A Trojan Horse for Change

A former trader decides to start a company and build software for other professional investors. His startup marches promptly to $100mn in revenue and sustains double-digit growth for years. It has a long future, and has already changed the market forever. 

The startup creates a new type of synthesis layer—an aggregation of systematic channels, unstructured documents, and proprietary sources. It automates analysts’ undifferentiated heavy lifting. No more browsing long reports or chasing esoteric data. And the product is much better than those of established competitors, whose tools and data are hopelessly siloed. It’s more interactive and plugs into diligence, dealmaking, and trading. Tedious tasks shrink from hours to seconds. Firms make pricing decisions with a speed and completeness never before possible. In time, these improvements to the old ways enable new investing strategies and new types of investment firms.

This is what the Bloomberg Terminal did in the bond and public equity markets in the 1980s. And it foreshadows why we’re underestimating how AI will change private markets. 

Bob Hamill understands how technology transforms investing. He’s spent nearly 35 years leading investment teams at some of Wall Street’s most well-known firms—Drexel Burnham Lambert, Citi, J.P. Morgan, Lehman Brothers, and Jefferies. In this time, he successfully navigated the rise of the terminal, electronic trading, and the internet. Over lunch, he told us how those shifts ultimately reshaped the market in surprising ways.

In 1984, the year the Bloomberg Terminal launched, Hamill was just starting his career in investment banking at EF Hutton. He vividly remembers how work was done with paper spreadsheets, white-out, and the HP 12c, a checkbook-sized calculator that could display 10 digits. Lucky teams shared a single Quotron, an electronic version of a ticker tape machine. “You would fill out the spreadsheets and do everything by hand,” he explained. “You would take it to the word processing department and a couple hours later it would come out. And the key thing is you had to be proofreading like crazy because they were literally entering the stuff in. Your desk had a ton of pens and pages of paper and white-out and erasers. No TVs, no screens.”

striegel, Minderbinder

The Quotron, HP 12c calculator. Not pictured: the pencil and paper required to finalize trade calculations.

Bloomberg meant alpha for early adopters. It allowed traders to access real-time data on their investments and quickly compute trade information, all using the same platform. It easily surpassed existing bond math calculators by summarizing extra information like a bond’s terms, company description, and holder list. This conferred an edge over traders relying on manual workflows and fragmented systems. “You didn’t need to calculate bond prices or look at prospectuses. You pretty much took the summary information for granted,” Hamill explained. “Getting that Bloomberg box was pretty cool. It was a status symbol.” 

These advantages quickly became table stakes. By 1986, Bloomberg was selling 10x more terminals than in 1984. By 1996, it was selling 10x more than that^{[1] [2]}. Early-adopter alpha vanished.

In the end, Bloomberg’s knowledge-work automation was a Trojan horse for far greater change. It made markets more competitive, but also enabled new types of investing, like programmatic, quantitative strategies and technology-native investment firms. Hamill also explained that it helped facilitate the rise of indexing, noting that “there was basically no passive money in the late 80s, early 90s.” Jack Bogle deserves credit for creating the investor demand, but it was Bloomberg that made it cheap to build, rebalance, and distribute indexes like the iconic Lehman Aggregate (created in 1986, now owned by Bloomberg itself). Today, almost half of US investment firm holdings are in indexes.

TOM TRACY PHOTOGRAPHY

A 1980s bond trading floor. Find paper spreadsheets, newspapers, spiral-bound pitch books, Monroe bond math calculators, Quotron monitors, and phone banks for direct lines.

Bloomberg was a key catalyst for spiraling changes in the bond and equity markets. We think AI will drive a similar pattern of change in private equity and venture capital today. Firms working against the backdrop of more crowded and competitive markets will readily adopt AI for operational efficiency. As this technology becomes ubiquitous, it will create opportunities to invest in completely new ways.

In a world where every private markets investor relies on AI, we believe that the enduring moats of relationships, data, and operational excellence will matter even more. Cheap data aggregation means only truly proprietary data will provide an edge. Democratized information means firms need unparalleled access and genuine trust to win the best deals. Maximizing operating leverage requires systems that can adjust to conditions in real time.

We explore these themes through conversations with the best founders building in this space today.

A $100bn Prize for AI in Finance

The artificial intelligence stack for investors is gaining momentum, fast. 

Gabe Stengel is the Founder of Rogo, an AI-powered financial research platform established in 2021 for deal teams in public and private markets. It’s part of a wave of new companies changing how investors operate, and Stengel has seen the rapid embrace of AI for everything, from memo summarization to sourcing automation, up close. He thinks the biggest impacts of AI are yet to be felt. 

“The things that would have required a special financial workflow two years ago, like benchmarking or comps, are now able to be done by systems like Rogo.” Eventually, “we’re going to do everything. We’re going to do the modeling. We’re going to do investment committee memos. We’re going to write that outbound email for you. We’re going to update your CRM when you speak with a company. Really, we’re going to replace and augment everything you do.”

Startup activity supports Stengel’s take that even more transformative use cases for AI will emerge in the coming years. Early-stage investors are jockeying for exposure—in the past year alone, VCs have put over $2.5bn into 100+ US startups building tooling for investment firms. This is still a relatively small share of overall VC funding, representing less than 1% of the nearly $400bn deployed in 2024, but is 5x more than the pre-LLM baseline. Nearly half of these companies are currently positioning themselves as AI companies, and many more incorporate AI into their product in some form.

Taken together, the startups being funded cover virtually every part of a firm’s operations—a new, AI-powered stack for investors. The products they are building allow investors to integrate internal data to accelerate deal sourcing, due diligence, and internal communications. They are often competing with incumbents—S&P Global, SS&C, and more—that are also racing to deploy generative AI in their product offerings.

