive about putting AI into every element of your business. Or as Jack put it, become “intelligence-native”.

The companies that build the AI muscle now will compound their advantage over their competitors in the next five years.

If you think the boards of software companies with hollowed out valuations, bloated cost structures, and stock-based compensation that exceeds 100% of free cashflow are watching this and asking themselves, “should we do this?”, then trust your instincts.

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A harbinger

The market’s feedback is clear: companies with bloated cost structures should take drastic action towards improving profitability. AI provides the cover to do so, and Block will not be the last company to do a headline-grabbing workforce restructuring.

As my friend and fellow venture capitalist Finn Murphy pointed out in an eloquent post on X, Salesforce has 76,000 employees. One of which is Matthew McConaughey, who earns millions of dollars a year as a creative advisor for the company (read: to hang out with Benioff).

It’s easy to pick on Salesforce, but many large and even mid-sized publicly-traded software companies are overdue for a right-sizing of their workforce.

Consider Atlassian, which has 13,800 employees. Nearly half sit in their engineering organization, which they pour over half their revenue into - one of the highest R&D-to-revenue ratios in enterprise software. Jira and Confluence are not exactly reinventing themselves every quarter. Everyone agrees AI coding tools are making engineers much more productive, so the market is now reasonably asking for proof.

If software CEOs have the fortitude to right-size their business the way Jack did, it should result in them being meaningfully more profitable, healthier businesses. As undrvalue on X put it in his spot-on article about AI enabling software companies to become more profitable in the coming years:

“Any company with 75%+ gross margins, a meaningful SBC burden, and a product that can be maintained and extended with fewer people has the same latent operating leverage coiled inside it. The AI bull case for software isn't about revenue acceleration - it's about what happens to the cost structure when growth no longer requires proportional headcount growth.”

AI’s Uneven Reality

Job cuts, whether driven by AI or not, will increasingly be perceived as AI taking jobs. That creates a real challenge for society across multiple dimensions.

The media, politicians, and those who seek to trade controversy for attention will seize on every layoff announcement as evidence of an AI jobs apocalypse.

The truth is these cuts are concentrated among companies that over-hired, face structural headwinds, and are dealing with tough investor questions about the value of their business in a world reshaped by AI.

Still, it’s a period of digestion for many companies, which means layoffs. That’s uncomfortable, and the human cost is real.

But plenty of companies are still actually hiring aggressively. Especially those benefitting from AI, or building AI applications, AI infrastructure, and companies across the economy looking to refresh their workforce with people who are AI-native.

If you’re looking for a new role, has an awesome weekly newsletter featuring great jobs across the tech ecosystem. And Bill Gurley is out with a new book about “how to thrive in a career you love”.

Like all creative destruction, this transition period will end up being good for the economy as capital is reallocated to higher-value uses.

The people who benefit the most will be those who embrace AI as a force multiplier in their work.

Similarly, the companies that benefit the most will be those who embed AI into their core operations. They won’t just be fit, they will be structurally better businesses than they were previously.

Whether Block is genuinely becoming “intelligence-native” with “intelligence at the core of everything” they do, or whether Jack is using AI as cover to right-size a bloated org - the outcome is the same. A leaner business with greater operating leverage.

Every tech CEO watching just learned that the market may reward them for doing the same.

On December 4, a contract opened on Kalshi that allowed users to bet on whether Giannis would stay with the Bucks or get traded to a different team. The contract opened with the market pricing in ~30% odds that Giannis would stay in Milwaukee.

Over the following month, the Giannis trade saga became one of the biggest stories of this basketball season.

The odds that he’d stay in Milwaukee began ticking up, eventually reaching a high of 91%.

Then January happened. Giannis booed his own fans after a blow-out loss. He got injured (for the second time), and ESPN reporter Shams Charania posted on X that Giannis had told the Bucks he wanted to be traded - a claim that garnered nearly 20M views on X.

The Kalshi odds of Giannis staying in Milwaukee began plummeting, falling from 78% to 45% in a single day, and then hit 38%. Over 200,000 contracts were traded on the platform. By January 30th, Giannis’ trade situation was the highest volume contract on Kalshi.

But then - the trade deadline of Feb 5th comes and goes - no trade. Odds that Giannis stays in Milwaukee skyrocket towards 100%.

Over the 2-month Giannis trade saga, Kalshi generated $23M of trading volume on Giannis contracts.

The morning after the trade deadline, Giannis announced he was “joining Kalshi as a shareholder”, as the first NBA player to take an equity stake in a prediction market. Kalshi later confirmed the deal was signed on February 5. The day of the trade deadline.

According to Kalshi, his stake is worth “less than 1%”. A stake of 1% would be $110M at Kalshi’s last valuation of $11B.

While Kalshi says Giannis is barred from trading on basketball contracts on their platform, basketball fans and the sports world called out the poor optics and perceived conflict of interest.

Did Giannis do anything wrong? Did Kalshi?

Does it matter?

The Insider Pattern

Modern prediction markets like Kalshi and Polymarket have the potential to be the most important financial innovation since Bitcoin. By enabling users to trade freely on markets across sports, politics, business, and really anything else, prediction markets create a price that corresponds to the odds of an event taking place. In doing so, they help us to better understand not just the future, but the present. Improving society’s information flow is an inherent good - for policymaking, capital market efficiency, and for consumers who can monetize their niche expertise.

But prediction markets only work if they reflect the wisdom of crowds. Recently, they’ve been reflecting the wisdom of insiders.

• The Maduro trade (Polymarket, Jan-26): An account opens days before Maduro is ousted by the US military, and places a $32k bet that he will be out of power by Jan 31. Odds on the contract had been around 5% for weeks. The account netted $436k, and only traded on contracts related to Venezuela.

• The Google Search Rankings Trade (Polymarket, Dec-25): An account correctly predicted 22 out of 23 of Google’s “Year in Search” rankings in a single day, making over $1M in 25 hours. The same user made over $150k correctly predicting the release date of Google’s Gemini 3 model a few months before.

• The Super Bowl Halftime Trade (Polymarket, Feb-26): A day-old account placed $70k of bets on performers at the Super Bowl halftime show, including Lady Gaga and Ricky Martin, and got 17 of 18 trades correct. Another trader bet $500k on Lady Gaga making an appearance just days before the show.

• The IDF Trade (Polymarket, 2026): An Israeli soldier and civilian used classified intelligence regarding upcoming IDF operations to trade on Polymarket, netting $150k. The Israeli military has since announced they charged the individuals, making it the first criminal prosecution globally for prediction market insider trading.

The pattern across these trades is obvious. First, a user creates a new wallet, and when they open their first trade, it’s in a niche market with limited public information, and they place a bet much larger than that market’s average. Their timing ends up being perfect, and usually multiple wallets coalesce around a single contract or idea. And the above trades are just a few examples - not to mention the insider trades on the Nobel Peace Prize, the OpenAI GPT 5.2 release, and too many more to list here.

In any other financial market, these trades would lead to investigations and consequences. In prediction markets, they lead to debate.

This problem is worth taking seriously, not because prediction markets are bad for society, but because they have immense potential for good. Improving how society processes information is important. That, however, is being outweighed by the risk that prediction markets become a haven for legalized insider trading.

Free vs Fair Markets

Losing sucks, and losing money sucks even worse. But market participants know what they’re getting into - whether it’s a trader in a prediction market, an investor in the stock market, or a gambler in the casino. Losing money is part of the game, but not when the game is rigged.

That is the unspoken contract by which any market is held together.

In blackjack, everyone knows the house has an edge, but people still play anyway because they understand the odds and have fun while doing it.

Gamblers view prediction markets similarly. But prediction markets go further by allowing people to monetize their niche expertise. Whether it’s sports, movies, politics, pop culture, or business.

If you’re a movie buff, you can decide if you agree with the market price that One Battle After Another wins best picture at the Oscars this year. You know the odds, and know you might lose, but you make the bet because you’ve watched every movie nominated for an Oscar this year and used Claude to build a statistical model based on online sentiment. Your passion, expertise, and skills are your edge. That is very different than the member of the Academy’s voting committee who places a big trade on the Oscar winner. Their edge is their access, influence, and proximity to the outcome.

Market participants are fine losing money to someone who made the smarter trade.

They’re not fine losing to someone who already knows the answer - or even worse, can influence it.

In What is a Prediction Market?, I wrote about the four conditions under which markets operate effectively to facilitate price discovery: Diversity of viewpoints, independent judgements, decentralized information, and an aggregation mechanism that is liquid, transparent, and hard to game. The last two are broken by insider trading, rendering the wisdom of the crowd irrelevant, and creating a transfer of wealth from the uninformed to the informed.

The prediction market platforms launched with a simple proposition: offer users maximal freedom to bet on anything. Maximal freedom creates liquidity, and liquidity is what makes markets work.

That’s true, until freedom extends to people trading on information they got from being close to the outcome itself. At the limit, freedom erodes the fairness that generates liquidity for the market to work in the first place.

To their credit, the prediction market platforms are not hiding from this debate.

The Case for Insider Trading - and Why It’s Wrong

As Polymarket’s CEO Shayne Coplan explained at a recent Axios event, markets where insiders may know the answer are distinguished by several characteristics. As he put it, “They have thinner liquidity, wider spreads, and nobody is confused that at some point in time someone may come in here…and when they do, all of the sudden it’s trading at 95 cents.”

When Coinbase CEO Brian Armstrong was asked about whether insider trading in prediction markets should be allowed, he said, “It’s not a clear cut question, because if your goal is actually for the 99 percent of people trying to get a signal about what's going to happen in the world — like, 'Is the Suez Canal going to be reopened?' — you actually want insider trading. You want some admiral sitting on a ship in the Suez Canal who has really good information to be trading so you get better signal. Now if you want to preserve the integrity of those markets, maybe you don’t want them to be trading. You may need a decentralization test. So it’s not a clear cut answer.”

There is a bit of truth here. If a researcher at the Wuhan lab in China was trading on pandemic contracts in late 2019, that may have produced a price signal visible before the Chinese administration officially acknowledged the COVID pandemic. In that scenario, the researcher is an insider, and their profit motive becomes a mechanism for whistleblowing. That’s good.

However, this argument falls apart once you extend it to people who don’t just know the outcome, but can actually influence it.

Consider Brian Armstrong’s example of an admiral in the Suez Canal. Placing a trade in a prediction market could create a distorting incentive to push for a policy because they have money riding on the outcome. Rather than asking, “when should the Suez Canal reopen?”, they ask “when did I bet it would reopen?”. The financial incentive has a distorting effect on what should be otherwise an objective policy decision.

In the US, we have already seen what happens when people trusted to make decisions are allowed to trade on the outcomes of those decisions.

Less than 20% of Americans say they trust Congress to do the right thing. That disastrous erosion of institutional trust has been supported by, among many other things, the fact that our representatives are allowed to trade stocks using information they receive as part of their job in Congress.

Prediction markets that lack common sense rules around insider trading risk more than being unfair. They risk scaling the same corrosive distrust that has poisoned society’s view of institutions like Congress. That pervasive sense that the game is rigged has contributed to the gambling reflex and casino economy that people like have written about. The irony is that same desire to gamble has helped fuel the consumer demand powering the rise of prediction markets in the first place.

If that distrust scales across every domain prediction markets touch - entertainment, sports, policymaking, etc. - the blowback will be significant. The platforms risk poisoning the very well they're drinking from.

The thing is the prediction market platforms know this. So what are they doing about it?

The Window for Self-Regulation

In the Axios interview with Polymarket CEO Shayne Coplan, he implied insider trading in prediction markets is a feature, not a bug, as it “creates a financial incentive to divulge information to the market.” Distinguishing insider trading from outright manipulation of outcomes, he said, “We’ve got some stuff in the works that will be a great improvement around what’s existing. Integrity around the markets is really important.” Okay, not the most reassuring stance.

Kalshi has been more concrete. Two weeks ago, they announced a new independent advisory committee focused on trading surveillance. CEO Tarek Mansour laid out a clear plan on X to work with the CFTC on enforcement of insider trading, including hiring a head of enforcement.

Those are real, important steps. They also were announced the same day - February 5th - that Kalshi signed a shareholder deal with an NBA player whose trade status drove $23M of volume on their platform. That tells you how far along we are in this process.

The prediction market platforms should be moving faster. Under the current administration, the CFTC has been aggressive in claiming sole regulatory oversight over prediction markets. And as sole regulator, the CFTC’s position towards the prediction markets is to treat them as “self-regulatory organizations”. Translation: Police yourself.

Regardless of your political leanings, that position of the current administration is the most favorable and permissive regulatory environment that Polymarket and Kalshi could ever imagine. So permissive that even the President’s son is an advisor to them - yes, both of them.

This is a unique moment in time for the prediction market platforms to build real self-regulating infrastructure and institutional-grade trust. It won’t last. There is a growing swell of politicians taking aim at prediction markets. If the next administration takes a less favorable view of prediction markets, the response won’t be surgical.

What Happens Next

A new consumer category claiming to offer a public good, growing virally, operating in a regulatory gray zone, and at the center of a major debate about their impact on society.

Sound familiar? Juul. They were in a similar situation a decade ago. Juul created an entirely new consumer category with vaping. It was an innovative product, less harmful than the cigarettes that millions of Americans were addicted to. At one point they were valued at $38B. But Juul did not self-regulate. Instead, they let teenagers become their fastest-growing user base, paid lip-service to age verification, and treated regulators’ concerns as a PR issue. The societal and regulatory backlash came - under the first Trump administration, no less - and it was swift, blunt, and total. The FDA sued Juul into oblivion and investors lost billions.

Gambling, like nicotine use, is real and is not going away. But blatant insider trading, like teenagers vaping, is societally untenable and too tempting for politicians and regulators looking to pick a fight against a visible, emotionally resonant problem.

And if the current prediction market platforms don’t self-regulate, someone else will - whether a regulator or a competitor. A new entrant that counter-positions itself as a “clean exchange” - staunch on insider trading, transparency, and enforcement - would have a major opportunity to create a differentiated prediction market platform.

Kalshi’s recent moves suggest they want to be that platform. Whether those recent announcements lead to real enforcement or remain as press releases remains to be seen.

Prediction markets have a massive opportunity. Global consumer financial markets, institutional hedging, and data services represent a TAM large enough to sustain a $100B+ market cap company. None of that will happen if these markets become a haven for legalized insider trading.

The wisdom of the crowd is a beautiful idea - that humans’ collective opinion, given the right conditions, is smarter than any one expert. The prediction market platforms harness it incredibly well. But the crowds have to believe the game is fair, or they’ll leave. If insiders crowd them out, it will destroy the wisdom these platforms were built to foster. They have a window to prevent that from happening. It’s smaller than they think.

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On Wednesday, CNBC host Deirdre Bosa tweeted about how she used Claude Cowork to build her own kanban board interface to manage her to-dos. She tweeted that she wanted to “try to recreate Monday.com”. The tweet went viral, and Monday’s stock traded down 6% on this before recovering and then trading down another 10% Thursday and up 2% Friday.

A journalist built an interface that looks like a feature in Monday, and erased $300M from its market cap in 30 minutes - this is for a company generating $1.3B in annual revenue across 250,000 customers.

But to the market this week, it’s as if Monday was just a kanban board.

That’s where we are today. The “death of software” narrative has grown so loud that the market is looking for any excuse to sell off software. And sell off it did, erasing a trillion dollars of market value.

But the narrative is wrong. Not because software companies don’t face disruption risk from AI (they do), and not because AI isn’t transformative (it is).

The “software is dead” narrative is wrong because it frames the future as binary: Value accrues to either software applications or agents.

After the AI agent architecture advancements of the last two months, it seems pretty clear that Claude & ChatGPT are well positioned to become the default “home screen” for knowledge workers. The main bear case for software in that future is that they become “dumb pipes” that merely provide data to the AI agents to absorb, put into context, and execute decisions on top of.

I believe there is a different architectural path though, and the best software companies are going to have to fight to get there. If you’re a software company clinging to the old paradigm, an expanding TAM will not save you.

Will AI kill software? I don’t think that’s the right question. I think the right question is whether software companies can adapt their products to capture value in the new computing paradigm - or let another company capture that opportunity.

A brief history of the Great SaaS Crash of 2026

It started in November.

Anthropic released Opus 4.5 on November 24, 2025. It was their most advanced model to-date, and the tech world lost their minds over the December holiday break.

Opus 4.5 enabled a breakthrough in Claude Code (Anthropic’s coding agent) - in the quality, reliability, and usability of the code it generated. It was an inflection point in AI’s ability to ship production-ready code, and you can see it in the data:

The narrative quickly grew beyond just developers, to the idea that everyone should use Claude Code. wrote a widely-read blog post highlighting potential use cases like finding sales leads, processing meeting notes, and improving writing/brainstorming.

Anthropic brought that idea to life on January 12, releasing Claude Cowork, which gave the power of Claude Code to non-engineers. Then the agent narrative accelerated with OpenClaw’s release a week later (fka Moltbot, fka Clawdbot). Virality ensued and stories exploded across the mainstream press asking questions like, should we be scared?

A big reason why Claude Code, Claude Cowork, and OpenClaw caused such a flurry is they run on your desktop. Meaning they see your apps, your notes, your files, your calendar. That means they have context. And in AI, context is everything.

And on January 30, Anthropic announced they’d provide Claude with a lot more context - with Plugins for Claude Cowork. Plugins are essentially integrations to software systems, plus a set of instructions on how to use those systems. Now the context for Claude Cowork isn’t just what’s on the user’s desktop - it’s data that lives in HubSpot, Workday, Salesforce, Slack, and more (full list here). No need to go into those apps anymore, right?

So to summarize, in this short two-month timespan, AI agents gained three things: Production-ready reliability, viral distribution, and connectivity to the apps that users care about most.

Yesterday poured fuel on the fire, with Anthropic dropping Opus 4.6 and OpenAI launching OpenAI Frontier, a new platform focused on enabling enterprises to adopt agents.

As part of their release of Frontier, OpenAI published this, codifying the SaaS is dead narrative in a pretty diagram:

For software investors, the implications of all this were immediate: If Claude/ChatGPT agents can access my calendar, meeting notes, and Slack, and share updates with my team about next steps after a customer meeting, do I really need to be paying so much to Salesforce? All of the intelligence and business logic will move to my AI agent, so can’t I just use any database to store my customer data, instead of paying so much for a CRM?

In that framing, the system of record just sits at the bottom of application stack as a database that feeds data like a “dumb pipe” to agents that gather context, understand business logic, and take actions on my behalf - then that seems like a pretty unattractive place to be for software! It’s time to SELL, SELL, SELL!

The bear case

Throughout all of last year, there was already a raging debate between bulls and the bears on software. The bear argument has basically been:

1. Vibe coding erodes moats. When anyone can build software, corporations will just build their own internal tools and competition will compress pricing power.

2. Fake free cashflow. Stripping out stock-based compensation actually shows many software companies don’t nearly generate as much cash as they seem to be.

3. SaaS has reached the top of the S-Curve. Corporations have bought so much SaaS in the last decade that IT budgets are essentially fully penetrated, and software industry growth will converge with GDP growth.

4. Talent costs are spiking. The best engineers want to work for hot AI companies, not traditional software companies. Software companies have to pay up and hurt their profitability, or lose talent to AI-native companies.

5. Negative reflexivity and capital outflows. Over the last two years capital has flowed out of software and into AI infrastructure, semis, energy, and the mag7. Multiple compression fuels the bear narrative, driving more selling, and creating a vicious cycle with no catalysts to reverse this trend.

These are all interesting arguments, but none of them justify the type of multiple compression we’ve seen.

Vibe coding a kanban board is easy. Building and maintaining the permissions, integrations, and compliance - and the multi-product suite that Monday offers - is a totally different problem, and vibe coding doesn’t solve it. Simply put, corporations do not want to build and maintain their own internal tools.

SBC is high, but it’s not uniform across every company. Software continues to grow well-above GDP and even general IT growth rates, and many software companies are actually seeing growth accelerating. Plenty of smart engineers still want to work at software companies. Finally, negative reflexivity holds until there is an inevitable bottom, even if the recovery takes longer than the bulls would like.

All of that said, there is one bear case that I alluded to at the start of this blog that is very real, and I believe is the principal cause of the current sell-off in software stocks: Value migrating from software applications to AI agents.

If AI agents are the interface between humans and software applications, and the agents contain all context, governance, and business logic, with the ability to read data, execute workflows, and make decisions - then the application underneath will be rendered a commodity database, merely storing data until the agent needs to access it.

That is the real risk that software companies are facing.

Software’s Two Futures

When mobile computing arrived in the late 2000s, humanity’s total computational activity expanded massively, but the companies whose value was tied to the desktop interface suffered. People’s “home screen” moved from their Windows desktop to their iPhone.

Microsoft’s stock treaded water for a decade. It didn’t matter if their theoretical TAM for personal computing devices expanded, because Apple captured that incremental opportunity. Microsoft was lucky that they were able to launch Azure and capture the cloud opportunity even though they missed mobile. Many companies didn’t survive these paradigm shifts.

When Google became the primary interface for the Internet, websites didn’t go away - there are more websites today than in 2000. But it didn’t matter how beautiful your website was, how valuable your users were, or how strong your brand was. Whether you were Nike, Walmart, or Hermes - you were subservient to Google, which captured distribution, discovery, and monetization. Google was the interface layer by which users arrived to your website.

Is the same pattern playing out right now between agents and software companies?

Are AI agents becoming the new interface layer or “home screen” for where work gets done? In the future, will we just access all of our apps via an AI agent that can interact directly with the app’s underlying data, rendering software providers as “dumb pipes” that store data and feed it to agents?

Will they be the version of Microsoft that dismisses the iPhone and misses mobile? Or will they be like the Microsoft that launched Azure despite cannibalizing Windows to aggressively pursue the new opportunity in cloud?

There are two futures for software companies, and they will all face one or the other.

The dumb pipe argument that shifts software companies from the foreground to the background:

• A general-purpose AI agent like Claude or ChatGPT accesses my system of record (i.e. CRM) via a plugin or API call, pulls out deal data, drafts a pipeline summary, and sends it to a sales VP.

• The sales VP reviews the summary inside of Claude’s interface. The CRM was involved, but only as a source of data. It could be swapped out for any other CRM with an API, or even a database for that matter - and the experience for the end-user would be the same.

• The CRM’s differentiation and user-relationship go away, along with their pricing power and stickiness.

This future would be very bad for software companies, and it’s what the current market that is valuing software companies between 3-4x sales is pricing in.

Here is the AI-enabled application future that positions agents inside of applications themselves:

• A general-purpose agent accessing CRM via a plugin has shallow context. If the CRM company built a domain-specific agent, trained it on proprietary data from millions of customer interactions, and embedded it inside their application - it would be able to go beyond what a general-purpose agent accessing data via API.

• The CRM’s AI agent could tell that sales VP, “this deal is at risk because the customer engagement pattern matches 73% of deals that stalled at this stage across our entire pipeline history.”

• The sales VP could then use their general purpose agent to connect with the CRM AI agent, and pull in data from the customer success system and ERP system, to write a customer action plan and contract proposal.

The data in all of these systems is a commodity.

But enterprises don’t just store data inside of ServiceNow - they store governed data. And along with it permission models, audit trails, compliance rules, security policies, intelligence and workflow logic that is non-trivial to configure - that is where the value lies.

Because for a Fortune 500 company that spends 18 months to configure Workday’s permissioning model to match their compliance requirements, controlling who has access to what data and what are they allowed to do with it - is of paramount importance. And a general-purpose AI agent will not be able to inherit that overnight.

If software companies allow all of that value to move to the general purpose agent, they will relegate themselves to merely being providers of that data, and they will become commodities themselves.

The question is whether they let that happen.

The AI-native roadmap

The best AI-native software companies already understand that the future belongs to applications that make agents a first-class part of their interface.

AI-native software companies like Decagon, Sierra, & Kustomer in customer support. DayAI in CRM. Rillet and DualEntry in ERP. And a number of my own investments including Quilr, Qualitate, Rilla, Membrane, Tribble, Quarter20, Dapta, & Coheso.

They are architecting their applications not as passive systems that work well when a general-purpose agent tells them what to do, but rather around the idea that the application itself should be intelligent. That doesn’t mean there’s a chatbot bolted into the side bar, but rather a redesign of how the product works with the assumption that the user wants to delegate tasks to the system.

I think one of the most visceral examples for knowledge workers is Gamma. You describe what you want, their builds it, and you can point to specific components in the deck to have the agent re-work for you. Compare that to Google Slides or Powerpoint (even with their AI copilots), or even asking ChatGPT to make a deck for you.

As I wrote in Raising the Bar, AI-native software products have several ways in which they differ from traditional SaaS applications.

SaaS companies should be as worried about AI-native application companies as they are the AI labs, if not more so.

Even in a future dominated by agents, every company will still need a CRM. But if the marginal company buying their first CRM opts for Day AI over Salesforce, that is going to be a real problem for Salesforce.

Their growth will stall and future will dim, and it won’t be because CRM as a category went away or became commoditized, but because the definition of what a CRM should be has changed.

The Haves and the Have-Nots

If you’ve made it this far, thank you - and now for some stock picks. Just kidding. But hopefully this part helps you navigate the current sell-off in software stocks.

As I wrote about in my Predictions & Themes for 2026, software is going to divide between the “Haves” and the “Have-nots” - companies who are well positioned for AI, and those who aren’t.

Even though the market is treating all software the same this week (as if they’re all going to die), the fact is these companies are not all the same.

There will be software companies who make it through the AI transition, and they will be bigger on the other side. The “Haves” are the ones with the ingredients to build their own AI-enabled future. The “Have-nots” will face a tougher battle.

The Haves. These companies have high gross retention, broad platforms spanning many budget lines, and products that sell to roles that AI empowers rather than roles AI replaces. They have the data, the distribution, and the culture of shipping product to fine-tune their own agents.

The Have-nots. These companies sell narrow point solutions, have relatively higher churn, sell to user bases whose jobs are in the crosshairs of automation, and lack the leadership and culture to ship or buy their way to a meaningful AI product. They lack deep proprietary data.

Certain categories are naturally better positioned: ERP, CRM, customer service, HRIS, CAD, EDA, and other broad systems. General purpose agents need their data, and they have an opportunity to build an agent-powered intelligence layer into their interface. They can own both sides of the stack and be well positioned on the other side of the AI transition.

On the other hand, software companies whose value is in making it easy for humans to interact with their data - an interface layer - these businesses are in trouble. When the agent becomes the interface, there will be little ground for them to stand on.

What comes next

The market is overreacting, but it’s directionally right.

The AI era is massively expanding the surface area of computing. Humans no longer need to be present for compute-intensive work. AI agents will take actions on humanity’s behalf, enable new use cases for technology, and unlock productivity gains we’re only just beginning to see. The total market for software will be far larger in the future than it is today.

But that doesn’t protect software companies from disruption at the hands of AI. Value will shift as agents become a foundational part of the way humans interface with applications.

Software companies that settle for being a plugin will watch other companies’ agents capture their user relationship, pricing power, and market value. Software companies that build agentic capabilities into the application itself, creating an intelligent interface, will come out larger on the other side of this transition.

I have no idea if this will be the bottom for software stocks. But it feels like we are somewhere between “capitulation” and “despair”.

The “dumb pipe” argument is a real bear case for software companies.

To refute it, software companies need to:

• Embed agents directly into their interface, and ensure their software provides the agentic workflow that their users want.

• Tell a clear story to investors of why their software remains as valuable or more valuable in a world where AI agents take digital actions on human users’ behalf.

• Back up that story with AI monetization, with clear metrics and consistent disclosure.

• Leverage M&A where possible to accelerate their AI-native product portfolio.

• Increase cash generation, and right-size SBC and per-share earnings growth.

Stock markets regularly overshoot. Ben Graham famously called the market a manic-depressive, swinging its estimates of a business’s value from wild optimism to deep pessimism. But markets are also the best forecasting mechanism that humanity has - aggregating our collective intelligence into signals about the future. The magnitude may be wrong, but the direction rarely is.

And right now, the market is sending a very clear signal. Investors, founders, and their companies alike should pay attention - there is a clear path of disruption coming to traditional software companies at the behest of AI. It’s also an opportunity. Now is the time to take it.

If you’re at all interested in software, investing, public markets or private markets, you’ll enjoy the conversation. We dive into what’s happening to SaaS stocks, how AI is reshaping the economy, and what the IPO market will look like in 2026.

This is the first of many conversations I plan to post here on Infinite Runway.

If you’d like to follow along with the slides mentioned in the pod, you can access the deck here.

2025 saw a deepening of AI usage as the technology’s users shifted from early adopters to mass market appeal. ChatGPT crossed 800 million weekly active users and will likely cross one billion soon. Google took the plunge and integrated AI answers into its search engine. Across the economy, workers in every field from doctors and lawyers, to HVAC technicians and logistics operators, and even your friendly neighborhood venture capitalist - are seeing their jobs augmented and even transformed by AI. And it’s just getting started.

What follows are ten themes and predictions I expect will define AI, software, and technology in 2026.

1. Chatbots Go Vertical

The first killer use case for AI is the chatbot. Yes, there are tons of software companies building applications on top of AI - coding assistants for developers, note-takers for sales reps, AI agents for customer support - many awesome companies building killer products, a few of which I’m lucky to call myself an investor in.

But by and large, the world-changing products have been ChatGPT and Claude. For most people, if you asked them how they use AI in their daily life, it’s those two products. They are general-purpose tools that do everything from writing code to planning a vacation.

A paper from OpenAI published the image below in September that breaks down how people use ChatGPT.

Of course, the AI labs have this data broken down far more deeply than the above chart. They know exactly what drives the most engagement and retention, and can hypothesize what will create the most willingness to pay.

In 2026, expect them to act on it.

The AI labs will start shipping specialized product interfaces for the highest value use cases inside of the apps we already use - ChatGPT, Claude, Gemini, Grok, etc. - rather than be standalone apps the way OpenAI released Sora.

The AI labs will do this not because the models can’t answer these use-case-focused questions today, but because their value will go beyond a generic chat interface. Persistent context, data connectivity, compliance & controls, and structured workflows will combine to deepen the value the chatbots provide their users.

Coding came first, with Anthropic’s Claude Code and OpenAI’s Codex, but these were just the beginning.

OpenAI’s announcement of ChatGPT Health last week is a perfect example of this. ChatGPT Health creates a dedicated space within ChatGPT that allows users to connect medical records and data from wearable devices, and create a dedicated thread of conversations with compliant data controls. ChatGPT Health will remember specific health-related things about the user, so that it builds up context the way a doctor would.

I expect we are going to see similar moves by Anthropic within health. I predict both OpenAI and Anthropic will expand their chatbots’ offerings to have specialized features in finance, education, travel, and shopping.

These are already some of the top use cases in AI assistants, but more importantly they are some of the largest areas of consumer spending in the US economy today - amounting to tens of trillions of dollars annually. Today, the AI labs monetize through subscriptions and API calls. But if AI assistants become how we manage our health, plan our finances, book travel, and shop? The TAM goes far beyond productivity software and search advertising.

Not all of these use cases will be flawless by year-end. Take shopping for example, The Information recently published an article on why agentic e-commerce has taken longer to go mainstream than some would expect.

But by year-end it will be clear that AI’s first killer use case - the chat interface with a blinking cursor - was just the beginning of how the world’s billions of internet users will come to see AI.

2. AI Drives Corporate Margin Expansion Across Economy

AI is the corporate equivalent of a GLP-1, as it helps companies lose weight (cut costs) and reduce cravings (eliminate redundant hiring), while retaining muscle (growing revenue).

The net result? Margin expansion.

Take C.H. Robinson. Founded in 1905, C.H. Robinson is a boring, old logistics business that acts as a middleman in shipping, helping get goods from point A to point B across air, water, and land. And in 2025, they have seen revenue decline by 9% Y/Y due to a combination of a weak freight environment and divestitures. Yet their operating profits are up 26%, and the company saw operating margins expand to 30%, up from 24% a year ago. C.H. Robinson attributes this in part to the deployment of over 30 proprietary agentic AI tools, which improved quote response rates from 65% to 100%, cut response times from 17 minutes to 32 seconds, and allowed the company to handle more volume without adding headcount.

So how did the market reward C.H. Robinson’s stock in 2025? Not with a golf clap, but rather with thunderous applause. CHRW ripped up 59%, outperforming the darling of the AI era - Nvidia’s return of 39%.

C.H. Robinson’s ability to use AI as an operational lever has transformed its business and allowed it to prevail through a tough macro cycle that would typically see profits decline.

And it’s a preview of what’s coming across the economy.

In the above chart, the X-Axis shows AI penetration - the share of businesses in a given industry expected to use AI in the next 6 months. The Y-Axis shows AI usage intensity - the share of GenAI Queries per 1M workers. Tech-forward industries that operate using lots of data - tech, media, capital markets, telecom, banking, commercial services - are the furthest along in adopting AI in their business. The laggards include transportation, utilities, oil companies, and retailers.

My prediction: Some of the best performing AI stocks in 2026 are going to be non-tech companies. Banking, insurance, capital equipment, logistics, and other old-world industries will see companies leverage AI to compress the cost and time required to produce work.

It’s not just tech anymore. The AI margin opportunity is here and will be coming to every industry across the economy.

3. AI’s Hiring Impact Becomes Widely Felt

A corollary to my last prediction is that companies will need to hire fewer workers.

Even if AI doesn’t result in the mass layoffs that many AI doomers are predicting, the impact of companies needing to hire fewer people will be felt strongly across the economy in 2026.

It will show up in the news we consume, the job market statistics, the way in which young people plan their post-college job search, and how people vote at the polls in the midterms.

The impact of reduced hiring will be felt strongest in coordination-heavy roles that exist to move information and manage project hand-offs. Back-office operations like supply chain, risk, customer support, and HR ops are all functions that stand out as being relatively more affected due to AI according to a recent McKinsey study. And 2026 is expected to see meaningfully more impact than 2025.

This doesn’t necessarily mean layoffs, but it will mean fewer new hires. Companies just don’t need as many people to grow as they used to.

AI is exposing how much of the modern corporation was built to manage inefficiencies and friction, rather than grow and create value. Companies will be leaner going forward, but the economy will be more fit as a result.

And just as existing companies need fewer people to grow, new companies need fewer resources to start, leading to an increase in entrepreneurship and new company formation.

4. Consumer AI Products Launch Ads

It’s been a long time coming, but in 2026 we will see the original business model of the internet - advertising - become an important part of AI business models.

It’s clear that OpenAI is very likely to put ads in ChatGPT. They’ve already started testing this, resulting in some negative user feedback. In forecasts to investors, the first “free user monetization” revenue begins kicking in this year.