Many firms we’ve met aren’t waiting around for products to come to market; they’ve developed their own platforms on top of models from the major providers. These internal platforms range from organizational and summarization tools to end-to-end investment diligence applications. Some of these efforts have already been spun out—Seattle-based Finpilot traces its roots to internal tools built at Euclidean Technologies, a quantitative investment firm. There is precedent for this motion: BlackRock’s Aladdin^® started as an internal risk management software project, and is now the platform of choice for the world’s largest asset managers.

We don’t see these trends slowing down. There’s still ample white space for companies and firms to build products, including in ways that slice the market differently than the traditional operational verticals we highlighted in our market map. For example, paid expert network calls are too expensive for many of the smaller firms we’ve met. However, voice AI has made tremendous strides over the past few years, and is approaching quality and latency indistinguishable from actual humans. A future expert network product could use AI across the entire product surface—to ingest requirements from firms, source the right experts, conduct calls at the level of quality of an analyst-mediated conversation, and synthesize results. In doing so, it could significantly lower the overhead and cost required to access expert insights. Startups like Listen Labs and Bridgetown Research are ones to watch in this space.

Another challenge we expect AI products to attack is that investor telemetry is underutilized in private markets like venture and private equity. Pitchbook and Cambridge Associates publish data on capital flows, vertical-specific funding dynamics, investor hiring, and manager performance, but are largely oriented around looking up specific data or high-level quarterly reports of aggregate statistics. We think that AI presents an opportunity for products that help investors tailor reference points to understand their performance across many dimensions. They could contextualize their deal flow within broader capital flow patterns to understand whether they are early or late to funding trends. The startup Vantager is attacking this from the limited partner side, using AI to normalize general partner reporting and provide reference sets to help LPs diligence managers.

We also see an opportunity for products that enable firms to get more out of their own data. The principal shortcoming we’ve experienced with off-the-shelf tools like Gemini or OpenAI Deep Research is that we can’t inject our proprietary data (often the most valuable and insightful) into the tools’ analyses. A product that allows firms to easily integrate and structure their disparate data—like decks, emails, and call transcripts—with minimal friction could supercharge research by making all the firm’s data legible to AI.

Labor automation is the wedge

While the emerging AI stack for investors covers a laundry list of investing firm processes, every product in it ultimately leverages AI for the same task: aggregation and transformation of data from one format to another. Think extraction of financial data from an earnings report transcript into an Excel workbook, the automatic identification and replacement of the ‘usual’ clauses during redlining, or summarization of a company’s data room during diligence. In all cases, humans are being replaced as the translation layer between documents and data. Most of these tasks have historically been impossible to automate with rigid, rules-based software, and manual work was the only viable solution. 

As such, the immediate prize these companies are chasing is the pool of labor largely populated by highly trained professionals like analysts, associates, lawyers, and operations staff. This is a large market, even when crudely measured via direct labor costs. There are over 450,000 financial analysts in the US alone, representing a ~$100bn+ labor TAM.^[3] This number increases into the hundreds of billions globally, especially when supporting infrastructure and other roles are included in the tally. 

We don’t think the full value of the market can be consumed by AI, but we do believe AI tooling can capture a significant portion of it. For most investment diligence workflows, upstream work to find and prepare data, and downstream work to synthesize and report results, is often as time consuming as any analysis. Automating these things frees more time for investment teams or operations staff to focus on the places that demand intuition and creativity. 

We’ve seen the benefits of this ourselves. At Positive Sum, we’ve built an in-house AI research platform called Hubble, and use it to accelerate our understanding of markets and companies. Hubble has full deal context—it automatically aggregates everything from our external sources, call transcripts, company decks, proprietary data, and internal discussions. This up-to-date context means the system is a valuable copilot across the end-to-end investment process: we use it to generate subject-matter primers ahead of our first call with a founder, flag risks in a company deck, measure how our firm’s perception of a company changes over time, and even draft full investment memos. Some of these tasks, when offloaded to Hubble, save up to eight hours of rote work, and over the course of a typical diligence process, Hubble can save days’ worth of effort. 

Private markets are the last mile

The $20tn managed by private markets investors—especially in venture and private equity—will arguably be the biggest beneficiary of this new way of operating. These firms run on varied documents, not orderly data feeds. Virtual data rooms are stocked with lengthy PDFs and slide decks. Bespoke spreadsheets are the currency of the realm. There’s no Sarbanes-Oxley driving regular and standardized reporting for firms to consume. 

There are good reasons why the current status quo exists. Historically, it simply didn’t make sense to invest in automation because of relatively low deal volume. Transactions are typically between a company’s owners and investors, perhaps through an investment bank. A PE or VC firm might ‘trade’ a dozen times a year and hold an asset for a decade; many hedge funds trade a dozen times per second. These deals are also relatively large—measured in the millions or billions of dollars. Deploying human capital to carry out diligence for these deals is a worthwhile investment. 

“Private equity is the last mile” for investment workflow automation, Techstars-backed Founder Lydia Ofori, CFA, CAIA says. She’s spent two decades working across both private and public markets investment firms, deploying a wide range of strategies, and has seen both the opportunities—and challenges—for automation up close. Now, her startup Plainr aims to let private markets investors get more out of their internal and external data via AI. Ofori sees the opportunity as a lattice of new models, first principles engineering, and a shift from static, manual processes to live ones. “I do believe that if a modicum of these things is achieved, the identification of alpha in a deal would happen faster, which in turn makes it possible for capital to move faster and consequently for private markets to expand.”

Rogo’s Founder, Stengel, agrees. “There’s more value locked up in sub-$50mn enterprise-value SMBs than people realize. And those businesses are probably systematically undervalued, and there’s a huge, huge portion of the US economy in this long tail of smaller businesses … I think there’s a lot of attractive private market assets, but there’s not enough brain power and horsepower toward actually looking into them.”