Google is already monetizing its AI summaries, and while it hasn’t put ads in Gemini, its ChatGPT competitor, just yet - I wouldn’t be surprised to see them this year.

It’s not just the big players - startups like OpenEvidence are having success monetizing their users through ads. OpenEvidence offers a ChatGPT-style AI interface for doctors that allows them to do research and ask medical questions. Nearly half of all physicians in the US use OpenEvidence, which is generating over $150M in annualized revenue via ads, and was recently valued at $12B.

The first banner ad was displayed in 1994, and while ads fortunately are less annoying than they used to be, the hyper-targeted nature of AI inquiries means they stand to be more valuable than ever.

The challenge of 2026 for the AI labs: monetize their free tier without degrading the product. Get it wrong, and their users will leave to an alternative AI chatbot. Get it right, and they’ve unlocked a massive new consumer AI business model.

5. ChatGPT Continues to Reshape Software Expectations

In Raising the Bar, I talked about how ChatGPT is resetting user expectations of software products. The same way Robinhood’s commission-free, mobile-friendly trading changed user expectations around the ease of buying and selling stocks, and the same way Amazon Prime changed the speed at which users expect their deliveries - ChatGPT is changing what users expect of their software.

Users now expect instant value without signup friction. They want software that anticipates their needs and acts on their behalf, whether it’s updating CRMs, booking travel, or sending follow-up emails. Users want to talk to their software, feed it screenshots and PDFs, and get structured outputs. And they want transparency: editable system prompts, explainable decisions, clear guardrails.

These features are becoming table stakes in every piece of software, and it is happening fast.

Many incumbent software providers will prove to be surprisingly slow in shipping products that create truly game-changing user experiences. For startups, the opportunity is clear to become the next generation of companies that raise the bar for the rest of their industry.

6. Software Stocks Further Divide Into Haves and Have-Nots

2025 was a tough year for publicly traded software stocks. The IGV, an index that tracks the software sector, was up 1% in 2025, while the S&P500 was up 17% and the NASDAQ up 21%.

Meanwhile, many application software companies saw their stocks get crushed, like:

• Hubspot, -42%

• Monday.com, -37%

• Atlassian, -35%

• ServiceNow, -28%

• Adobe, -21%

• Salesforce, -20%

• Workday, -17%

And yet, many infrastructure and cybersecurity software stocks ripped:

• MongoDB, +80%

• Snowflake, +42%

• Crowdstrike, +37%

• Twilio, +31%

• Zscaler, +25%

So what gives?

2025 was a year of bifurcation within software. A small group of stocks, primarily in infrastructure & cybersecurity software, drove most of the gains in the sector because they were viewed as clear AI beneficiaries.

As the two charts above show, among publicly-traded software businesses, infra/cyber stocks have out-grown their application-layer counterparts. Even more importantly, the forward-year growth estimates for consumption software (i.e. infra) have been revised up vs. application software which have been revised down (although only slightly).

Put simply, software stocks that didn’t translate AI investment into financial metrics suffered - even if they had solid results. Investors held an apathetic view towards the software sector, diverting attention and dollars to AI “picks and shovels” ideas like semiconductors, networking gear, data centers, and energy.

In 2026, the bifurcation of the AI "haves” and “have-nots” will deepen within software.

However, I expect many of the large cap application platforms like Salesforce and ServiceNow that traded down in 2025 to see outperformance in their stock prices, driven by AI monetization.

Those best positioned have high gross customer retention, broad platforms with expansive budget presence, and a culture of shipping (or acquiring) new products. These companies stand to see new sources of revenue from AI-related features in 2026, as well as AI-driven margin expansion and cost efficiencies.

On the other hand, software companies that offer narrow point solutions, who sell to a user base whose jobs will likely get replaced or reduced by AI (i.e. customer support), and who lack the management team / execution capability to navigate through this type of product transition are poised to see another year of underperformance.

7. Prediction Markets Double Trading Volume

Earlier this year I wrote a blog post explaining what prediction markets are, and why they stand to be much more than just digital casinos.

Prediction markets had a banner year in 2025. Both Kalshi and Polymarket are valued at over $10B. Robinhood has rolled out their own prediction market, as has DraftKings and FanDuel, with Coinbase’s expected to launch one soon too.

2026 is going to see prediction markets trading volume jump even further, and I expect Kalshi and Polymarket will raise at $20B+ valuations.

Two catalysts that I expect will drive this growth in 2026: The World Cup in North America this summer and the midterm elections in November.

Estimates range, but Polymarket and Kalshi are likely processing around $3B each per month today. By the end of 2026, I expect this number to at least double to $6B+ each.

8. Voice AI Reaches Mass Consumer Penetration

A few weeks ago over the holiday, I called my hotel's front desk for a room request. A voice answered, it was human-like, natural. It took me a few seconds to realize I was talking to an AI. It handled my request - sending up extra pillows to my room - without me ever speaking to a human.

By the end of 2026, most consumers will have a voice AI experience in which an AI agent handles a situation for them end-to-end, without the need for a human agent to intervene. Many consumers won’t even realize they were speaking with an AI.

Industries where humans are used to waiting on hold - airlines, hotels, events, home services - these will see the largest benefit of using voice AI. Legacy if-this/then-that voice bots with rigid menus and long hold times are costing enterprises billions in operational waste and consumer trust. Modern voice AI agents detect tone, handle multi-intent queries, and resolve complex issues in real time. They will allow companies to recoup lost revenue, reduce human-supported call volume, and improve customer support experiences.

9. 2026 IPOs will disappoint

While many expect 2026 to be a hot market for IPOs, I expect many of the most in-demand privately held companies will hold off on going public. This runs counter to the view held by public market investors, who in the survey below from Wolfe Research, overwhelmingly stated they expect the IPO market to return in 2026.

The below chart is from Setter Capital, a secondaries platform that helps connect buyers and sellers of privately held stock in hot companies like SpaceX and OpenAI. They publish a quarterly list of the top 30 most in-demand startups among private investors.

From this list, I expect 3-5 companies will go public in 2026, with the most likely in my opinion being Canva, Databricks, Kraken, Revolut, and - for a dark horse pick - Notion.

The AI labs have too much business model uncertainty and capex intensity to go public, and ditto for the hardware companies on the above list. AI applications like Lovable, Replit, and Glean are still in hyper-growth mode - early in business-building and reinvesting cash internally. Same goes for the prediction markets.

But here is the bigger point: private investors have built a shadow public market for these companies.

These companies’ shares trade with regularity across secondary markets and via tender offers whereby the company organizes a sale of employee and investor stock to other investors (or buys the stock back itself). Removing the need to provide liquidity to employees and shareholders reduces the incentive to go public. And for the top companies, the demand for their shares outstrips the supply available, creating a premium for their shares and liquidity practically as often as they could want it.

Until there is an overhaul of the regulations that govern public companies, it’s unlikely for this situation to change. Or put another way, expect the market for IPOs to disappoint in 2026.

10. M&A Stays Hot in Software & AI

2025 was a big year for software M&A. Just in December, major deals like ServiceNow’s acquisition of Armis and IBM’s acquisition of Confluent were announced.

Major software companies across categories from cybersecurity (CyberArk, SolarWinds), payroll (Dayforce, Paycor), and infrastructure software (Confluent, Couchbase, Informatica) were taken out of the public markets by strategic and private equity acquirers.

In the chart below, I highlight the most significant acquisitions of public software companies. The median EV-to-revenue take-out multiple was 5.5x, with the median forward-year growth rate being 11% Y/Y.

Looking ahead to 2026, which public companies are most likely to be acquired?

• The factors I look for are 1) companies that are being persistently undervalued by the market relative to peers, 2) a product suite that is potentially strategic or has increased monetization potential inside of another platform, and 3) single vs dual share class structures that can make an acquisition less likely if the controlling shareholder (founder) doesn’t want it.

• For 2026, GitLab, SentinelOne, Okta, ZoomInfo, and Monday.com are my top 5 picks for public software companies most likely to get acquired. Each one has a large customer base, strong brand, and product that can be upsold or made more valuable by being bundled into a larger platform’s suite. They all trade below 5x forward revenue. In the cases of GitLab and Monday.com, I believe the market is undervaluing the tailwinds that AI could end up being for these businesses. That said, other than ZoomInfo, they all have dual-class share structures, which means that if the founder who has the controlling shares doesn’t want to sell, then there won’t be a sale.

What about acquisitions of private companies?

• 2025 saw the largest acquisition of a venture-backed software startup ever with Wiz getting taken out by Google for $32B. ServiceNow acquired both Armis for $7.75B and MoveWorks for $2.85B. There were plenty of other smaller but still notable deals like Salesforce’s acquisition of Qualified, Palo Alto acquiring Chronosphere and Protect AI, and many others.

• For 2026, acquisitions of private companies will continue, but they will not be of the hottest AI-native companies. Most acquisitions will center on companies that reached $10M+ ARR prior to the AI wave, are growing 30%+, have demonstrated AI tailwinds, and provide a product expansion story to their acquirer.

• As for the hottest, highest flying AI-native companies, there will be only a handful of M&A events in 2026. Manus, the AI agent company that sold to Meta for $2B last month, is the rare example here. The AI super-cycle is running red hot and private capital is cheap for companies that are growing. For companies whose growth stalls, many have raised enough capital to grind it out before selling. Check back here in 2027.

Lastly, don’t forget about the IP Licensing Deals, which allow the acquirer to license the IP and hire key leaders of companies without actually acquiring them. The 7 deals that have taken place with this structure have been the dominant form of M&A by big tech companies, including:

• Groq acquired by Nvidia ($20B)

• Scale AI acquired by Meta ($14.3B for 49%)

• Character AI acquired by Google ($2.7B)

• Windsurf acquired by Google ($2.4B)

• Inflection AI acquired by Microsoft ($650M)

• Adept acquired by Amazon (~$400M)

• Covariant acquired by Amazon (~$380M)

And IP licensing deals will continue to dominate big tech M&A going forward, as they provide cover from the watchful eye of anti-trust regulators. I expect we’ll see more of these, especially in AI infrastructure - where regulatory scrutiny is highest today.

A Final Note on the Year Ahead

Below is one of my favorite charts of all time. It shows estimated global GDP over the past 2,000 years. From Rome to the Renaissance, it’s basically flat, driven by centuries of zero-sum survival and subsistence living. The industrial revolution hits, GDP goes vertical, and it hasn’t stopped since.

We live in an age of social media, deep political partisanship, a shifting global order, culture wars, and the 24-hour news cycle. In short: chaos. It’s easy to forget that we’re also living on an exponential curve of technological progress and economic growth.

Economic output is the closest thing we have to a long-term scorecard for civilization’s capacity to improve living standards. And the score keeps going up. The turbulence we feel isn’t happening in spite of the curve, but rather it’s a consequence of it.

So if the world feels like it’s getting weirder, faster - that’s because it is. But it’s also getting better.

Personalized healthcare and bespoke education. Robots that assist with elder care, household tasks, and menial work - freeing up time for humans to pursue their work and passions. AI that catches disease before symptoms appear. Real-time translation that erases language barriers. Autonomous cars that reduce traffic fatalities. Cheap, abundant energy.

These are shifting from science fiction to reality, and while they may not be on my list of predictions for 2026, they are for 2046.

The internet has trained us to focus on the news of the day, but history rewards those who bet on compounding.

So don’t get lost in the noise, and remember the line is still going up. Happy new year!

As a venture capitalist, I work closely with my portfolio companies on their funding announcements, stealth exits, and major launches. After doing this dozens of times, and help shape messaging, coordinate launches, and plan day-of execution - I realized there’s a repeatable playbook. But most of what’s available online is fragmented, anecdotal, or too high-level, so I wanted to write something digestible, comprehensive, and actionable. This is that playbook.

This document is meant to be a guide, a step-by-step operating manual for startups planning to come out of stealth or announce a major funding round.

Deciding when to announce the fundraise

Don’t take the decision lightly. It is a free media event that you can use to get attention from the public. Coming out of stealth is a particularly important type of fundraising announcement, as it creates a first impression with the market.

There are three reasons for startups to announce a funding round. To attract and gain credibility among:

1) Future investors

2) Potential hires

3) Prospective customers

A fourth reason becoming more common is to dissuade would-be competitors. Especially if it’s a monster round for a company in a new category, where the company has a chance at category leadership. Investors are moving earlier and more aggressively for these types of rounds among AI-native software companies in a strategy known as king-making, but that’s another post.

The point is - the way to make the most of a fundraising announcement is by being ready to absorb any inbound that you might get from those three groups - investors, hires, and customers.

In other words, startups should ask: Are we ready to raise a new financing round? Are we actively making new hires? Is our product mature enough to absorb new customers today?

If the answer is no to any of those questions, it is worth considering delaying the announcement.

If the answer is yes to all three - congrats, you’re ready to announce.

Preparing to make the announcement

Startups should aim to create multiple media artifacts that can be distributed throughout the announcement process, including:

1. Press release

2. News article

3. Social media posts

4. Website blog posts

5. Launch video

6. Customer testimonials

As a first step, founders should think about the story they want to convey to the public. Three key elements to any startup’s story are: 1) the founder’s journey and vision for the company, 2) the market the startup operates in and the tailwinds propelling the company’s growth, and 3) the traction/customers/proof-points that serve as leading indicators for the company’s future growth.

Crafting the press release

Even if you never publish it, creating a press release is a great foundation for your company’s strategic narrative that you’ll push as part of the announcement.

Founders should create a first draft of their press release, get feedback from key team members, and circulate it with trusted advisors with expertise in marketing/comms/PR.

A good funding announcement press release has a few key elements:

1. Make the company feel bigger than it really is - tell the world about the vision while making the product tangible and grounding the language in today, not the future

2. Explain what the product is, what types of customers use it, why they use it (and what they were doing before), and the value it provides them

3. Size up the market opportunity and tell how/why the product is aligned to key shifts that are transforming the market

4. Announce the funding round and investors who participated

5. Share traction metrics that are vague enough to not provide exact details on the company’s size, but specific enough to reflect traction (millions in revenue, hundreds of customers, 10x growth, etc.)

6. Founding team’s background and a quote from the CEO

7. Customer quotes about why they use the product

8. Tell prospective customers & employees how they can get in touch with you

A recent great example from one of my investments is Angle Health’s Series B press release.

Here is a core template:

• Headline: [Company] raises $X to scale [vision] for [ICP]

• Subhead: Founded by [founder], [company] is reshaping [market] with platform trusted by [customers]

• Problem: Before [Company], teams had to [bad workaround]

• Solution: Now they can [better outcome] with [fewer resources, less time, etc.]

• Traction: Used by [range] customers across [industries]

• Quote: CEO vision

• Quote: Customer outcome

Executing a news strategy

Once you’ve drafted your press release, pick a news platform - TechCrunch, Axios, WSJ, etc. and pitch them the opportunity to get the exclusive on your fundraising story.

Try to find a warm intro to a journalist, because journalists who cover financing rounds are inundated with pitches. Anyone with experience in marketing/comms should have some journalist connections, but investors can often help here too.

Three rules of thumb when pitching journalists:

1. Offer the journalist access to an exclusive - this is an opportunity for them to publish the story before anyone else, and it makes it a more enticing pitch if they can get exclusive access to an interesting story.

2. Give the reporter at least a week or two heads up prior to your launch date. It needs to accommodate both their writing schedule and give them time to write the story.

3. Share enough information so that they can get interested in the story and agree to an exclusive. The fewer details you share up-front, the harder it can be to get the reporter attention. Offer to make the founders, investors, and key customers (if possible) available for interview.

If the journalist agrees, make sure to get aligned on an embargo date - the day of your launch - prior to which they agree to not share any information with the public. Share the press release and other information with them, and use the opportunity to get to know them.

All of that being said, if the funding round is small (sub-$3M), it will likely be challenging to get a reporter to do the story (unless there is a special hook).

In that case, the best bet is to go for daily deal newsletters. Axios Pro Rata is an example. They typically include any deal regardless of amount, but there isn’t a story - deals get one-line features in a list of deals. However, they often link to press releases that can give readers more information, and it’s better distribution than nothing.

Finally, make a decision around whether or not to publish the press release via a wire service. The upside is that each of the major media outlets focused on venture/business deals will receive the press release and can choose to publish their own article. The downside is it costs a bit of money ($1k) and it’s unlikely any news outlets pick up an article based just off the press release unless it’s a significant funding round.

PR agencies can help with all of the above, but they come with additional costs.

Writing a blog post for your website

Blog posts on startups’ website are an important part of executing an effective launch/press day. They’re often linked to via press releases, social media posts, and news stories, which gives company websites a traffic boost. If done right, that can mean prospective customers sign up for the product and potential hires apply for a job.

When writing the blog post, use the material from the press release, but throw in videos such as customer testimonials/interviews and short product demos. This makes the blog more engaging than a typical press release, and adds context for prospects.

A great recent example of a blog post I liked was Serval’s Series A funding announcement.

Once the announcement is made and the blog post is live, there should be a banner at the top of the website homepage that links to the blog post.

Ensure there is a clear call to action that allows prospects to request a demo and potential hires to apply for open roles.

Gathering customer testimonials

Customer testimonials are a great way to showcase the companies who use your product and the use cases they have for it. They become referenceable for future customers, and lend credibility to potential hires and investors.

At a minimum, quotes from key customers are helpful. If you can conduct customer interviews and string them together in a short-form video that can be shared on socials and your website, that’s even better.

Create a social media strategy

Social media offers the most upside for attention on your announcement, but it requires the most attention and creativity to drive that attention.

If you manage to get an article placed with a major news outlet, make sure you time your social media posts to coincide after the article has been published on the news outlet’s website, and make the article a key artifact you share on socials.

Launch videos have become another key artifact around startups’ fundraise announcements in 2025. If the video is short, creative, and attention-grabbing, it can drive millions of impressions. Cluely kicked off the launch video craze this year with their “cheat on everything” ad. A less controversial example for B2B businesses I like is Cofounder. If you’re looking for something more creative, I love this Kalshi commercial and Cotool’s launch video (which parodied the Deel/Rippling spy drama, which was a dominant meta narrative on tech twitter at the time).

There are lots of ways to structure an effective launch video, and in the age of AI they can be done cheaply using Veo, Nano Banana, and Adobe Premier.

Between the press release, website blog post, news article, customer testimonials, and launch video, that is a full docket of artifacts to share on launch day.

Plan out each of the social media posts ahead of time - when, where, and what will be posted. Don’t leave any decisions to make the day of the announcement.

Executing an effective Launch Day

The day before launch day, founders should send an email to employees, investors, and other relevant stakeholders letting them know you plan to make your announcement the next day.

Provide a timeframe they should expect the story to be published in the news and on your social accounts, and ask that they withhold any posts until they’ve seen you post first.

Once the story is out, amplify it by sharing the launch artifacts across your personal and company social media pages.

Once you’ve posted, send an email again to your investors, employees, customers, and other stakeholders pointing them to the news article, press release, company blog post, and your social media posts.

The email should highlight several things:

1. The coverage from a top business media outlet, reflecting your company’s growing leadership in the market

2. The capital you raised will allow you to accelerate your product roadmap to better delight your customers and continue your mission

3. The customer testimonials and investor relevance to the company’s mission

4. Links to each artifact

5. A call to action for them to post on their social media channels. Make sure the people in your network with the largest followings are actively posting

6. Examples of text they could use in their post/re-shares on social media

Follow-up the next 1-2 weeks by sharing additional artifacts beyond whatever you initially shared. Create dedicated posts focused on sharing customer stories, product demo highlights, or spotlighting the founder’s background. The goal should be riding the momentum from the announcement to maximize chances of driving inbound traffic to the company.

Closing Thoughts

Treat your funding announcement as a GTM event, with the same rigor you’d have as your sales motion or fundraising process. Funding announcements should compound brand, pipeline, hiring, and downstream capital raising - don’t waste it!

If you’ve made it this far, hopefully you’ve found this post helpful. I put together a checklist with 15 action-items from this post that startups can use to execute an effective fundraise announcement. If you’d like to get a copy of the checklist, reach out!

Helsinki, while absolutely freezing this time of year, is a great city and very underrated. It’s beautiful, clean, easy to get around, and most importantly for me - there are some incredible restaurants. If you get the chance to go, I recommend it. At some point I’m hoping to experience it when it’s warm and not in the middle of winter.

Anyway, Slush is a unique conference. It isn’t associated with a particular vendor, like Salesforce Dreamforce or AWS ReInvent, so no one is trying to sell the attendees anything. It’s really about facilitating connections among the European tech ecosystem.

If Slush is trying to sell its guests anything, it’s the idea that the European tech ecosystem is important, and that its stakeholders - citizens, companies, and policymakers - should do more to actively foster it, for the benefit of the entire continent.

That’s a powerful idea, because Europe - despite its geographic size, cultural weight, and strength in industries like luxury goods, autos, and pharma - is punching below its weight in tech. Europe’s tech ecosystem has grown significantly in the last decade - capital raised from tech startups this year is up more than 3x relative a decade ago. But it could be growing more, and faster. There were a few core ideas that people at the conference discussed for how to change that.

4 Ways to Support European Tech

Fixing cross-border regulation. While the countries of Europe are bound by several types of political and economic agreements, most notably the EU, each country is its own patchwork of laws that govern its citizens. For startups, that means radically different rules they have to comply with when expanding operations across countries, especially when it comes to things like incorporating a business, hiring and firing workers, and paying taxes. EU policymakers who spoke at the conference referenced the “28th regime”, a proposed alternative legal framework for companies to follow instead of adhering to each country’s specific business laws.

Improving workforce mobility. In the US, most employees are “at-will”, and can be fired basically for any reason as long as it doesn’t involve discrimination. In Europe, “at-will” employment basically doesn’t exist and employers are required to have documented processes with justified reasons, as well as varying degrees of severance and notice periods. For startups, where speed of execution is often the number one determinant of success, the illiquidity in labor markets is like a governor on a car’s engine reducing the potential speed that startups can reach.

Deepening risk capital system. As Jeff Bezos said earlier this year, America’s system of risk capital is a major contributor to our entrepreneurial system. By comparison, Europe’s risk capital system is much smaller. According to Atomico, Europe’s tech startups are on track to raise $44B this year, compared to $215B in the US. Globally, Europe accounted for 17% of venture capital in 2024. Interestingly however, Europe is on par with the US in terms of the number of startup entrepreneurs - each the US & EU accounts for roughly a quarter of the world’s founders. Where the risk capital depth is most acute is in growth-stage/late-stage funding. According to Crunchbase, earlier this year Europe accounted for 20% of the global early-stage venture funding, but only 10% of late-stage. The largest startup financing round in Europe this year has been Mistral, which raised a $2B series C. Compare that to OpenAI, which raised $40B this year.

Creating a European tech giant. All of the “Magnificent 7” companies, which increasingly dominate the value of global stock markets, are based in the US. Europe has 5 tech companies valued over $100B, but it has yet to mint a trillion-dollar company. While there are a number of candidates who could reach $1T like ASML, Stripe, or Revolut, it likely won’t be until at least the 2030s given their current growth rates. Europe’s lack of tech giants is a challenge for many reasons, not least of which is that much of the massive funding we’re seeing taking place in AI in the US is actually coming from tech giants rather than private investors. However - and surprisingly - Marc Lemaître, the Director-General of the R&D division from the European Commission, who spoke at Slush, said he wasn’t sure if creating a trillion-dollar company was an important factor in fostering Europe’s tech ecosystem. I couldn’t disagree more - American tech giants do a lot of the US tech ecosystem including serving as sources of M&A (and liquidity) for startups and investors, train employees, and as I mentioned make massive investment in R&D to push the industry forward. Most importantly, tech giants supply the north star for every new founder: a visible peak that shows what’s possible when ambition, timing, and luck all converge.

The case for Europe

Europe has no equivalent to the American dream. But many Europeans seem determined to create one.

In my experience, European entrepreneurs are just as ambitious, creative, and determined as those in the US. AI and defense represent two industries with massive tailwinds for Europe to capitalize on over the next decade. Large European tech startups like Stripe and Revolut, as well as global tech players with a presence in Europe such as Palantir, are creating their own “mafias” of employees who leave to become founders.

Some of the most exciting AI-native startups like Lovable, Synthesia, Legora, and ElevenLabs call Europe home. And I’m proud that some of my firm’s most exciting investments, like Membrane, SurrealDB, and Robco (and others still in stealth mode) are based there.

Spending three days at Slush gives you a glimpse into the ambition that Europeans have for their tech industry. If you still perceive Europe as slow, conservative, or allergic to risk - go to Slush and it’ll change your mind.

They were right. And yet, the trade didn’t work. Tesla’s stock went up ~14x between 2017-2020. The bears lost many fortunes and in some cases shut down their funds.

Why? Many reasons, including Tesla’s compelling cars, direct-to-consumer distribution, global factories, and mission-driven brand.

But underlying all of that was one thing - Elon built a cult of personality around himself.

In fact, Elon was so successful at this, that “building cults” has become a bit of a meme on X/Twitter and in the startup community more generally.

Marc Andreessen was recently on the Cheeky Pint podcast and said:

“The Elon method…, which is becoming more and more salient, I think. And something we’re trying to get our founders to do a lot more of is ‘I’m going to become a cult of personality’. And it’s going to be a cult of personality, not just inside the company, but outside the company. And we’re not going to spend any money on marketing. We’re not going to put any time into IR. We’re going to put on the greatest show of all time and the company and the stock, the books, the videos, the products, the jobs are all a function of the cult of personality.”

The Elon method of “building a cult” translates belief into a corporate asset. The result is lower marketing costs, lower capital costs, and better access to talent.

But how? And can anyone else actually do this besides Elon?

Let’s dive in.

Storytelling as Capital

Elon Musk - by speaking in public about his mission and his struggles - created a following of believers who bought Tesla stock. They held through thick and thin - even through 2018 when cash dropped below $2B, and nearly bankrupted Tesla before it could see through to the Model 3 sales ramp.

Its dedicated shareholder base enabled Tesla to raise $18.5B in equity between 2015-2020. The capital was a lifeline that got the company through what Elon called production hell. In 2019 the Shanghai Gigafactory launched, and in 2020 Tesla printed four straight quarters of profitability and was included in the S&P 500.

5 years later, Tesla continues to defy the gravity (low P/E multiples) that would pull down most car companies. Tesla’s price-to-earnings multiple on 2025 earnings is 217x vs. Ford at 11x and GM at 6x. With a $1.4T market cap, it is the 10th most valuable company in the world - just ahead of Warren Buffett’s Berkshire Hathaway.

Elon was the first modern CEO to rewrite the boundary between product and story. CEO’s have taken heed.

The Elon model - of building in public, creating a spectacle, using social media and the press to speak directly to the public and investors, and convert belief into cheaper capital and distribution - has now become the playbook followed by many leaders in tech.

But who besides Elon is actually running that playbook?

Dr. Karp

Palantir trades at 114x 2025 revenue estimates. The next most expensive software stock is Cloudflare, which trades at 36x, while the median high growth software company trade at 14x (high-growth being defined as growing revenue over 20% this year).

To put that in perspective, Palantir’s $470B market cap is worth more than Salesforce and Adobe combined despite having $4B in annual revenue compared to Salesforce’s $41B and Adobe’s $24B.

Don’t get me wrong - Palantir is an incredible business. It is the only major publicly-traded software company expected to grow revenue over 40% this year, making it the fastest growing software stock AND has scaled free cash flow margins of 45%.

But the numbers don’t explain the valuation premium, so what does?

Dr. Alex Karp, Palantir’s CEO1 .

In the last few years since Palantir went public in 2020, Karp has shifted his public persona from a quiet, if eccentric, bookish intellectual - to an aggressive and energetic philosopher warrior. Nowhere is that contrast starker than in this 2009 interview on Charlie Rose compared with this 2024 interview on CNBC.

Karp, through sheer force of his personality, has built an investor base that believes Palantir is not just a data integration and analytics platform with an effective go-to-market strategy that can win large government & enterprise contracts. Rather, they believe Palantir is at the frontlines of the West’s battle against authoritarianism.

Aside from positioning Palantir at the center of an existential battle of good and evil, Karp is also funny. He’s entertaining, and unquestionably himself - authentic.

That blend has created a cult following among Palantir’s investor base that has propelled the company to the second most valuable enterprise software company in the world, only behind Oracle.

Palmer

It’s not just Elon & Karp.

Palmer Luckey is clearly running a similar playbook at Anduril. He talks about it here, explaining the difference between merely growing follower count on social media, and building a cult following among people who matter.

“I like to see the growing follower count as a side effect of the larger strategy. It’s okay if they think you’re a little nuts, a little out there - that’s okay. But they can’t think you’re immoral. They can’t think you’re not credible. They can’t think you’re incapable. It’s okay if they question your style, but the substance has to be there.”

Palmer’s core belief in founding Anduril was that the US military was falling behind in innovation, and failing to integrate modern technology into defense would lead to a West that was vulnerable to hostile players abroad. The artifacts of that belief are not just the cutting-edge products like an unmanned fighter jet named Fury, but Palmer’s ability to talk about it everywhere from mainstream outlets to 60 minutes to Joe Rogan, where he recently went on to talk about Anduril’s new AI-powered helmet, EagleEye.

The Information recently called Anduril “the most coveted stock in the private markets”, with the stock trading at a 2x premium to the round done last June, which was at a $30B valuation.

Now Palmer is going to run it back in a new industry - banking - with Erebor, the new tech-focused bank where Palmer serves as chairman of the board and is the main shareholder.

The Founder Cult Flywheel

A company is, in many ways, a manifestation of the beliefs of its founder.

Tesla was founded out of Elon’s belief that society needed to accelerate its path toward sustainable energy. Palantir was founded on the belief that the West needs superior technology to secure its freedom. Anduril was founded on the belief that the US’s military is falling behind and needs modern technology to protect America in the 21st century.

But it’s not just them - I could write a much longer post talking about what Vlad Tenev at Robinhood is doing, Sam Altman at OpenAI, Dario Amodei at Anthropic, Patrick and John Collison at Stripe, Jensen Huang at Nvidia, and the list goes on.

Founders who can put their beliefs out into the public, and elevate their company’s mission to the most ambitious version of itself have the potential to create a sustainable competitive advantage.

Here’s how it works:

• It starts with the founder’s belief, framed as a story with high stakes, and if possible an existential enemy.

• The story is manifested into artifacts - products, ads, CEO letters to investors.

• Those artifacts create a community who identify with them. Customers, investors, employees, partners, and the broader public.

• The presence of that community reduces transaction costs across the business. Lower customer acquisition costs, cheaper capital, better talent, and pricing power - all should lead to strong execution.

• Execution produces results - proof the community needs to strengthen their conviction.

• Scale reinforces the founder’s story, garners more believers, and the viral loop continues.

The market rewards founders who can convert storytelling into operational leverage.

Operational Leverage

Despite me using the phrase throughout this post, I honestly think “building a cult” has become a meme, and a bit a cringey.

However, the reason it has become a memetic, over-used idea is because it has a kernel of truth in it.

That truth is about the power that founders have when they generate deep belief in their company among investors, customers, partners, and the broader public.

Belief that goes beyond the numbers and the product - but rather to the long-term vision of the company.

Generating that level of conviction is about creating a feedback loop between story and execution. Story creates permission from the public. Proof renews their belief with greater conviction. When done right, belief can lower the cost of distribution, capital, and talent. It ultimately can compress the distance between vision and execution, the future and the present.

“Building a cult” is how founders convert story-telling into operational leverage, which can become a competitive advantage. Which in the current age of AI, abundant capital, and existential questions about how companies succeed in the future - is something we’re all looking for.

At the time, I was a summer analyst at a hedge fund, and Credit Suisse had invited a few dozen of their hedge fund clients’ interns to listen to Mauboussin speak.

Mauboussin began his talk by handing a jar of jellybeans to the audience of 100 summer interns. He gave each intern an index card and asked them to write down their guess for the number of jellybeans in the jar, put their index card into a bucket, and pass down the jar of jellybeans and the bucket to the next person.

Over the course of his talk Mauboussin delivered his greatest hits - a series of frameworks for investors covering luck vs. skill, base rates, and culminating in the wisdom of crowds.

At the end of the talk, Mauboussin had one of his associates read out the average of the group of interns’ guesses on how many jellybeans were in the jar.

The average of all of the guesses: 1,162. The actual number of jellybeans: 1,151.

The average guess of the group of interns nailed it within 1%.

Mauboussin used the jellybean jar to explain this idea known as the wisdom of crowds - the power of aggregating many independent views into a signal that can be more accurate than any single participant. That mechanism underlies markets, the economy, and, in many ways, capitalism itself.

My 21 year-old mind was blown. I went back to my office, started researching prediction markets, made an account on PredictIt, and ordered a copy of James Surowiecki’s The Wisdom of Crowds. Much of Mauboussin’s work draws on that book’s core idea: crowds can be highly effective at making predictions when certain conditions are present.

As a venture capitalist, I have been equally fascinated by the rise of Kalshi and Polymarket as I have been frustrated with myself for not having invested in either of them! The two startups, valued in the billions, turn the wisdom-of-crowds theory into tradeable contracts about the future. The price of each contract reflects the probability of an event.

So far the wedge for Kalshi and Polymarket has been elections and sports games - a fun way to make NFL Sundays more exciting.

But their potential runs much deeper.

Prediction markets could become a pillar of the global financial system, as they allow us to get a glimpse of the future.

Their implications for individuals, businesses, and entire economies are huge. So let’s dive into prediction markets - what they are, how they work, and how they stand to impact the economy going forward.

The price of the future

Capitalism, the economic system that brought the world from agriculture to AI, rests on one simple idea - the price mechanism.

A fundamental difference between capitalism and other economic systems is that prices of goods and services are set by buyers and sellers rather than any organization or government.

Under the right conditions, markets are very effective at price discovery - revealing the true value of what a good or service is worth.

Whether we’re talking about the agora of ancient Athens, where merchants haggled over prices of olives and wine, or the modern stock market where high-frequency traders move trillions of dollars electronically - the mechanism is the same: prices that reflect the value of a good or service emerge organically as people trade.

Prediction markets work the same way. Instead of olives or stocks though, participants in prediction markets buy and sell contracts related to future events. Who will win the next presidential election? Will inflation come in above expectations next month? Which movie will win the Oscar for best picture this year? When will the price of bitcoin hit one million?

How prediction markets work in practice:

• Prediction markets quote their contracts $0.00 to $1.00.

• If the price of a contract “Republican wins 2028 US presidential election” is $0.60, it reflects the market’s belief that there is a 60% probability of a Republican winning.

• If you think the true probability is higher than 60%, you should buy at $0.60. If the Republican does win, the contract will pay out $1.00, and you profit $0.40.