Now, the last mile of private markets is traversable. As the cost of admission to AI automation has dropped precipitously, the calculus around automation vs. manual labor has shifted. At the same time, firms are facing pressure to compete in increasingly crowded and competitive venture and private equity markets.

One of the most durable trends in private markets over the past decade has been growth in AUM. Assets managed by GPs are headed to $24 trillion by the end of the decade,^[4] up from $19 trillion today and under $10 trillion in 2018.^[5] There were 3x more PE and VC firms in 2022 than in 2010.^[6]

The primary reason, of course, is return potential. Over the past 30 years, US buyout funds have consistently returned low double-digit percentages annually. The top quartile of funds have returned closer to 20%.^[7] Venture capital returns were even more impressive, breaking 30% annual return on a 25-year basis in 2021.^[8] Over the same time horizon, the S&P 500 averaged around 9%. 

Jack Lynch is the VP Strategy at Ridgeline, a cloud-native asset management platform designed for the AI era. He covers financial markets, software, and AI on his Substack, Reading Ambitiously. We sat down with him to get his thoughts on where the market is headed. “In the 1990s, it was index funds and ETFs that rewrote the rules. Active gave way to passive,” Jack told us. “This time, the lines aren’t between active and passive. They’re between public and private. Between liquid and illiquid. Between price discovery and opacity.” Now, “active management hasn’t disappeared—it’s just moved upstream. It’s private equity, private credit, structured solutions. These managers are underwriting complexity, building companies, and shaping outcomes in ways public markets no longer reward.”

It has become abundantly clear that competition for the best deals is increasing up and down the deal stack. Private markets AUM has grown faster than the number of available, high-quality deals. In venture, median seed valuations have increased by 60% since 2020.^[9] Over the past 15 years, global buyout EBITDA entry multiples have gone from 7.6x to 11.9x and all but the top quartile of buyout funds have seen annualized IRR decline by around 15%.^{[10] [7]} A PE shift to mid-market may offer a temporary reprieve, but an investor at a mid-market buyout firm explained that “even at this level, we’re seeing some increased competition or increased market efficiency, and now founders are thinking, or have heard, that they should call a banker. I think we’re probably only five years away from the lower middle market getting way more competitive and banked.”

It is now becoming increasingly difficult for firms to find and win the best deals. Dry powder as a fraction of AUM in both venture and PE has steadily increased.^[11] As the margin for error in delivering benchmark-beating returns has narrowed, firms are spending more resources on diligence. The number of days the average deal has spent in due diligence has increased by 30% over the past five years.^[12] At the same time, the pressure to reach conviction quickly is ratcheting up thanks to competing capital aggressively hunting for deals. 

Today’s Alpha, Tomorrow’s Infra

Increased competition is pressuring firms to improve the speed, breadth, and efficiency of diligence and operations, and AI presents a compelling solution. Every firm can benefit from automating the undifferentiated heavy lifting currently delegated to humans.^[13] “For 30 years, enterprise software has chased one promise: work should move itself… AI agents stand to finally finish the job,” writes Lynch. Consequently, nearly 70% of startups building private markets AI tools highlight time savings on their homepages—in the form of fast data aggregation, analyst efficiency, or real-time data feeds. 

For 30 years, enterprise software has chased one promise: work should move itself… AI agents stand to finally finish the job.

—Jack Lynch, Ridgeline

The earliest AI adopters will gain an edge over their peers as they realize the benefits AI offers, like faster pricing backed by a broader set of data.^[14] For the next few years, investment firms with the most aggressive and innovative AI strategies have an opportunity to defend their returns even as private markets AUM continues to grow. Consultants tend to focus on enumerating these near-term advantages of AI adoption.^{[15] [16] [17]}

But as more firms incorporate AI into their workflows, edge will get diluted. First-mover advantages will be ephemeral. Scraping Form Ds as they are posted to identify companies raising new funding can give a sourcing edge, but only until other firms adopt the same approach. Flagging documents in a virtual data room that require immediate attention can help a team reach conviction faster than others, but only if the other data room viewers aren’t doing the same thing. In the long run, this will accelerate competition for many corners of PE and venture. Process automation is not process power. 

Unless firms adapt to this world, it will once again become harder for them to reliably capture alpha associated with the best deals. This will be particularly true in growth venture and large-cap PE, where there are fewer overall deals, more external signals of traction, and greater transparency into a company’s operations. However, even the earliest stages of investing won’t be immune from this shift—venture investors are already pushing the price of the hottest seed-stage deals up, and new tools like Harmonic or Otto promise to make it easier than ever to identify those deals earlier.

It’s even plausible to imagine that as AI becomes ubiquitous, some pricing inefficiencies may go away. Rogo’s Stengel pointed especially at smaller deals, where heavy diligence processes don’t make economic sense. “I do think AI will result in being able to price risk more appropriately. The more transactions there are, the more data you have, the savvier folks can be. This will only increase across all of these different asset classes as AI is able to help you create downstream data research and synthetic data over almost every type of business and asset.” 

Of course, the system is dynamic—firms are unlikely to sit on their hands and watch yields go down. Jack Lynch at Ridgeline emphasized that adaptation by the best firms will be how returns are preserved. “I do think that price discovery will be easier,” in a world where AI is at the core of every firm. However, he was skeptical that returns would be systematically pressured. The data supports this—while entry multiples for buyouts have crept up over the past 15 years, and returns have compressed since the early 2000s, the top quartile of firms have preserved their ability to deliver benchmark-beating returns. And, as competition in the large-cap buyout space has increased, firms have shifted focus to the less competitive mid-market—KKR raised $4.6bn for its inaugural mid-market fund in 2024.^[18]

Regardless, the transformation of AI from alpha to infra in private markets is already underway. A third of asset managers have a full-scale generative AI use case deployed today. According to BCG, this could rise to as high as 95% within two years.^[19] Sid Masson, Founder of Wokelo AI, an AI-powered due diligence platform, expects that the vast majority of venture firms will depend on AI in their investment operations within the next few years. Masson thinks the PE adoption curve may be slower, but it is just as inevitable.