• Since it’s a market, traders can sell their shares at any time. If the news changes, and shares trade up to $0.65, you can sell your shares at a profit before the election actually takes place.

So by buying and selling these contracts, market participants turn information into prices, and those prices encode probabilities - but it only works if certain conditions are present.

Wise crowds and mobs

Sir Francis Galton is credited with first observing the wisdom of the crowd. In 1907, Galton was at a livestock fair where 800 people participated in a contest to accurately guess the weight of the cow. Galton was shocked to see that the average of the guesses came within 1% of the cow’s actual weight. He wrote about his experience in an article in Nature, remarking “this result is more creditable to the trustworthiness of democratic judgement than might have expected.”

After a century of development in financial markets and economic theory, James Surowiecki’s The Wisdom of Crowds popularized the conditions required for crowds, like the one Galton observed, to have good judgement.

The four conditions under which crowds, or markets, can make accurate predictions:

• Diversity of views. Market participants bring different information, biases, and mental models. Diversity makes individual errors cancel each other out, rather than compound them.

• Independence of judgments. Traders form opinions without being influenced by each other’s opinions. Independence keeps errors uncorrelated.

• Decentralization of information. Knowledge/information about the outcome is dispersed. No single authority dictates the outcome.

• Aggregation. A mechanism exists to reliably turn many views into a single signal (i.e. a price). The mechanism should be liquid (lots of traders), transparent (easy to understand what they are betting on), and hard to game (traders can’t single-handedly influence the outcome they’re betting on).

However, wise crowds easily turn into a mob. Five ways markets can break down resulting in poor price discovery:

• Homogeneity. When everyone thinks the same way, errors compound.

• Centralization. A single authority or narrative drives the market.

• Division. Liquidity or beliefs fragment into camps.

• Imitation. Investors mimic others instead of making independent decisions.

• Emotionality. Fear or euphoria swamps rational valuation.

Inevitably over the course of any market’s history - whether its tulips, stocks, real estate, or Beanie Babies - there will be periods where the conditions for effective price discovery break down. What matters is the market is structurally set up in such a way to support diversity, independence, and decentralization.

Okay - enough theory. Let’s dig into modern prediction markets and where they’re going.

Prime Time

The first modern prediction market was launched in 1988 at the University of Iowa. The Iowa Electronic Markets (IEM) has been running for almost 40 years, regularly outperforming polls at predicting outcomes of elections. Along the way, prediction markets garnered increasing attention with small platforms like PredictIt, but they were mostly confined to academia and small studies.

That changed in 2020, with the launch of Polymarket, and the subsequent launch of Kalshi in 2021.

Polymarket was first to take prediction markets on-chain, launching the first blockchain-based prediction market.

• How it works: Traders bet on an outcome with USDC. When the the outcome is settled, an independent checker known as an “oracle” in the crypto world, looks at public sources and determines what happened (i.e. which team won the game). Once confirmed, the software automatically pays out USDC to the winners.

• Their core innovation: Polymarket applied the blockchain’s trustless verification to prediction markets, replacing a middleman with software.

Kalshi’s approach focused on regulation, launching the first prediction market regulated by the US government.

• How it works: Overseen by the CFTC, Kalshi is a fully regulated U.S. exchange. It allows traders to trade in dollars, and they put up money in advance to ensure guaranteed payouts. A centralized system, similar to those that power commodity exchanges, ensures that traders get paid correctly. Each contract has an official data source cited to publicly determine outcomes of an event (i.e. citing the Bureau of Labor Statistics for inflation prints).

• Their core innovation: Kalshi’s was the first fully regulated US prediction market.

While Polymarket doesn’t (yet) charge trading fees, Kalshi does have trading fees and withdrawal/deposit fees.

Kalshi’s last valuation was $2B, backed by Sequoia and Paradigm. Polymarket’s is $1B, backed by Founders Fund and General Catalyst, and rumored to be getting interest at higher valuations.

Other companies are taking note and launching their own prediction markets, from juggernauts like Robinhood to startups like ProphetX.

Sports and politics have been the wedge so far. The US election last fall saw massive trading volume, and football season will likely drive another season of growth.

It makes sense - betting on sports and politics is fun. People care, they have opinions, and they can talk about it with their friends.

Plus, sports betting in America generates hundreds of billions in annual trading volume and $10B+ in net revenue for betting/gambling platforms. That is a big pie for prediction markets to eat into - and grow - by making betting more accessible.

A less appreciated, and potentially transformative opportunity for the prediction markets isn’t with consumers, however. It’s with institutions - businesses, investment firms, and multi-national corporations.

From Bets to Budgets

Managing risk is a core part of any business or investment firm. Idiosyncratic shocks like war, natural disasters, economic depressions, or even small changes in crop prices or supply chain disruptions can have major negative consequences to businesses.

Helping those institutions hedge those risks is a massive business today, dominated by exchanges like the CME, clearing houses like LCH, and banks like JP Morgan and Goldman Sachs. These organizations offer derivatives, futures, options, and other financial instruments that help corporations offset unwanted moves in interest rates, currencies, commodities, and more.

To give you a sense of scale, the largest derivatives exchange in the world, the CME, generated $5B in revenue last year tied to fees from its derivatives.

Off its total revenue base of $6.1B last year, the CME generated 64% operating margins and 57% net income margins. Its market cap is just shy of $100B.

It may not be a hyper-growth AI company, but that is quite a good business!

However, most derivatives aren’t traded on exchanges like the CME. The overwhelming majority of derivatives are traded in what are known as over-the-counter (OTC) transactions. These are privately negotiated deals between the buyer of a derivative (i.e. a corporation or investment firm) and a counterparty like a bank.

So the opportunity for prediction markets to get into institutional hedging is actually much bigger than just taking share from CME and other derivatives exchanges. It’s also about going after the OTC market, and the tens of billions of dollars of revenue generated by investment banks and other counterparties.

Prediction markets have several advantages over traditional hedging via OTC derivatives. They allow for better precision, with contracts tied to specific events. Bespoke OTC derivatives often take weeks to spin up, with meaningful legal costs, whereas prediction markets can offer broad catalogues of contract offerings. Being cloud-native with modern infrastructure should also enable prediction markets to offer high quality data feeds and APIs - which gets to our second institutional market opportunity for prediction markets.

Selling the Tape

Just as Robinhood monetizes via payment for orderflow, Kalshi and Polymarket have the potential to launch large data businesses. Kalshi has already launched an API.

As prediction markets continue to scale their market catalogues across macro, crypto, elections, and more, institutional investors will want access to that data. Small changes in the prices of these contracts could serve as a valuable predictive insights to investors and corporations.

By comparison, CME generated $710M last year from their market data business.

What Could Go Right?

Prediction markets have gone from small, academic curiosities that test fundamental principles of economics, to become major businesses that drive billions of dollars in trading volume every year. And it’s only the beginning.

Real tailwinds are propelling the growth of prediction markets. Retail investors are only becoming more numerous as society becomes wealthier and more online. Regulatory relationships support legitimacy and institutional trust. Polymarket and Kalshi have barely scratched the surface of the thousands of contracts they could potentially expand into. And the market opportunities from institutional hedging and selling data via API can be even bigger than their consumer businesses.

They have a long way to go, but Kalshi and Polymarket are already starting to put together the building blocks of a global financial market - deep liquidity, regulatory relationships, strong brand, and infrastructure required to handle scaled trading volume.

As they continue to expand the types of contracts they offer and the complexity of transactions they can handle, the combined consumer + institutional market size for prediction markets could be a revenue opportunity measured in the tens of billions.

From jellybeans to elections, prediction markets tell us what might happen tomorrow, and that’s a price worth watching.

Before diving in though, I want to highlight that in many ways, the story of Netskope and their decision to IPO at this point in the company’s lifecycle, is a reflection of the broader state of the capital markets in tech, and of many of the largest private SaaS companies.

Like many other SaaS leaders, Netskope started at the beginning of the 2010s, rode a massive wave with the rise cloud computing, and now have a full product platform that can compete with the largest companies in their category.

Now, the cloud wave that carried their decade-long growth run has largely played out. Netskope has matured - it is a market leader and is shifting focus to becoming profitable.

At the same time, a new market is breaking open with AI, which represents a new s-curve, shifting the market in such a way that threatens to displace mature companies like Netskope - the same way the cloud threatened legacy security providers over a decade ago. But that same threat is also an opportunity, if they can capture it.

So Netskope is going public as it embarks on its second act as a company - becoming profitable, maintaining category leadership against larger competitors, and navigating product expansion to address the AI opportunity.

It’s a similar story to other IPOs we’ve seen in the current cycle, and one we’ll continue to see. The same could be said about other privately-held SaaS leaders who are long overdue for an IPO, including Canva, Airtable, Miro, Snyk, Databricks, and many others.

To be clear, it’s better late than never! But it would be wonderful if we could get back to a place where tech companies IPO’d earlier and retail investors were able to participate the value creation that comes with being a shareholder in a company experiencing hyper-growth. Until 2021, that was the case, and software companies went public with anywhere from $100-500M in revenue.

Unfortunately, for many reasons I’ll cover in a future post, the capital markets have experienced a structural shift and the trend of companies going public later in their lifecycle will likely continue.

Netskope is still an incredible company and the tech industry should applaud their decision to go public!

With that, let’s dive in.

Founding Story

In 2011, Sanjay Beri was VP/GM of the Access & Security business at Juniper Networks. Juniper was a provider of networking hardware that was one of the darlings of the telecom/dot-com bubble - at the peak in 2000 it was worth $40B+ and itself was one of the best venture outcomes ever, but that’s another story (it was acquired by HPE earlier this year).

By 2011 Juniper was a steady, boring business selling hardware that powered corporate IT networks. The nature of those networks were changing to become increasingly defined by software rather than hardware - a shift that Sanjay Beri had a front row seat to.

As businesses were increasingly adopting SaaS, accessed via a browser, instead of traditional desktop software, old perimeter-based security solutions became obsolete. The idea of a “corporate network” shifted from a fixed location - an office with a server room, desktop computers, and wired internet - to anywhere. Corporate computing was starting to take place on mobile devices, tablets, connected factories, and anywhere with an internet connection.

Around the same time in 2011, Chamath Palihapitiya had just founded Social Capital, and recruited Mahmoon Hamid from US Venture Partners to be a general partner at his new firm. Mahmoon now runs Kleiner Perkins and is a top venture capitalist, but at the time he had only been working in venture for 6 years.

According to Mahmoon’s original blog post announcing the creation of Netskope, he and Sanjay originally met through Mahmoon’s wife - the two of them were classmates at University of Waterloo together. Mahmoon and Sanjay started talking about the future of cybersecurity in the cloud, and Sanjay’s desire to start a company going after the opportunity he saw.

In 2012, Mahmoon ended up leading Netskope’s seed round with $5.5M and incubating the company for the first 6 months at the Social Capital offices. Lightspeed followed with a $15.9M Series A in 2013 and the company was off to the races.

Product

Netskope’s first product was a cloud access security broker (CASB) which provided control and security over SaaS apps that employees could access. Any sensitive data being put into a SaaS app (or taken out of it) could be stopped in real-time by Netskope.

Since then, the company has expanded to offer security, networking, and analytics in a single platform they call Netskope One.

Netskope One is a product portfolio including over 20 individual product modules, broken down into three categories:

• Security: Netskope watches and controls how company data moves across cloud apps, websites, and internal systems. Abnormal, high-risk behavior is stopped in real-time to ensure sensitive data doesn’t leak out of the organization.

• Networking: Netskope built a network of 120 data centers in 75 regions globally to securely route its customers corporate internet traffic, removing the need for customers to have physical firewalls.

• Analytics: Netskope unifies activity across users and apps into clear dashboards and alerts, so security teams can see what’s happening, fix performance hiccups, and prevent problems before they spread.

Netskope’s unified stack of tools that protects data, applications, and users is known in the security industry as a Secure Service Edge (SSE).

Over the last few years, Netskope has expanded beyond SSE into networking products that enable its customers to connect their employees securely to the internet without needing to use legacy VPNs or firewalls.

This combination of security + networking tools is known as a Secure Access Service Edge (SASE), and has become the defining product suite offered by network security leaders like Palo Alto Networks, Fortinet, and Zscaler - Netskope’s largest competitors.

Rather than list out Netskope’s 20+ products individually, I’ll focus on the ones that matter the most.

• Cloud Access Security Broker (CASB): Netskope’s original core product. CASB provides visibility and control for data in cloud apps like Office 365, Salesforce, Slack, etc. It can detect sensitive data going into or out of cloud apps, and enforce policies (like preventing confidential files from being shared publicly), and protect against cloud-based threats. Netskope was one of the pioneers of CASB as a category.

• Secure Web Gateway (SWG): A cloud-based web proxy that inspects and filters internet traffic. It protects users as they browse websites or use cloud services, blocking malicious content and enforcing acceptable use policies.

• Zero Trust Network Access (ZTNA): Also known as Netskope Private Access, this allows authorized users to securely connect to internal applications without using a legacy VPN.

• Next-Gen Firewall (FWaaS) and SD-WAN: These are the networking parts of Netskope One. They enable enterprises to route employee internet traffic through Netskope’s cloud and apply firewall policies without on-site hardware.

  • Netskope calls its private secure cloud infrastructure the Netskope NewEdge Network. With over 120 data centers in 75 regions globally, this is a competitive offering against companies like Cloudflare. By having global infrastructure, Netskope can offer its customers a fast internet experience with better security than legacy VPN providers.

• Data Loss Prevention (DLP): Across the entire above suite of products, Netskope’s DLP features can detect and block sensitive data exfiltration (credit card numbers, intellectual property) and scan downloads for malware.

In summary, Netskope’s product has evolved from data security into a comprehensive platform of security and networking products that address a wide range of customer needs. From enabling safe SaaS usage, to completely replacing legacy firewalls, Netskope has consolidated a variety of use cases that position it to compete head-on against the biggest names in cybersecurity.

Business Model

Netskope has a classic subscription-based software-as-a-service business model. Most of Netskope’s revenue is from recurring subscriptions, structured as annual or multi-year contracts (typically 1-3 years).

The two primary levers that determine Netskope’s pricing are 1) the size of the customer’s employee base, and 2) the number of product modules they’re consuming.

Netskope has been effective in selling into its installed base of customers, as the above chart shows.

In the last fiscal year ending Jan-25, 68% of the Netskope’s revenue growth was driven by expanding contracts with its existing customer base, and the other 32% of growth was driven by new customers.

Go-to-Market

Netskope’s GTM motion is focused on top-down, enterprise sales. Over 85% of its revenue comes from customers who spend over $100k per year. Netskope counts of 30% of the Fortune 100 and 18% of the Forbes Global 2000 as customers.

Roughly 1/3 of Netskope’s employees are in sales or customer-facing roles, and they often sell to the Chief Information Security Officer or senior head of security.

Like many cybersecurity vendors, Netskope relies heavily on a channel partner ecosystem of value-added resellers and managed service providers. 93% of Netskope’s revenue goes through partners, and 7% is directly between Netskope and the end-customer.

In the S-1, Netskope disclosed that one of their channel partners accounted for 12% of their revenue in fiscal year 2025. My guess is that partner is likely Exclusive Networks, who Netskope cites in their case studies as a key partner.

Leveraging partners is a key part of many cybersecurity vendors sales strategies. Microsoft, Cisco, Palo Alto Networks, Fortinet, and the largest security vendors all extensively use channel partners.

Many large enterprises prefer to procure their technology through a partner, who can help implement it and ensure success. For Netskope, they get to leverage the partner’s integration services teams without needing to manage that expertise in-house. Of course, the partner gets a cut of that revenue, but they also often end up pulling Netskope into deals, so these are highly synergistic relationships.

Financials and SaaS Metrics

Netskope has solid metrics, coming public just as it is inflecting to profitability while maintaining relatively high growth rates.

Below I compare Netskope’s key financial metrics to the 25 publicly-traded cyber security companies.

Note that Netskope is on a January fiscal year, so when I refer to fiscal year 2025, it is the 12-months leading up to January 2025, essentially equivalent to the calendar-year 2024. As of this writing in August 2025, Netskope is in its third quarter of fiscal year 2026.

• ARR: As of July 2025, Netskope reported $707M in ARR, up 33% from $531M in the July 2024 period. For fiscal year 2025 (calendar year 2024), Netskope reported $618M in ARR.

  • If we assume ARR growth continues at ~30% Y/Y, Netskope would achieve $803M.

Below I estimate Netskope’s ARR path over the last several years, based on a combination of data sources:

• For data from 2019-2022, I use Sacra’s estimates.

• For 2024 and 2023 I use data disclosed from Netskope in their S-1, for the fiscal years 2025 and 2024. Note the fiscal year is slightly misaligned from the calendar year, so these estimates are likely different from actuals.

• For 2025 (fiscal 2026), I project forward 30% Y/Y ARR growth, conservatively based on ARR growth as of July-25, which Netskope discloses as 33%.

• Revenue: In the most recent quarter, July-2025, Netskope reported $171M of revenue, up from $130M the year prior (+32% Y/Y). Annualized from the most recent quarter, this would be $684M. Last year Netskope delivered $538M in full-year revenue.

  • Assuming Netskope ends up delivering roughly $700M in full-year revenue for the 2026 fiscal year (2025 calendar year), it would be in the bottom half of publicly-traded cybersecurity stocks.