Enduring Moats, New Strategies

So what happens next? Is the lasting impact of AI just task automation and more competition for the best deals? Perhaps even a slow asymptote toward public benchmark returns as yields are pressured? How will firms consistently generate alpha? 

Through our conversations, we’ve come to believe that there will be two broad sources of investment edge:

• Traditional advantages. Successful firms will continue to rely on differentiated access, proprietary data, and operational leverage. AI will sharpen what these things mean, and investors today need to deliberately adjust their strategies to build moats around them.
• Emergent strategies. The widespread adoption of increasingly sophisticated AI across all parts of a firm will create a novel set of ingredients. This generates the opportunity for creative investment strategies that are impossible today. 

Traditional differentiators will intensify

1. Access and trust

In an era where AI is embedded in every PE and venture firm, we believe even more dealmaking power will accrue to the investors that can develop a unique, repeatable motion to gain access to the best deals. This access can be built on top of firm reputation and brand, personal relationships, or operational value-adds. Ultimately, these things all tie back to trust—trust that an investor will be a great partner during inevitable difficulties, trust that a particular firm’s backing will accelerate value creation, or trust that a firm will be a responsible steward of a business. This trust is not fungible, and can’t be replaced by AI.^[20]

AI will threaten the impersonal parts of sourcing. We spoke to one venture firm with several billion in AUM that’s built a data-driven sourcing platform to pull signals of startup traction from a broad swath of the internet, like social media, hiring patterns, and news articles. It then uses these signals to funnel ‘hot’ startups to a team of junior analysts who are tasked with establishing contact. This isn’t the only way the firm meets and invests in founders, but it provides each member of the sourcing team dozens, or even hundreds, of additional investment leads each week. 

Generative AI makes this kind of tooling significantly easier to build. During our research, we met multiple firms that have used generative AI to vibe code their way to various analogs. Examples we’ve seen include:

• A multistage firm automatically grabbing new Form Ds and flagging companies as actively raising. 
• An early-stage firm parsing inbound emails and enriching them with external data to build prioritized lists of potential investment leads.
• A seed stage firm that combines the backgrounds of founders with a high-level analysis of their ideas to identify promising pairings. 

Startups are providing these capabilities as a service, too. Otto, a personalized outreach platform that uses publicly available telemetry to automate lead identification and initial outbound counts over 100 PE funds as users. 

Hamill told us how this exact transformation played out in his career—after Bloomberg democratized market information, trust mattered more. In a world where other advantages were vanishing, cultivating personal relationships was an enduring way to maintain access to clients. After Bloomberg, he explained that entertainment budgets “actually went up, because dealers realized [that] now I need that customer to want to call me, instead of the other way around. All of a sudden, I need them to call me. Technology definitely drove that … As we got into a Bloomberg-driven world where everybody had all the information, then you were trying to get an edge by spending money to get the client to like you, to trust you.”

Just a block from South Park’s cluster of top VC firms, San Francisco’s iconic Caffe Centro (reopened in 2024) was where countless founder–investor relationships were forged over lattes.

As more and more firms adopt these low-cost tools, the ability of these tools to reliably deliver opportunities will get increasingly diluted. At some threshold, their primary contribution will be noise. A founder who receives one or two cold inbounds personalized with AI may reply; a founder who receives hundreds of such messages predicated on the same leading indicators will almost certainly not. This pattern has already played out in enterprise sales and hiring. As cold sales outreach at scale has become progressively easier and more personalized thanks to tools like Outreach or Salesloft, hit rates have fallen.^[21] LinkedIn made it dramatically easier for applicants to apply for roles, and a founder in the recruiting technology space we recently spoke with described a sharp decrease in callback rates as a result—and a tendency for recruiters to prioritize employee referrals. 

We think that adoption of automated, infinitely personalized sourcing tools in PE and VC over the next few years will lead to the same pattern. In turn, the ability of a firm or investor to create access that can’t be automated will become critical. Through this lens, firms that have already built this through a strong product or brand stand to increase their dominance. Y Combinator is the premier destination for early-stage SaaS founders. Sequoia or a16z on the cap table is a clear, positive signal for other investors. Thoma Bravo’s expertise in software has been cited in pre-emptive take-privates.^[22]

Firms without this, who try and make up the difference with volume, will likely find themselves shut out of the best opportunities.

2. Proprietary data

On the data side, the key will be to create truly proprietary data—and make it legible to AI. As with unique access, the power of differentiated data about markets, companies, or people should come as no surprise to any private (or public, for that matter) markets investor. The difference now is in what qualifies as proprietary.  

In the past, it has generally been forgivable to conflate ‘difficult to analyze’ with proprietary. Deal and valuation history, other investors, and headcount timelines are relatively easy to analyze thanks to tools like Pitchbook or FactSet. Social media mentions, policy documents, or online reviews require significant technical work to even get the data into a form that can be analyzed. Historically, analyzing this data was well beyond the scope of a small PE or VC firm’s capabilities. These barriers to entry can make an external dataset that adds marginal decision-making power something that only a handful of firms can contemplate using. 

AI changes this. As it becomes far more efficient and cheaper to aggregate and extract signals from data, it becomes much easier for firms to do so. As they do, the value of these signals to generate returns gets diluted. This shift is already underway. The major LLMs, tools like Hebbia, and databases like Harmonic, already enable most firms to access and utilize more data about markets or industries than they could have before. For example, one early-stage VC firm we spoke with uses AI to build a ‘primer’ on every company it meets. This primer aggregates funding data, creates descriptions of key market dynamics, and explains any comparable attempts by other startups. 

This pattern is reminiscent of the shift that bond traders went through during the late 1980s and early 1990s. At the time, the bond markets were paper-driven and opaque, which benefited firms that had the resources and networks to trawl the market for information. “There was a real asymmetry of information because the bigger the firm, the more information it had—just because of the network effect of people speaking to people,” Hamill explained. “The biggest dealers, Salomon Brothers, Goldman, etc. had that advantage and they used it ruthlessly. Drexel Burnham really had the advantage in the beginning because they sort of created a whole new market for junk bonds and made crazy amounts of money.” Now, “the bond market has become much more efficient. It’s as efficient as the stock market, I would say.”

What’s become clear in the market is that buyers want one tool that connects to their internal data and their external data.

—Gabe Stengel, Rogo

The easiest source of proprietary data is internal—deal and decision history, portfolio company reporting, and investment solicitations. These artifacts provide differentiated data points, but also encode a firm’s style and taste, allowing for even more tailored AI behavior. Unfortunately, it’s often very difficult to make these things visible to AI.^[23] This is a primary focus for companies like Rogo: “What’s become clear in the market is that buyers want one tool that connects to their internal data and their external data. And so, the incumbents like Alphasense are creating platforms to do this,” Stengel said. “Ultimately you need both internal and external data together, right? You don’t want to hire a researcher who only can access internal materials, and then one researcher who only accesses external materials.”  

3. Operating leverage

Another option is to double down on downstream, operational value creation levers, where AI is a valuable tool, but not a replacement for existing processes. This is less applicable for venture investors. However, operating alpha is a clear differentiator in private equity—a set of firms surveyed recently by Accenture aim nearly 80% of their value creation plans at operational improvements.^[11] This number has steadily increased in recent years as a natural consequence of the competitive pressures we identified in previous sections. As deals have become more competitive and entry multiples have increased, firms have responded with greater deal complexity and longer holding periods to create space for greater operational intervention. 

Of course, AI will play a key role here as well. Most obviously, in a direct way: the same benefits that AI delivers to dealmakers apply to portfolio companies as well. Last year, a Bain survey found that the majority of portfolio companies held by PE firms managing a combined $3tn+ were actively pursuing use cases for generative AI. Almost a fifth had already deployed applications to production.^[24] The ways AI is being used are typically mundane, but can deliver significant operational efficiencies. One PE consultant we spoke with described an AI project to automate invoice ingestion into an ERP—hardly the use case AI technologists dream of, but one that allowed the company to replace a full-time role.

We see the next frontier as one of strengthening operating plans before a bid is submitted. Given the increasing importance of holding period activities, it should come as no surprise that value-creation plans are being mentioned in PE firm earnings calls nearly 4x more in recent years.^[11] Like venture, AI is already being used during PE diligence and research to aggregate information and source deals. However, AI also makes it possible to continually update value-creation plans throughout the lifecycle of the investment, allowing firms to manage their holdings more effectively.

Assuming a firm has invested in a strong AI foundation, suddenly a static company analysis as it relates to deal evaluation, value-creation, and ongoing fund life management can be adjusted dynamically as one sees fit.

—Lydia Ofori, Plainr

Ofori, Plainr’s Founder, believes that AI will play a major role here, allowing PE firms to better forecast the range of possible outcomes during pre-deal diligence. “During deal management throughout the fund life, the opportunities for expansion as far as value creation goes [are] enormous. [With AI], you can see the possibilities for a company as clear as day,” she says. Furthermore, these plans can be updated with new information—both internal and external—much more frequently than before, enabling greater management precision throughout a holding period. “Assuming a firm has invested in a strong AI foundation, suddenly a static company analysis as it relates to deal evaluation, value-creation, and ongoing fund life management can be adjusted dynamically as one sees fit, to assess numerous end states and numerous opportunities. All in real time.”

Curious and contrarian

The areas we highlighted above—differentiated access, proprietary data, operating leverage—should feel familiar to most investors. However, there’s a deeper story at play, as the widespread adoption of AI creates a set of ingredients that have never before existed in private markets. The history of back-office automation illustrates the potential for unexpected change. “If you study what happened to bulge bracket banks when the back office got automated, the first-order effects were obvious,” said Stengel. Headcount migrated from rote, clerical work like manual trade-ticket matching and paper-based reconciliation, to higher value (and margin) activities.

Eventually, the cheap financial data feeds and computation resulting from this automation became low-cost services. In turn, it became feasible to build products that could serve small-balance customers at near-zero marginal cost, leading to a wave of direct-to-consumer financial services companies: neobanks, zero-commission brokers, robo-advisors, and more. “No one expected Chime, or some of these tools that went direct to the consumer. And really no one anticipated Robinhood.” With AI, “we’re still in the first-order effects range. The second-order effects are up for grabs, and that’s what I think is the most exciting opportunity.”

So, what might the future private markets landscape need to make room for?

New strategies will emerge

1. Continuous market measurement

One of the prime challenges in private equity and venture is the multi-year lag between underwriting an investment and realizing an outcome. In the February edition of the Consilient Observer, Michael Mauboussin, the well-known expert in predictive analysis in finance, writes, “Feedback in investing and business is impeded by noise and lag time between forecast and outcome.” The solution is logical—to “break down a thesis into subcomponents that are relevant over shorter time horizons.”^[25]

This is a tall task when testing these subcomponents for more than one or two theses requires manually parsing reams of documents or continually monitoring newsfeeds. With AI, it becomes feasible to automate much of this work. Instead of infrequent, shallow updates – think quarterly emails to a founder to catch up over coffee, or tracking a few high-level quarterly earnings metrics across a manually curated compset—firms could automatically ingest a much wider array of data as soon as it becomes available. These could then be evaluated by AI models against a set of criteria (possibly AI-generated) relevant to the overarching thesis, with changes surfaced to humans.