  • Netskope would be among the fastest growers though, and join only two other companies growing over 30% - CyberArk and Rubrik. However, with CyberArk potentially being acquired by Palo Alto Networks, Netskope stands to be the fastest growing cybersecurity company in the public markets (assuming the deal goes through and CyberArk is acquired).

• Gross margins: Netskope’s gross margins were 69% in fiscal year-2025, and 74% in the first half of the current fiscal year 2026.

  • Netskope’s gross margins are slightly below the high-70s average for publicly-traded cybersecurity companies. The main reason for this is likely because of the infrastructure investments in building out its networking and datacenter network. As it grows its customer base on these products, it will realize operational efficiencies that bring it closer to Cloudflare’s gross margins in the high 70s and Zscaler in the low 80s.

• Operating margins: Netskope’s operating margins are deeply negative, with -34% non-GAAP operating margins in fiscal 2025 and -19% in the first half of the current fiscal year 2026.

  • Most publicly-traded cybersecurity companies are larger, more mature and more profitable businesses than Netskope. Average margins for the group are in the low-20% range, but growth is lower. As Netskope continues to scale it should realize considerable operating margin leverage.

• Free cashflow margin: Netskope’s free cashflow margins are also negative, with -28% non-GAAP operating margins in fiscal 2025, and -1% in the first half of the current fiscal year 2026.

  • Similar to operating margins, Netskope’s FCF margin from 2024 puts it at the bottom of publicly traded cybersecurity companies.

• Rule of 40: Netskope’s Rule of 40 score is 31 in the most recent quarter (-1% FCF margins + 32% rev growth).

  • This puts it slightly below the median for cybersecurity stocks, which is 36 on the Rule of 40 score.

Additional key metrics:

• Customers. As of July-25, Netskope had 4,317 customers, up 21% Y/Y from 3,571 one year ago.

• Net dollar retention. 118% as of July-25, up from 113% one year ago.

• Gross dollar retention. 96% as of July-25, compared to 95% one year ago.

  • Note - Netskope does not include contraction in its gross dollar calculation. It does include churned accounts, but excluding contraction is unusual compared to what is considered ‘standard’.

• International presence: 46% of Netskope’s revenue comes from the US, while the remaining 54% comes from outside the US.

Market Opportunity and TAM

Netskope breaks down its TAM into distinct categories given the multiple markets that its SASE suite addresses. Netskope references IDC for the following market sizes.

• Security. $24.4 billion in 2024, projected to grow at a 15% CAGR to $43.2 billion in 2028.

  • Includes secure web gateway, Zero-Trust Network Access (ZTNA), information protection/DLP, and virtual client computing.

• Networking. $40.7 billion in 2024, projected to grow at a 18% CAGR to $78.4 billion by 2028.

  • Includes firewall, SD-WAN infrastructure, cloud & data center interconnects, 5G/4G enterprise wireless WAN, delivery platform (PaaS & software), segmentation, and IaaS networking.

• Analytics. $9.2 billion in 2024, projected to grow at a 17% CAGR to $17.1 billion by 2028.

  • Based on enterprise network observability, Tier-2 SOC analytics, and cloud-native XDR; Netskope addresses these with UEBA, Advanced Analytics, Netskope Cloud Exchange (NCE) integrations, and Digital Experience Management (DEM).

• AI Security. Netskope doesn’t provide a 2024 estimate, but frames it as a $30.8B opportunity in 2028.

  • Includes AI app access control, real-time user guidance around using AI, and data protection for AI usage.

Valuation

If you’ve made it this far, thank you for following along, let’s get to the fun part - what Netskope may be worth in the public markets.

Initial assumptions and estimates. Netskope’s revenue was $538M in calendar 2024 / fiscal year 2025, up 32.5% Y/Y. If we assume 30% growth in fiscal 2026 relative to last year, it would imply revenue of $700M this year. So far as of July this year, Netskope has delivered $328M of revenue, which would be 47% of our $700M annual revenue estimate. Last year, Netskope’s first fiscal half-year generated a similar 47% of full-year revenue, so we can assume our $700M estimate for this year is likely reasonable.

• Publicly traded software companies often trade on investors’ estimate for not this year’s revenue, but the following year. Let’s assume Netskope decelerates growth from 30% to 27% in calendar year 2026 / fiscal year 2027. Assuming 27% Y/Y growth next year on our $700M estimate for this year’s revenue would imply $889M in revenue for next year’s revenue.

Comping to public peers. According to our cybersecurity comp group above, provided by Wolfe Research, the median company trades at a 7x multiple on 2026 revenue. The median company is only expected to grow revenue in 2026 by 11%, however.

• With my estimates for Netskope to grow meaningfully above the median security stock over the next two years, I expect it will trade at a premium to the group. Cloudflare’s multiple of 26x 2026 revenue is likely too high, but recent IPO Rubrik at 13x and other high growers like Zscaler at 12x suggest a low-teens multiple on 2026 revenue may be reasonable.

Estimating Netskope’s valuation. Given the above, I expect Netskope to debut its IPO between 10-14x 2026 (fiscal-year 2027) revenue estimates, which I believe analysts will likely peg around $890M, and would imply a valuation between $9-12B. Given the recent demand for IPOs that we’ve seen with companies like Figma, there could be upside to that range. However, given the intense competitive landscape for network security and Netskope still being early on its path to profitability, I don’t expect the company to trade at a massive premium like Cloudflare.

Please note multiples are a short-hand for valuation. This is not my opinion of Netskope’s fair value, but rather a reflection of where multiples currently trade for cybersecurity stocks, and my view of where Netskope is likely to trade in the short-term.

Conclusion

Netskope is one of the defining cybersecurity companies of the cloud era. It embarks on its journey as a public company as a scaled category leader with a full security and networking product suite that can compete against the largest players in its category - Palo Alto Networks, Zscaler, Fortinet, and Cloudflare.

From here, the company’s mandate from the public markets will be straightforward: keep growth above 20%, expand profitability, and prove AI is a tailwind. That is the opportunity, as well as the test, for the company and its new shareholders.

Disclaimer: This analysis is for informational purposes only and is not investment advice.

Let’s start with GPT-5. It’s been a bit over a week since OpenAI rolled out their latest model. ChatGPT’s nearly one billion users were told they were going to get a single model that provided a unified AI experience rather than needing to toggle an alphabet soup of different model names in the ChatGPT menu. Fewer hallucinations, better intelligence.

Instead, people were underwhelmed. Everyone from Bloomberg to the TBPN guys to YouTubers gave it a shoulder shrug. Reddit was filled with threads of people complaining about OpenAI removing 4o, which people felt was a warmer, friendlier model than GPT-5. Then OpenAI quickly put 4o back into the menu of model options.

For all of the disappointment though, GPT-5 actually was impressive - particularly the router that chooses which AI model to use to answer the user’s question. As SemiAnalysis put it, the router is the release.

Looking at the bigger picture, the fact that we even have AI this advanced is incredible. I am firmly in the camp that even if AI progress halted with current models, society would have years of innovation and productivity gains as a result of embedding intelligence in every piece of software.

But real or not - society’s perception that we are shifting from step-function improvements in model quality/performance, like the jump from GPT-3 to GPT-4, to incremental improvements should be concerning to anyone who works in tech.

Why should it be concerning? Because we are in an AI-fueled bubble. And as is the case with all bubbles, the AI bubble is not just confined to the asset experiencing inflating valuations.

The stock market, consumer sentiment, and the broader economy are also being impacted by the AI bubble. As George Soros explains in the Alchemy of Finance with his reflexivity theory, stock prices impact consumer sentiment, which impacts economic fundamentals, which impact stock prices. It’s a reflexive loop.

When one piece of the loop breaks, it can cause a chain reaction. Reflexive on the way up, and the way down. What starts with a vibe shift can lead to an actual recession.

Which brings us back to AI this week.

The Code-Gen Come-Down

The growing chorus of AI criticism has not just been confined to OpenAI and GPT-5, but also Cursor.

Cursor is a tool used by developers to accelerate code generation, and is one of the leading AI-native application startups. Outside of the model companies, Cursor is arguably the most highly regarded startup of the AI cycle. Cursor and their competitors have seen revenue growth that is unparalleled in the startup world, with several surpassing $100M in ARR in just a few quarters.

Code-gen is the first killer application of AI.

The issue is OpenAI’s models power Cursor’s product, which results in Cursor paying high fees to OpenAI - so high that Cursor reportedly has negative gross margins. In other words, it costs Cursor more to provide their application to their customers than their customers pay for it. It’s like selling a dollar for 90 cents.

Over the last couple weeks, the code-gen startups that were previously AI darlings have come under intense scrutiny. Their gross margins and business structure have been loudly criticized - with some good humor. Chris Paik, a venture capitalist, even went so far as to say Cursor doesn’t have product-market-fit. Tell that to its investors that recently valued it at nearly $10B.

Culture Shock

The criticism of AI is beyond business models though. It has become cultural, both inside and outside of the tech industry.

A new phrase that is gaining popularity is “being oneshotted”. Technically, one-shot learning is an approach to training AI that enables models to learn based only on a single example.

“Being oneshotted” is now a phrase that describes what happens to people who talk to AI like it’s a person, or use it for their writing or thinking. They start to sound like ChatGPT.

An X account that goes by “spec” has garnered 30k+ followers for calling out famous people who seem to have been Oneshotted. The list includes Joe Rogan, the All-In guys, Travis Kalanick, the White House’s X account, Paul Graham, and many others.

I know this might seem like a niche tech joke.

But John Oliver just had an entire 30-minute segment on his show dedicated to AI slop and how it’s being used by social media companies to increase user engagement.

Nearly 3-years into the AI era kicked off by ChatGPT’s release at the end of 2022, society is wrestling with the impacts that AI’s adoption is starting to have on it.

Just like social media, which promised to bring people together, had the unintended consequence of deepening societal divisions, perhaps AI, which promised intelligence, is actually making us dumber.

Okay - that is too extreme.

But skill atrophy as a result of overuse of AI is a legitimate concern that has become mainstream. New academic studies have shown mixed results on how AI can negatively impact developer productivity and on students learning in schools. In an article titled AI’s great brain robbery — and how universities can fight back, prominent historian and academic Niall Ferguson didn’t even question these negative impacts from AI, but rather what society should do about it.

I believe all new technologies cause growing pains for societies that need to change as they get used to a new technology’s impact. Even an invention as seemingly simple as the bicycle led to a moral panic in Victorian society.

And in case I haven’t made it clear, I am very optimistic about AI’s impact on individuals and society. I’m aligned with Yann LeCun, Meta’s Chief AI Scientist, who said, “Amplifying human intelligence is the best thing we can do for humanity. It might have an effect on society similar to the appearance of the printing press in the 15th century... So maybe this is a new Renaissance we're going to see.”

But I am concerned about the vibe shift taking place with AI right now.

The scariest thing is actually much simpler than these philosophical debates over AI’s role in society - it’s OpenAI.

If OpenAI sneezes…

OpenAI is the barometer for the AI bubble that the economy is riding on right now.

Sam Altman talks about how it’s important that OpenAI stays a private company for now. He says how hard it would be for OpenAI to have their stock publicly listed, implying the volatility in the stock would be a distraction for the company and dampen their ability to build for the long-term.

The fact is OpenAI is under a microscope, so much so that it amounts to the same result as them being public. Let me explain.

OpenAI is part of a small cohort of privately-held companies (including Canva, Databricks, SpaceX, and others) whose shares are regularly exchanged in the form of secondary sales among private investors. The Information is writing about OpenAI’s revenue results on a nearly quarterly basis now, including two weeks ago when they reported OpenAI is at a $12B revenue run-rate and on track to beat its plan for $12.7B in revenue by the end of this year.

So the distinction between private and public - that a company’s financials are not disclosed publicly and its shares are not freely tradable - don’t exactly apply to OpenAI (**before any of my finance friends come at me - yes I’m aware the company still needs to sign off on share approvals and we have limited financials from them - you get my point).

If OpenAI underperforms expectations on revenue growth, or reduces those revenue projections, there is no doubt that will be a big news story. If the miss is bad enough, it could impact the valuation investors are assigning to the company and potentially their willingness to fund the “trillions of dollars” that Sam Altman wants OpenAI to spend on AI infrastructure.

Why? Because the implication of OpenAI slowing revenue growth would be that AI is less economically valuable than expected - either to consumers, businesses, or both.

That has risks to every major tech company - Meta, Google, Amazon, Tesla, etc. - which are telling investors about how the return on investment from AI justifies large volumes of capex spend.

Investors have looked past these companies spending hundreds of billions of dollars on data centers and AI infrastructure because they believe there is a pot of gold on the other side of the rainbow.

If that belief in the ROI of AI changes, these companies will come under massive pressure to cut capital expenditures or watch their stocks drop.

And if the mega-cap tech companies cut capex, who stands to lose the most? Nvidia, the most highly valued company in the world, which now accounts for 8% of the value in the S&P500 - a record high for the index since its launch in 1981. Nvidia is leading the stock market’s rally, and as its share price goes, so goes the stock market.

Let’s take a step back from forecasting an impending stock market crash. Do I really think the last few weeks’ vibe shift is indicative of AI actually being less economically productive than people realize, and therefore signaling an impending AI bust and recession? No, definitely not.

We are so early

We are still so early in rolling out AI across the economy. It is like society is in 1900 - the light bulb was invented 20 years earlier but widespread adoption is complex and expensive, so cities are being lit by kerosene lamps. Today, society is powered by software and digital innovation across not just technology but every sector - healthcare, manufacturing, transportation, energy, finance - yet the vast majority of that software is not intelligent. Kerosene lamps.

There are endless opportunities to upgrade, replace, and entirely rethink how old software can be deployed across the economy with AI-powered software.

Enterprise use cases have largely concentrated on code-gen, and yes for now those companies have questionable unit economics - but they are young companies, have large amounts of capital on their balance sheets, huge user bases, talented teams, and I believe a lot more innovation to come (and better unit economics). Beyond code-gen, there are thousands of startups being launched seeking to provide businesses with AI solutions to make them more efficient and productive.

On the consumer side, as many people including Yoni Rechtman, a venture capitalist, have pointed out - so many consumer applications from cookbooks and travel guides, to fitness applications and self-help programs - will be recreated with AI-native applications.

Below the application layer of specific use cases, we are still benefitting from innovation at the hardware layer.

Nvidia’s most advanced chips, the Blackwell line, are just starting to come online. Early benchmarks show big gains in training, which looks promising for when they are used to train a future flagship LLM like GPT-6.

Watch the vibes

So GPT-5 was a bit underwhelming. AI has shifted from novelty to mass adoption, and enough time has gone by that society is starting to feel its impact - both positively (innovation) and negatively (AI slop and skill atrophy). And as the dog days of summer 2025 wind down, it feels like there’s been a vibe shift in AI.

The good news is OpenAI, Anthropic, and the big tech companies are seeing incredible revenue growth that so far has outpaced everyone’s expectations. Enterprises are very early in embedding AI across their organizations to improve efficiency, and consequently profitability. Demand for GPU’s continues to far outstrip supply. Hardware innovation is continuing apace. There is a long runway of application innovation even on today’s models. AI will be used to make us healthier, wealthier, and - yes - more intelligent.

And we humans always overestimate technology’s impact in the short-term, and underestimate it in the long-term.

So there are many more reasons to be positive than not.

The future is bright but the path will be bumpy. Watch the vibes.

This is the question I’ve found myself discussing with founders and investors repeatedly this summer. If you work in the startup ecosystem and feel like the market for Series A’s is unpredictable right now, that’s because it is.

Some startups are landing large A’s with less-than-typical metrics, while others with better numbers are raising smaller rounds. Seeds are regularly clearing $20–25M valuations, yet the median A is still valued at ~$40–60M, and we’re seeing the emergence of a new tier of ~$100M+ A valuations.

In this post I explore what’s driving these changes at the Series A, and how software startups can adapt.

AI killed spreadsheet investing

Early-stage investors traditionally evaluate a potential startup investment based on three pillars: The founder, the market, and the product. Different venture investors have different ways of prioritizing these criteria. Metrics like growth and retention are a fourth pillar, if the startup has them.

The 2010-2021 software wave saw the standardization of investment patterns in startups. SaaS investors had clear visibility into markets due to established trends, well-defined competitive landscapes, and consistent business models like subscription pricing, predictable customer acquisition costs, and measurable retention metrics. This allowed investors to rely heavily on pattern matching, historical data, and well-defined market sizes in known categories of software. I wrote a bit about this from the founder’s perspective in my last blog post.

But in the last few years, AI has emerged as a defining technology that software investors firmly believe has the potential to disrupt existing categories of software, and create many new ones. Historical pattern matching, growth benchmarks, and market sizing exercises have become far less valuable, and in many cases have been thrown completely out the window. Victor Lazarte summed this up on the 20VC podcast as “spreadsheet investing is dead”.

All of this has led to a fundamental shift that many in the startup ecosystem haven’t internalized yet: A startup’s market has become the most important determinant of its ability to raise early-stage venture capital.

Markets & S-Curves

S-curves define technology adoption in new markets. When a new technology like the smartphone is released there is a gradual experimentation phase where early adopters begin to try it out. Then adoption takes off, with the technology becoming rapidly ingrained in society - the steep part of the S-curve. Finally growth plateaus as adoption becomes saturated and everyone has the technology. From the printing press to the internet, nearly every technology’s adoption follows an S-curve.