For example, as the global pandemic unfolded, the lack of historical precedent, coupled with near-overnight changes to daily life, created space for a large number of equally uncertain investment theses. How long would lockdowns last? Would sidewalk robots replace food delivery couriers?^[26] While it would have been impossible to quantify absolute likelihoods, tracking relative changes in likelihood would have been useful information. If US schools largely return to in-person instruction, as they did in late 2021, does it become marginally more or less likely that professional events remain virtual? More bluntly, should I invest in Hopin at $8bn in 2021? 

Naturally, adopting this proactive approach brings advantages—investors are less likely to fall victim to sampling effects, where a point-in-time snapshot of a market may miss or underplay an important trend. They are also better positioned to capitalize on events than investors starting from scratch. In the 1980s, the venture firm Accel famously popularized the ‘prepared mind’ approach to venture, whereby investors arm themselves with an informed viewpoint on a company, business model, and market. The reasonable assumption underpinning this is that a prepared mind increases the quality of investment decisions. Assisted by AI, doing this continuously becomes feasible.

2. The quant-native private market investor

If a much greater volume of external data can be brought to bear in pricing thanks to AI’s ability to traverse the unstructured world, reliably triangulating valuation based on external factors alone starts to look tractable. Comparables already factor heavily into price-setting in both venture and PE. Markups between venture funding rounds tend to be similar company-to-company. PE firms already track public data across many potential investments on an ongoing basis.

Augmented with other public telemetry about management team, traction signals, or recent news, it’s not farfetched to believe that most companies can be priced reasonably well, even before data room access. Armed with this continually updating pricing data, investors could look for companies that are inefficiently valued against some overarching thesis, a purely capitalist strategy. Alternatively, investors could focus on frontrunning processes, pre-empting rounds by issuing term sheets or unsolicited bids far earlier than any competitors. In both cases, the ability to execute would depend on the ability to analyze more data, faster—the exact conditions AI creates.

Greater private markets liquidity would unlock these more quantitative strategies further. Right now, especially in venture, the decision to invest is tightly coupled with a prediction of ultimate company success. The intermediate off-ramps provided by a more liquid market would allow for strategies that hinge on accurately predicting shorter-term market movements. For example, a successful venture fund typically contains one or more power law returners—the Ubers and Facebooks of the world. These funds also contain a long tail of companies that are nevertheless viable (smaller) businesses. If these companies could be effectively priced and subsequently traded by more firms, PE-style distressed strategies or something akin to event-driven investing could emerge.

As another example, during our conversation with Stengel, he theorized that firms could potentially issue term sheets right away, as part of a spread strategy. “Instead of just writing a cold outbound email, I could send a cold inbound current term sheet. And I’d just say that we’re ready to price, and we’ll give you the liquidity instantaneously without having to spend a lot of time” he said. “I could envision a quantitative VC firm that front runs every process by offering term sheets with some pricing, and then immediately sells the assets in secondary to other firms. And then this firm could take a spread rather than the traditional VC process, which is buy and hold for 10 years.”

To be clear, there are prerequisites to most of these things happening. Right now, it’s difficult to actually convert any short-term pricing edge into an actual trade—deals take weeks to months to close, secondary sales take weeks to months to execute. And, companies themselves may continue to serve as a check even if greater liquidity materializes. Founders and management teams care who is on their board or helping them navigate go-to-market. “The best counterpoint to this thought exercise is that I care about who my investors are,” Stengel said. “There are probably founders and executives who don’t, but that’s going to be a forcing function to prevent some of this stuff we’re brainstorming.”

Nevertheless, speed to price and ability to bring better data to bear have always been sources of edge in investing, and we believe that to be true here as well. 

3. Increased liquidity

So far, the unlocking of liquidity in private markets has been challenging to bring about, despite efforts from 2010s-era marketplaces like Forge, EquityZen, and Nasdaq Private Markets.^[27] In 2024, total US secondary trading volume was less than 0.5% of public trading volume. Forge, one of the largest pre-IPO exchanges accessible to the general pool of accredited investors, accounted for only one-tenth of this small corner of the overall market. 

Still, longer holding periods and greater private markets activity have created sustained interest in making exchanges for private assets accessible. Larry Fink, the CEO of BlackRock, devoted his 2025 Chairman’s Letter to making a case for private markets, and laying out BlackRock’s strategy to become a major private markets player. He wrote, “BlackRock has always had a foot in private markets. But we’ve been—first and foremost—a traditional asset manager. That’s who we were at the start of 2024. But it’s not who we are anymore.”^[28]

AI won’t singlehandedly overcome the impediments to realizing greater private markets access. Today’s private exchanges mostly suffer from a deficit of infrastructure and lack of access: trades are often broker-mediated, even when wrapped in a UI layer, and can take months to clear. Only accredited investors can participate. Selling shares in a private company generally requires explicit approval from the company’s board of directors. Supply is limited for the top ‘pre-IPO’ companies like SpaceX, and demand is limited for shares in companies outside the top few dozen or so. 

However, AI will act as an important tailwind to greater private markets liquidity, primarily by increasing the ability for investors to conduct price discovery. “For decades, private markets have been among the most opaque corners of finance. Investors know these assets hold long-term value—but exactly how much value? That’s not always easy to determine,” wrote Fink.^[28] Currently, bid-ask spreads for secondaries can be enormous—up to 50%, a clear indicator of market opacity. As we noted previously, we believe AI will both make price discovery easier, and potentially more precise. This would act to increase market transparency, which in turn, could drive greater demand for private market assets, boosting efforts to increase liquidity.

Future private markets: AI-driven, competitive, and complex

Putting these pieces together, we see a future in which venture and PE become more complex, more quantitative, and more competitive. AUM will continue to increase across asset classes, meaning more capital chasing deals. As a result, investors will need to increase their ability to source the best deals earlier in a crowded market. They will also need to increase underwriting sophistication and speed in order to avoid mispricing deals, or missing them as others get through price discovery faster. AI will act as both catalyst and enabler.

Increasing competition will push some investors to go earlier and smaller. Smaller funds or solo GPs investing primarily on the basis of their differentiated personal networks and relationships may migrate here as institutionalization pushes earlier and earlier. On the PE side, a similar trend is likely to unfold, with larger firms taking advantage of the increased underwriting efficiency offered by AI to push even earlier in an effort to preserve returns. We’ve already heard from smaller mid-market PE investors that the ~$100mn deal space is getting more competitive. 

As investors indexing primarily on access and taste move earlier and smaller, later stages will demand increasing rigor. In these segments, quantitative approaches will gain traction, with AI providing the technology on which these approaches are built. Assets will trade hands more frequently as it becomes easier to price, and spread strategies and shorter holding periods could emerge. Investing in these areas will trend more toward science than art.

Separate Truth from Beauty

The emergent strategies we explored are hypothetical, for now. No one is certain which AI applications and investment approaches will thrive in private markets as a consequence of today’s push for AI-driven operational efficiency. This has caused some investors to adopt a conservative stance toward using the technology. As one investor at a top 10 quant firm put it: “Right now, most investors are focused on cosmetic uses for AI, or applications that create operational efficiency, or just plain scared by the pace of progress from making investments in it.” 

We believe the worst approach for investors is to wait and see which unexpected use cases or strategies take off. Models and products are changing rapidly, and sitting on the sidelines risks creating an institutional knowledge deficit that makes it difficult to recognize true step function improvements in the future. It also guarantees that any new ways of capturing alpha will go to someone else.

Instead, we think that investors today should be aggressively experimenting with new tools to understand what’s possible now, and adopting or investing in the most promising outputs.

For firms

For those looking internally at their own firm’s operations and strategy, the message is clear: adaptation is not optional. We believe it’s critical for investors to adopt a flexible, experimental mindset toward AI. The Bloomberg terminal showed how new investment technologies often have unexpected secondary effects, and these effects are often more impactful than the initial expected benefits delivered by a product built using the technology. 

We’re still too early in the AI adoption curve to know what these things are. Stengel’s advice is to “allow yourself to experiment constantly in the near term on different tools, different types of workloads, different ways of thinking about investing. Don’t be afraid to dump tools. Don’t be afraid to force junior people on your team to mix up their process. Make sure that your vendor onboarding is easy, such that you can try five tools and constantly experiment. The firms that are the best are going to be the ones that are constantly experimenting and then doing what sticks.”

The firms that are the best are going to be the ones that are constantly experimenting and then doing what sticks.

—Gabe Stengel, Rogo

Even the most adventurous firms can’t test every new tool, so investors must be able to identify the most promising ones. Maithra Raghu, the Co-founder of Samaya AI, a company building expert AI agents for financial services, was quick to point out that this requires more than simply demoing new products. “It’s important to separate ‘Truth’ from ‘Beauty’ in AI,” she told us between fielding investor calls at Samaya’s Mountain View office. “Beauty is the exciting new AI demo—the ‘expert-level’ Deep Research that wows at first look, works in carefully constructed examples, but falls short in the real world.” 

Raghu gave us a framework for cutting through the hype. First, look for teams building products explicitly for financial services; general-purpose tools often struggle to get the details of specialized finance workflows right. Second, look for teams innovating at both the model and product layers, with tight feedback loops between them. Finally, ask for proof: teams should be able to show that their products consistently outperform off-the-shelf models in head-to-head benchmarks. These products stand the best chance of long-term relevance. “Truth is the enduring impact AI has on the real world,” Raghu said. “It’s the AI that is painstakingly constructed to handle the ambiguity, noise, and complexity of the global financial ecosystem. It takes a lot longer to build this truth, but its effects are lasting.”

While aggressively experimenting, firms can (and should) invest in backend work that puts them in the best position to get the most out of any AI tool they try. “Figure out what [are] the foundational bets you need to make, and start investing in them right now, to set yourself up for success no matter what. Those are things like putting in a real data layer, such that you are tracking what everyone does and centralizing it and making it queryable,” Stengel said. “Make the longer-term infrastructure bet to organize your data in a way that is AI-friendly, such that even if you’re not using ChatGPT or Rogo or another AI product right now, it’ll be easy to turn the faucet on when you need it.”

For investors

Investors looking at opportunities in investor tech should be excited that today’s alpha is tomorrow’s infra. Infra is investable. The infrastructure that private markets depend on will soon have AI at its core. Startups building in this area are chasing a $100bn+ revenue opportunity currently occupied by human labor. While a stack has already emerged, with companies like Hebbia, Rogo, Harvey, Plainr, Samaya, and more scaling quickly, we believe that we’re still in early innings—most firms haven’t yet adopted AI comprehensively across their organizations. 

We believe that AI-powered tooling for private markets represents an eminently investable, horizontal thesis, with significant value left to capture for startups building in this area. We’re particularly interested in products that tackle voice AI use cases, like expert network calls, those that help investors access and integrate capital flows data and other market telemetry into their day-to-day investment work, or enable firms to make more of their internal data legible to AI models. 