For investors and entrepreneurs, the most exciting part is the exponential part of the S-curve. ChatGPT has been experiencing this phase of growth for the last two years. This is when new markets are created. As Peter Thiel talks about in Zero to One, the greatest opportunity in starting a new business is taking part in the creation of a new market, like his famous investment in Facebook did in social media. Apple did this with smartphones, Microsoft with the personal computer, and Uber with ride sharing. When a company is aligned to the creation of a new market, it has the opportunity to escape competition and establish market dominance. If the market is large enough, this is where businesses with 100-Billion or 1-Trillion dollar valuations get created.

The above slide is from tech industry analyst Benedict Evans, from his AI Eats the World presentation. He represents the consensus view among investors, which is that AI is the next tech platform shift, and it will either be great for the economy, or the most economically productive technology of all time - either way, pretty good!

Suffice to say investors are excited. Every VC is on the hunt for this generation’s Uber or Facebook - the company that will define this tech transition. (Hint: The answer is OpenAI, but there will be others too).

That means for software investors, the questions they ask themselves when evaluating a potential investment are often AI questions: How will this startup’s market change because of AI? Will it grow faster because of AI, or get cannibalized by it? Will OpenAI, Anthropic and the foundation model companies enter this market? Does AI change customer expectations around what this product should be?

For founders starting AI companies, these questions matter at all stages, but where it matters most is at the Series A.

Series A’s, They Are A-Changin

At pre-seed and seed, investors bet on founders and the problems they aim to solve, even if the product specifics remain fuzzy. By the time a company gets to Series B and beyond, financial metrics play a larger role in assessing a potential investment - great companies can start to look obvious.

But most Series A’s represent a unique moment in a company’s journey. They (typically) happen only a few months or quarters after a startup reaches product-market-fit. A lot can still go wrong, and investors often have around a year of metrics to consider. Unless growth is explosive or the founder is already well known, then investors are going to focus on the company’s market, the market’s growth, and the company’s ability to be a leader in that market.

Simply put, at the Series A, investors want to see that a company has the potential to be a market leader. But not every company has the potential to be a category-leader, and as the chart show below, startups are taking longer to raise series A’s.

But that above chart is the median startup.

AI startups are a different story. They are raising Series A’s faster, and doing so with better metrics than investors are historically used to seeing at the Series A. Last month Andreessen Horowitz, one of the top venture capital firms, published a provocative blog post called What “Working” Means in the Era of AI Apps, which shows the current cohort of AI-native software companies are setting a new standard of expectations for pre-Series A growth.

For software companies, going from zero to $1M in ARR in the 12 months following a company’s first customer contract used to be considered the standard benchmark for what “good” looks like before raising a series A. Now, at least according to a16z, that is bottom quartile, and the median is zero to $2M in 12 months.

Takeaways for Startups

For companies at the Series A, there are increasingly two types of rounds.

• Standard Series A: $12-15M

  • Credible team, good metrics, and a clear market opportunity

• Mega Series A: $25M+

  • Strong team, strong metrics, and significant category-defining market opportunity catalyzed by AI

Let’s consider some of the anecdotes from Mega Series A’s so far this year:

• OpenRouter - $40M (Seed + A) led by a16z, Sequoia

  • AI middleware that enables developers to seamlessly use different LLMs in their application

• Campfire - $30M led by Accel

  • AI-driven ERP system

• Greptile - $30M led by Benchmark

  • AI-powered code review

• Wispr Flow - $30M led by Menlo

  • AI speech-to-text tool that allows users to talk into any app on their computer

• Listen Labs - $27M led by Sequoia

  • AI-powered voice surveys for market research

• Rillet - $25M led by Sequoia (just announced their Series B this week only 2 months after their Series A)

  • AI-driven ERP system

Assuming the typical 20% dilution, these are companies being valued well over $100M.

What Mega Series A’s all have in common is a very clear story around how their company has the potential to create and define an AI-native category of software. They can explain:

• Why Now: Why is this category inflecting or emerging because of AI right now?

• Budgets: How will you win spend from incumbents, or how will you create entirely new budget lines that fund your customer contracts?

• Competition: Will the foundation model providers enter this space, and if so how will you compete against them?

• Model Improvement: How does the march of better/faster/cheaper models make your product meaningfully better over time?

• Distribution: How will you build an initial customer base and then scale it over time?

• Proprietary Data Loops: How does user/customer data make your product experience better for customers over time

• Market Awareness/Pull: Is your target market actively searching for a solution, or will you need to educate and evangelize?

Raising a Series A for a software startup in 2025 is equally about great numbers as it is telling a great story that aligns the company to what many investors believe will be the biggest platform shift of their careers.

The numbers still matter, but the story is the difference maker.

The best Series A companies are making a clear, compelling case for why their category is being reinvented and why they’ll lead that reinvention. Software startups have to articulate how their market is being transformed by AI, positioning themselves ideally as the catalyst driving that change. Telling that story and finding investors who share in its vision has never been more important.

Looking at past tech cycles shows that disruption often follows similar patterns, and by learning from them, we might be able to better spot the innovations that change the future.

I was recently listening to a podcast with Eric Vishria, a general partner at Benchmark, and for my money one of the top venture capitalists on the planet. Vishria made a compelling observation of what drove success among software companies in the early 2000s in the beginning of the SaaS era:

“In the SaaS world, over and over again you had people who really understood the customer and the problem, and understood the domain and what the technology was capable of. But it was not a question of if you could build something…CRM existed before Salesforce…Salesforce followed Siebel, Workday followed Peoplesoft, ServiceNow followed Peregrine and others. So they were cloud/SaaS versions of the prior product, which was on-prem…They understood the customer problems and took advantage of the next wave…Most of the V1 SaaS companies, they weren’t so crazy - we run this product for you and make it better, web-based.” - Eric Vishria on The Peel with Turner Novak (link)

He makes two points:

1. Successful SaaS founders had deep domain expertise, understood their software market, and the problems customers faced.

2. They were able to leverage that expertise to port over products that were successful in the on-premise software era, rebuilding them as SaaS.

Vishria went on to contrast the type of founder that was successful at the beginning of the SaaS era, with the type of founder that is successful today in the beginning of the AI era.

“…product development happened in [in the SaaS] cycle…almost diametrically opposite of product development in the AI era. I look at the teams that are having the most success today, and they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when, and what they’re gonna be good at and what they are not gonna be good at. And they are spending their time on, how do I apply this model to this domain, or to this user? So they are working technology out rather than customer problem in. They are really close to the metal in terms of capability and are applying it out.” - Eric Vishria on The Peel with Turner Novak (link)

Vishria claims that the most successful AI founders today, rather than being domain experts, are experts in the AI models themselves.

Contrasting early SaaS founders as domain experts versus early AI founders as technology experts is a fascinating observation.

No doubt many of the early SaaS successes like Salesforce and Workday certainly match Vishria's two points. Their founders understood their category, had expertise from the on-prem era, and used that experience to build their SaaS company.

Yet other SaaS pioneers, like DocuSign, HubSpot, Unity, Akamai, or Olo - all of which were founded in 2006 or earlier, didn't neatly fit this pattern. Their products represented new market categories, and not all of their founders previously had narrow domain expertise or founding experience.

So, looking beyond these anecdotal examples - what does the data say? Do SaaS founders tend to be repeat entrepreneurs who possess deep domain expertise? Were SaaS ideas often replications of previous iterations of software in the on-prem era? And what can we learn from these questions as we enter a new era with AI?

Founder Backgrounds in the SaaS Era

To start to try to answer these questions, I pulled together a dataset of 90 publicly traded SaaS companies1, and tried to uncover common patterns in their founders’ backgrounds.

Surprisingly, the data suggests there is no clear pattern.

57% of the companies in the dataset were first-time founders, and 43% were second-time founders. 51% came from the same category of software in their previous job, either as a founder or not, and 49% came from a different category of software (or a different industry altogether).

I took the analysis a step further and added a time component. Do founders of a particular background become more or less prevalent as the SaaS wave continued over last 25 years?

Surprisingly again, there was no clear trend or shift over time.

• Correlation between founding year and second-time founders: 0.12

• Correlation between founding year and domain experience: 0.16

Remaking the old and inventing the new

What about the idea that it was more common in the beginning of the SaaS wave (2000-2006) to see companies that were a direct “port” of an existing on-premise software category?

For each of the 90 companies, I marked whether its core product was a direct SaaS re-creation of an existing on-prem category or represented an entirely new, cloud-native innovation. The results showed a moderate negative correlation (-0.3) between founding year and the likelihood of being a “ported” category.

In other words, the more recently the company was founded, the more likely its category is cloud-native, and did not exist prior to the SaaS era. As the SaaS wave matured, re-architected legacy categories gave way to entirely new, cloud-first innovations.

SaaS-native companies like Snowflake, MongoDB, Twilio, GitLab, ServiceTitan, Monday.com, UiPath, Samsara, and many others created product or technical innovations that created new categories of software. Even if they rhymed with previous generations of software, they were not recreations of on-prem products.

While a correlation of -0.3 is moderate, the trend reflects the evolution of SaaS from modernizing legacy on-prem tools to unlocking new use cases made possible by the cloud.

Which brings us to AI.

Implications for AI founders Today

There are interesting implications here for founders today building AI companies.

Similar to how early SaaS pioneers like Salesforce took established on-prem products (like CRM) and moved them to the cloud, today's AI startups are 'AI-ifying' existing SaaS products.

Companies like Day.ai are trying to replace Salesforce, building an AI-native CRM. Gamma is trying to displace Google Slides in slide deck creation, and Decagon and Sierra are trying to displace Zendesk in customer service software.

Some of these companies “AI-ifying” old software archetypes will work, and many won’t.

A big outstanding question is whether AI will prove to be a disruptive innovation or a sustaining innovation. In The Innovator’s Dilemma, Clayton Cristensen highlights how sustaining innovations can be weaponized by incumbents to sustain their leading market position, versus disruptive innovations which can empower startups to disrupt incumbents. The internet was a disruptive innovation, enabling Amazon to disrupt the retail industry and Google to disrupt the ad industry. Mobile was largely a sustaining innovation, leveraged by Google, Apple, and Meta to further entrench their competitive positions (though there were new disruptive players like Uber and AirBnB).

In the software industry, SaaS was a disruptive innovation because it changed the business model, delivery model, and pricing model of software - from up-front payments for software code on a server to a subscription for applications accessed via the web.

There is a strong argument to be made that for much of software, AI will be a sustaining innovation. The incumbents like Salesforce and ServiceNow are all over AI, making acquisitions and adding new AI products, and aggressively pushing them onto their customers. Today’s tech CEOs have all internalized the The Innovator’s Dilemma, and they don’t plan on losing market share.

Rather than focus on existing categories with incumbents, there is a more exciting opportunity for startups to think about new capabilities that AI models that will deliver, which can create entirely new categories of software. What industries lack software can benefit from AI? What new problems will exist in the future because of AI, that can be solved through new AI-enabled software?

Here are a few AI-native categories that I’m particularly excited about, with some example companies:

• AI-generated media - Synthesia, Midjourney, Pika, ElevenLabs

  • Disrupting traditional video production, visual effects, and graphic design by eliminating the need for film crews, professional actors, specialized VFX artists, and expensive studio tools

• Legal AI - Harvey, Legora, Coheso

  • Disrupting labor spend on junior lawyers, and software spend on legal project management tools

• AI-powered surveys - Qualitate, Listen Labs, Ethos

  • Disrupting expert networks like GLG, and survey software like Qualtrics

• Industry-focused AI note takers - Abridge, Rilla

  • Disrupting manual note taking, voice note recording, and industry-specific workflows

• AI-automated security event resolution - Cotool, Dropzone

  • Disrupting labor spend on junior security analysts focused on managing alerts and notifications

• AI-enabled recruiting platforms - Mercor, Paraform, Candix

  • Disrupting recruiting agencies and manual candidate sourcing

• AI-Powered Sales Enablement - Glean, Tribble, Docket

  • Disrupting traditional sales playbooks, manual research, and legacy enablement platforms

Like art, technology constantly builds on its own history, so even AI-native categories reflect patterns from earlier software eras. What sets them apart is that AI now enables entirely new solutions and experiences that simply weren’t possible with previous tools, expanding the boundaries of what software can do.

Many in tech have taken to Twitter/X to decry the deal, and what they view as an unfair outcome for the employees who may not be paid as part of the stock ownership they’ve earned in the company. I think this narrative is overblown - the employees will probably get a great payout commensurate to their vested equity ownership, and I expect that part of the story to come out in the coming days. I believe this because the opposite scenario, leaving their employees with nothing, would be an embarrassing abdication of leadership on the part of the management team.

Still, there is a strong case to be made that these IP licensing deals are not good for the tech ecosystem. Like them or not, they are likely here to stay, so anyone who works in the startup ecosystem - founders, employees, investors, LPs - needs to understand how these deals work.

There has been a lot written about the details of the deal, so rather than provide an exhaustive re-hashing, I’ll share how these deals work, the incentives on both sides of the table, explore why they are so controversial, and lay out a path forward for the industry.

The high-level details of the deal

Google’s announcement to strike an IP licensing deal with Windsurf comes after months of OpenAI being rumored to acquire the company. The OpenAI-Windsurf talks fell through because of OpenAI’s partnership with Microsoft, which gives Microsoft access to all of OpenAI’s technology. The competitive dynamic between Windsurf and Microsoft’s suite of software development tools, in particular GitHub, created angst on both sides that ultimately killed the deal.

Enter Google. The company announced it would be paying $2.4B to Windsurf to license its technology, hire the founders and key leaders, and pay out investors and employees. Employees who have unvested stock will not be receiving a payout, according to the most recent reporting.

Big tech’s new acquisition playbook

The Google-Windsurf deal follows a similar playbook that Google executed in their $2.7B deal with Character AI. Microsoft had a similar deal with Inflection for $650M. Amazon had deals with Adept AI for $1B and Covariant for $1B. And more recently, Meta just announced a similar deal with Scale AI for $14.3B.

There are nuances to each of these deals, but they represent a similar flavor of IP licensing agreement that has become the norm among big tech companies. They share a few traits in common:

• Startup maintains independence. The buyer doesn’t take any equity stake in the startup. The startup remains as an ongoing company with employees, shareholders, and a product it sells to its customers. All of the equity in the startup becomes owned by the employees, and the investors relinquish their equity in exchange for cash.

• Non-exclusive licensing. The buyer gets access to the core IP and technology from the startup on a non-exclusive basis. Non-exclusivity means the startup technically could be free to strike similar types of deals with other companies in the future. In other words, the startup still owns its product, and is free to do whatever it wants with it.

• Senior talent acquisition. The buyer hires key personnel, often the founders and senior technical leaders, to join as full-time employees. The startup appoints senior leaders who stay behind into C-suite positions, who can lead the company in whatever direction they see fit.

• Cash payouts. The buyer pays a lump sum to the startup, split across three key recipients:

  • 1) The founders and senior leaders who will go work at the buyer’s company,

  • 2) The investors in the startup who will receive cash in exchange for giving up the equity in their company

  • 3) The startup itself, which will retain that cash on its balance sheet and use it to fund future operations. That includes a dividend to shareholders, which are now the employees.

So, to sum it up in the case of the Google-Windsurf deal: Windsurf will remain an independent company, wholly owned by the employees who stay. Venture capitalists who funded Windsurf, such as Kleiner Perkins, will get a return on their investment and then no longer own stock in the company. Windsurf’s product will be licensed to Google, and Google will be able to build their own version of it to sell to their customers.

Dual incentives

Big tech’s M&A playbook is increasingly relying on these IP licensing deals over traditional acquisitions because they prioritize speed by evading regulatory scrutiny. A traditional acquisition is subject to government review by anti-trust regulators, who assess a deal’s potential to create a monopoly in the merged companies. If the regulators deem a merger or acquisition would create too much market power, they can block the deal.

IP licensing deals on the other hand, are structured as commercial agreements, with the startup continuing to operate as an independent company post-transaction. So, they are not acquisitions (wink wink).

In reality, these deals are absolutely acquisitions.

In an era of $100M salaries to top AI employees, the scarcest asset in the tech industry is talent. With these IP licensing deals, big tech gets access to a startup’s top employees and their core IP - without needing to deal with the headache of integrating dozens or hundreds of people.

While it may seem like the incentives are strong for the buyer, there are also strong incentives for the startup. The incentives are strongest for the founders and investors. They both get a meaningful cash payment, and in the case of the founders, a life-changing sum of money plus the opportunity to become a leader at a big tech company. They get to run the product they previously had built at their startup, but do it with the seemingly-limitless resources of a global tech company. For the investors, earning something between a good and great return on their investment, in a relatively short time period can also be a great deal. Especially if the company is number two in its category, the way Windsurf was to Cursor, the leader in the code generation space.

Where the incentives are weaker are for the startup’s employees. Depending on how the startup negotiates its terms, employees may or may not receive payouts for the stock they’ve been paid as part of their compensation.

Why these deals are so controversial

Silicon Valley and the broader tech ecosystem rely on trust. Since Arthur Rock wrote his Intel memo in 1968, the standard employee compensation package in the tech industry has included equity that creates shared upside for employees and management in the event of a successful exit.

In an acquisition - whether traditional or these IP licensing deals - it’s incumbent on the CEO of the startup to ensure their people are being taken care of in the deal. It’s unclear at this point to what degree the Windsurf employees are being taken care of, but we will likely find out in the coming days.

For many people though, the sight of a startup CEO decamping to a big tech company with a huge bonus and leaving the startup behind to go it alone, is the opposite of Silicon Valley’s in-this-together ethos.

In IP licensing deals, the startup’s core IP gets diluted because the big tech company can sell it to their customers. If Google’s customers can buy Windsurf from Google, bundled into their Google Cloud bill, why would anyone buy Windsurf from Windsurf? Plus, a startup without its founders can become strategically rudderless, especially before it has scaled to become a profitable and sustainable business. The morale hit from all of the above can kill a startup’s culture, and its ability to attract and retain talent.

So why don’t the startup’s founders just pay out their employees and shut down the company? The answer here is murky, but my guess is because claiming that the startup is going to continue on as an independent entity provides cover from government anti-trust scrutiny.

How tech can move forward

We are likely to see more of these IP licensing deals in the future given how rapidly AI is upending the competitive dynamics in the tech industry. The tech industry needs a plan moving forward.

Understand why this is happening. AI - it poses both the greatest existential risk and the biggest value-creation opportunity for every big tech company. This dynamic is compelling them to act aggressively and decisively to advance their business interests and stave off competitive threats. In the era of AI, big tech companies need two things - speed, and AI talent. Structuring deals as IP licensing agreements rather than traditional acquisitions solves for both, as big tech gets the AI talent without the government roadblocks.

Don’t blame the government. Many people are reflexively blaming the government’s anti-trust policy, which in some cases like Adobe/Figma as creating incentives for companies to skirt the M&A process with these IP-licensing deals. This misses the bigger picture. Yes, anti-trust policy has been unnecessarily heavy-handed in some cases, and the rhetoric from both political parties against big tech has worsened the problem. The fact is these deals are happening because of big tech’s need for speed + AI. Robust anti-trust policy is a fundamental part of a healthy capitalist economy. Big tech should engage policymakers to update antitrust laws to allow for rapid anti-trust resolution in emerging categories like code gen.

CEOs need to be held accountable to take care of their people. In any acquisition, the CEO has ways of ensuring their employees get compensated for their equity, or not. These IP licensing deals do not implicitly favor the management team over the employees any more than a typical acquisition. It is incumbent on the CEO to take care of their people, and in cases where they don’t, they deserve the public criticism that they’ll receive. In the case of Windsurf, the CEO has taken a lot of barbs on Twitter/X, but it’s probably too soon to tell whether it’s actually deserved or not.

New compensation structures. Startup employees should push for compensation structures that result in fair payouts in the event of an IP-licensing deal. These can include acceleration clauses or "change of control" provisions that trigger immediate vesting or cash equivalents for equity holders in an IP licensing or talent-acquisition scenario. Fair outcomes for employees shouldn’t rely on the threat of public scrutiny of a CEO’s actions in these deals.

These IP licensing deals are likely to become more common, as they enable big tech companies to move quickly, hire top-tier AI talent, and avoid getting blocked by regulators. That is, until the government recognizes that these deals are acquisitions and starts looking into them. In the meantime, IP licensing deals risk undermining the core incentives of shared success that have made Silicon Valley attract ambitious talent willing to take the risks inherent in early-stage startups. Protecting that is more than just about fairness, it’s about protecting the innovative edge that moves the tech industry forward.

Many people outside of the design world remember Figma for the $20B acquisition attempt that Adobe made in 2022 to buy the company. $20B was a huge price for Figma, which at the time was reported to have roughly $400M in annual revenue. It was a clear admission by Adobe, the incumbent in creative software, that Figma had established a leadership position in collaborative design software. After regulatory hurdles led to the eventual breakup of the deal, Adobe was forced to pay a $1B break-up fee to Figma, which would continue on as an independent company.

In my opinion, Figma staying independent was a great outcome for the software industry, as Figma is a truly innovative business led by an incredible founder and CEO, Dylan Field, who now has the opportunity to take Figma to the next level by building a full suite of AI-powered software tools that address a variety of creativity and productivity related use cases beyond design.

On July 1, Figma filed their S-1, a legal statement that a company makes to the US Securities and Exchange Commission before they intend to list their stock on an exchange and sell shares to the public. S-1’s are jam-packed with tons of information about a company’s business. We are going to cover Figma’s founding story, product portfolio, go-to-market strategy, metrics, and more - plus where I think Figma will likely trade when it goes public.

Let’s dive in.

Founding Story

Figma was founded by Dylan Field and Evan Wallace in 2012. The two met at Brown University, and their initial vision was to move design to the browser, making design inherently collaborative and accessible to anyone involved in building software.

Design tools are software systems used by designers, product managers, illustrators, marketers, and front-end software developers. They can be used to design websites and applications, create specs for how a user experience should look and feel, or create visual assets that can be used in marketing and advertising campaigns.

Historically, design tools like Adobe and Sketch were downloaded to a user’s computer and used as a desktop application. That created inherent siloes, where users would work alone on a file and hand it off to their teammates when they wanted their input.

The technological breakthrough for Figma was WebGL, a technology that enabled graphics to be rendered in a web browser by tapping into a computer’s GPU. By using WebGL, Figma offered users a desktop-like visual design experience in the browser. As a browser-based application, Figma was able to provide its users with the breakthrough of multiplayer functionality. Think of it like Google docs, for design.

The dramatic improvement in user experience led to rapid adoption after Figma’s initial product launch in 2015. By focusing on collaboration, accessibility, and a rich plugin ecosystem of over 10,000 community-built extensions, Figma has cultivated a vibrant user community, and positioned itself as a platform for all things design.

Product

Figma has grown from its initial Figma Design tool into a multi-product suite of creative tools.

• FigJam: A collaborative online whiteboard introduced in 2021 for brainstorming, ideation, and workshops. It has sticky notes, diagrams, and real-time ideation with playful, easy-to-use features. FigJam broadened Figma’s user base by engaging product managers, engineers, and others involved with early-stage collaboration.

• Figma Slides: A newer offering launched in 2024 that enables creation of slide decks with Figma’s signature multiplayer approach. It targets use cases in which teams can design presentation decks together, bridging the gap between design and traditional presentation tools.

• Figma Sites (CMS): Launched in 2025, Sites is a web content tool aimed at allowing teams to design and publish marketing webpages. Figma Sites lets users create on-brand marketing collateral and microsites easily, and expands the company into the no-code website design space, competing with tools like Webflow.

• Dev Mode: In 2023, Figma launched Dev Mode, providing engineers with a tailored view of design files, and makes hand-off from design to code more seamless by integrating developer needs directly into Figma.

• Figma Buzz: Figma Buzz is a tool for creating and sharing content such as social media assets, display ads, and more. Its targeted at marketing teams, directly positioning against template-driven design tools like Canva.

• Figma Make: Part of its AI-centric product push, Figma Make uses AI to allow users to write a prompt and generate a workable prototype, and potentially even a production-ready output.

• Figma Draw: Figma Draw is a dedicated product for detailed editing and product illustration.

Underscoring my point about Figma’s ability to innovate faster as an independent company rather than as a subsidiary of Adobe, Figma released four of the above products just this year. Figma Make, Draw, Buzz, and Sites were all released in 2025.

The arc of Figma’s product expansion over the last decade reflects its growing user base, starting with designers and expanding to marketers, developers, illustrators, and more.

Business Model

Figma has a classic freemium SaaS business model that leverages a product-led growth strategy. Figma offers packages sold as annual or monthly subscriptions, and prices its product on a per user (or per seat) basis. These recurring subscription fees are the primary source of Figma’s revenue.

Starting in March 2025, Figma made the interesting decision to change its product packaging. Rather than offer individual licenses on a per product basis, it offers different types of multi-product licenses (seats) that customers can pick from depending on their needs. Each of the below seat packages includes access to FigJam and Figma Slides.

• The Viewer seat allows users to view files and leave comments for free.

• The Collab seat gives access to FigJam and Figma Slides.

• The Content seat gives access to Figma Buzz, Figma Sites CMS, FigJam, and Figma Slides.

• The Dev seat gives access to Dev Mode, in addition to Figma Buzz, Figma Sites CMS, FigJam, and Figma Slides.

• The Full seat gives access to all of Figma Design, Figma Draw, Figma Make, Dev Mode, Figma Buzz, Figma Sites, FigJam, and Figma Slides.

Go-to-Market

Figma’s GTM strategy is reliant on an initial product-led sale, and is complemented by targeted enterprise sales for large accounts.

Users can start for free and become paying users as they want access to more features. As Figma’s customers increase their paid users, entire teams may adopt the product, at which point Figma can leverage its enterprise sales teams to create a standardized license agreement across the customer’s business.

In summary: viral free usage → team adoption → enterprise standardization. This is often referred to as a “land and expand” strategy in the software industry.

As Figma adds new products, including the four new products it released this year, it can increase monetization either by bundling them into higher-tier plans or potentially introducing new pricing/packaging (as it did in March, discussed above).

To support its bottom-up sales strategy, Figma has invested heavily in building a large, global user community. Config, Figma’s annual user conference, attracts thousands of attendees (8,500 this year), and Figma has local user meetups around the world. Figma’s community resources are often driven by users themselves, and they have created over 250,000 resources and more than 10,000 plugins that allow users to customize Figma for their workflows. Figma’s own content marketing efforts complement these grassroots initiatives, to help onboard and inspire new users.

Financials and SaaS Metrics

Figma has strong metrics, with a scaled revenue base that continues to grow efficiently. Below I compare some of Figma’s key financial metrics to a roughly 120 company universe of publicly-traded SaaS stocks.

• Revenue: In the most recent quarter, March-2025, Figma reported $228M of revenue, up from $156M the year prior (+46% Y/Y). Annualized, this would be $912M. For the full year 2024, Figma reported $749M, up 48% Y/Y.

  • Figma would be the fastest growing company in the publicly-traded software universe, and the only company with above 40% revenue growth. In terms of size of revenue base, at $749M in 2024 this puts Figma in the bottom half of publicly-traded SaaS companies, with roughly 80 companies that revenue above Figma’s $749M.

• Gross margins: Figma has best-in-class gross margins of 92% on a non-GAAP basis, and 88% on a GAAP basis, for the full year 2024.

  • Figma’s 92% non-GAAP gross margins would make it #4 on the SaaS charts, behind Doximity, Autodesk, Ansys, and GitLab.

• Operating margins: Figma’s operating margins are also strong, with 17% non-GAAP operating margins in 2024. GAAP operating margins were -117% in 2024, primarily due to stock-based compensation.

  • With 17% operating margins in 2024, is right around the middle of the pack of publicly-traded software companies, with about 50 companies below it.

• Free cashflow margin: Figma’s reported adjusted free cashflow margin was 24% in 2024, up from 18% in 2023. In the March-2025 quarter, Figma reported 41% free cashflow margins.

  • Similar to operating margins, Figma’s adjusted FCF margin from 2024 puts it in the middle of publicly traded SaaS companies. However, if it can maintain the 40% range delivered in 1Q25, this would put Figma in the top 5 SaaS companies by FCF margin, with the top 4 being Doximity, Applovin, Palantir, and Paychex.

• Rule of 40: Figma’s Rule of 40 score is 87 in the most recent quarter (41% FCF margins + 46% rev growth), and 72 in 2024 (24% FCF margins + 48% rev growth).

  • This puts it among the top Rule of 40 companies including Applovin, CyberArk, Monday.com, Snowflake, and Zscaler, which are 74 and above.

• Users. Figma has 13M monthly active users as of March-2025, two-thirds of which Figma claims are non-designers.

• Customers. Figma has 450,000 paid customers as of March-2025, including 10,517 customers paying it over $10k per year, and 963 customers paying Figma over $100k per year. Over 40 customers pay Figma over $1M per year.

  • The $10k+ ACV cohort below accounted for 64% of Figma’s revenue in the March-2025 quarter, while the $100k+ ACV cohort accounted for 37% of revenue.

  • 95% of the Forbes 500 use Figma, and 78% of the Forbes 2000 use Figma.

• Net dollar retention. Figma’s net dollar retention is 134%, which would be considered best-in-class among public SaaS companies.

  • However, Figma calculates its net dollar retention by taking the $10K ACV cohort of customers, calculating their revenue today, and comparing it to that group of customers’ revenue one year ago.

  • This is the opposite of the standard NDR definition, which would take a given year’s cohort of customer revenue, and compare the following year’s revenue from that same customer cohort (including both up-sell and churn).

  • This makes Figma’s net dollar retention metric more akin to a “gross bookings expansion” metric, as it does not include churn.

• Gross retention rate. Figma reports a 96% gross retention rate, which similar to the NDR discussion above is very impressive but requires some caveats.

  • Figma calculates gross retention similarly to its net dollar retention metric, by taking a given period’s revenue from customers with over $10k ACV, comparing that cohort’s revenue to the year prior after subtracting churned customers.

  • If Figma were to include its entire customer base, and look at down-sells as well, it would yield a meaningfully lower gross retention rate.

• International presence. 85% of Figma’s monthly active user base came from outside of the US in the March-2025 quarter, while 53% of revenue came from outside the US.

In the below charts I highlight some of the most interesting charts from the S-1 as it relates to metrics discussed above.

Market Opportunity and TAM

In its S-1, Figma positions itself at the center of a generational shift toward digital product design and collaboration.

Figma cites IDC data, claiming a $33B total addressable market encompassing the “global workforce engaged in digital product design”. This probably includes all spending on design and collaboration tools, across all user groups from designers to marketers to developers. Figma calls out that it sponsored the IDC study it references in calculating its TAM, titled “Global Workforce Engaged in Software Design Expands to 144 Million by 2029”.

It’s always difficult in calculating an exact TAM for global software companies like Figma. What I often consider more important than absolute TAM size is what are the tailwinds or headwinds effecting the market’s growth. In the case of Figma it has a number of key market opportunities in its favor:

• Digital Transformation: Every industry is building digital products (websites, mobile apps, internal tools), driving demand for design tooling.

• Collaboration/Remote Work: Global and remote teams require cloud-based, real-time collaboration platforms.

• Legacy Displacement: Figma’s modern collaborative design approach is displacing legacy incumbents like Adobe and Sketch.

• Cross-selling: Figma’s expansion into whiteboarding, presentations, and web development, among other categories, place it into very large software markets in the productivity category.

• Up-selling: Figma has a massive expansion opportunity within its existing customer base. 78% of Forbes Global 2000 companies have Figma users, but only 24% of them spend >$100k with Figma. 76% of customers use 2+ products, and with Figma releasing 4 new products this year alone, there will be ample opportunity to deepen existing customer relationships.

Valuation

If you’ve made it this far, this might be the part you are most interested in, so let’s dive into Figma’s valuation.

Initial assumptions and estimates. Figma’s revenue was $749M in 2024, up 48% Y/Y. If we assume 40% growth in 2025 relative to 2024, that would place Figma’s 2025 revenue at $1.05B. It has already achieved $228M in 1Q25 revenue (+46% Y/Y), which would be 22% of our $1.05B full year revenue estimate, which compares similarly to the 21% that 1Q24 represented for the full year 2024. So our $1.05B revenue estimate for 2025 seems reasonable.

• Publicly traded SaaS companies often trade on investors’ estimate for not this year’s revenue, but the following year. Let’s assume Figma can again achieve 40% revenue growth in 2026, which would be $1.47B up from our $1.05B estimate. Assuming it grows 40% in 2026 may be aggressive, but our 2025 40% growth estimate may prove to be conservative. So even if the numbers differ slightly from year to year, the ~$1.5B revenue estimate for 2026 may be reasonable.

Comping to public peers. According to Wolfe Research, there are 13 publicly traded SaaS companies expected to grow over 20% in 2025. This cohort includes Palantir, CyberArk, Monday.com, Klaviyo, Cloudflare, Snowflake, Samsara, GitLab, SentinelOne, Zscaler, Crowdstrike, DataDog, and OneStream. This group trades at a 16.5x average revenue multiple on 2026 revenue estimates, and an 11.6x median multiple. Palantir, which trades at 66x 2026 revenue estimates, dramatically pulls up the average for the group. The second highest 2026 revenue multiple for the group is Cloudflare, at 25x.

• With Figma’s revenue growth last year of 48% and 1Q25 revenue growth of 46%, it easily clears the fastest growing public SaaS company, Palantir, which is expected to grow 36% this year. That said, Palantir’s 66x 2026 revenue multiple is in meme stock territory, and I don’t expect Figma to price there. That said, I do expect Figma to get a meaningful premium to the 11.6x median. Figma has meaningful better growth, gross margins, and rule of 40 scores than Cloudflare and Crowstrike, which at 25x and 21x 2026 revenue estimates are the #2 and #3 highest revenue multiple SaaS stocks (per Wolfe Research).

Estimating Figma’s valuation. Given the above, I expect Figma to debut its IPO between 12-18x 2026 revenue estimates, which I believe analysts will likely peg around $1.5B, and would imply a valuation between $22-27B. I expect in the days/weeks after the IPO it’ll likely trade up to 20-25x 2026 revenue estimates, which would give it a $30-37.5B valuation range.

• Please note these are quite frothy multiples, indicative of the current stock market environment that is bidding up tech stocks relentlessly. This is not my opinion of Figma’s fair value, but rather a reflection of where multiples currently trade for high quality software companies like Figma.

Conclusion

People often talk of category-defining companies. Figma is one of the truly category-defining software businesses of the SaaS era.

Now that we have entered a new software era of AI, Figma has an opportunity to redefine itself from a design software company to an AI-powered creative suite. It’s very exciting that the company is choosing to embark on that journey in the public markets. Go Figma!

Disclaimer: This analysis is for informational purposes only and is not investment advice. Please do your own research.