Ultimately people—founders, builders, and investors—are what will drive AI’s impact on private markets. Technology alone won’t transform the trajectory of venture and PE over the next decade. Jack Lynch has perhaps the best advice to offer for anyone looking to successfully navigate the new opportunities created by AI. “The best investors are intellectually curious, they are contrarian, they are independent thinkers,” he explained. “Investing is of course a science, but equally an art. The science now promises to be enabled by new technology, but the art is what the world’s greatest investors have in common.” 

Donald Lee-Brown is the director of research and Terran Mott is a research analyst at Positive Sum.

Acknowledgements

We thank the following individuals who also contributed ideas for this piece:

• Ryan Eisenman, Founder and CEO of Arch 
• Tim Flannery, CEO and Co-founder of Passthrough
• Mason Lender, Co-founder of Vantager
• Sid Masson, Founder and CEO of Wokelo
• Dhruv Tandon, Co-founder of Decisional

Positive Sum is an investor in some of the companies discussed or referenced in this article.

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The scar catches the dying light first—a crescent beneath Ramtin Naimi’s left eye that vanishes when he smiles, which he does often these days. From the terrace of his Tiburon home, the 34-year-old venture capitalist commands a view that stretches across the entire Bay Area: San Francisco glittering in the center, the Golden Gate Bridge suspended to the right, Berkeley and Oakland pooling to the left. Container ships drift beneath the bridge like bath toys while planes climb from SFO and small yachts chase the last breeze.

“It might be the nicest view in the world,” Naimi tells me, sipping a glass of Napa red, which his wife Lizzy notes as a rare indulgence. He’s dressed in his standard uniform: black Saint Laurent cap, t-shirt, and jeans; minus the white Vans he kicked off after arriving home.

The Transamerica Pyramid rises from San Francisco’s skyline like a pin, marking the neighborhood where Naimi runs Abstract, his $1.8 billion venture firm. From this vantage point, success appears inevitable. But tonight, his gaze drifts past the pyramid toward Van Ness Avenue, where 11 years earlier a very different version of Naimi sat in a bankruptcy lawyer’s waiting room, surrounded by fluorescent lighting and the particular desperation that smells of instant coffee and broken dreams.

The journey between those two points—from insolvency to influence—reads like fiction, the kind of story editors reject as implausibly neat. Yet here sits the evidence: a man who parlayed supernatural hustle, pattern recognition, and an immigrant’s refusal to accept limitations into one of the Valley’s most successful seed firms, backed by Stanley Druckenmiller, Bill Ackman, Kevin Warsh, and Michael Ovitz.

“I’ve been discounted by so many people in my life,” Naimi says, and the scar under his eye seems to deepen in the fading light.

Money lessons in the Naimi household arrived with the subtlety of a Persian carpet seller. “America revolves around money and you need to figure out a way to make it,” his father repeated like a family motto.

His parents had been childhood neighbors in Tehran before the Iranian Revolution scattered them across continents. They found each other again in Los Angeles as young adults and married in 1984. By the time Naimi was born in November 1990, they were living the classic immigrant story: small business owners grinding their way toward the American Dream.

His father cycled between restaurants and dry cleaners, arriving home past midnight only to rise again at 6am, stress permanently etched into his face. CNBC played constantly in their household, his father monitoring markets that had nothing to do with his businesses.

When Naimi turned 12, his mother announced they were moving to San Francisco to be near her sister. Naimi, his older brother, and mother crashed with his aunt’s family for three months while his father liquidated everything in LA. The family then settled in Tiburon, first in a cramped one-bedroom apartment, then a two-bed, and finally a modest starter home. 

His mother prioritized neighborhood over house size. “We were the tiniest fish in the biggest pond. I had friends whose fathers were the CEO of Visa or the CEO of Blue Shield and lived in these extravagant homes. I knew these things existed, and I knew the path to them wasn’t being a doctor or a lawyer,” Naimi explains.

In high school, Naimi discovered a gift for seeing systems. His first entrepreneurial venture emerged from observing an inefficiency in Marin County’s social hierarchy: the divide between girls whose parents threw elaborate Sweet Sixteen parties and those whose parents couldn’t. His solution was financing celebrations for anyone who wanted one.

Naimi would rent venues, hire DJs, arrange insurance, and recruit security from local gyms: “roided-out meatheads” who worked for $50 a night. The birthday girl got her party with her name on the banner and DJ shout-outs. Naimi got to charge admission and invite kids from every high school in Marin County, whether they knew the birthday princess or not.

His first party nearly bankrupted him. He’d spent so much on production that his parents worried he’d lose his savings. But within 10 minutes of opening, his pencil box overflowed with cash. He had to run to the bathroom for a garbage bag.

To float his party empire, Naimi worked weekends at West Elm, the furniture store. He would spot customers who’d rented Zipcars—clearly intent on taking furniture home—and aggressively upsell them using the store’s progressive bonus structure. “Somebody would come in wanting to buy a sectional,” he recalls. “I’d ask, ‘Do you want the pillows? The coffee table? The rug?’ They’d say no, just the sectional. So I would take off all the accessories just to show them what it would look like at home.” He ended up with the highest average ticket sales at the store.

But Naimi’s real education was happening in front of a screen, where his father’s CNBC obsession had metastasized into something more dangerous. At 13, he convinced his parents to lend him $2,000 to learn stock trading. When $2,000 proved insufficient for meaningful equity positions, he migrated to derivatives that could multiply his buying power.

The timing was fortuitous in the way that only hindsight reveals. As the 2008 global financial crisis unfolded during his senior year of high school, Naimi was trading out-of-the-money options against triple-leveraged ETFs tracking the banking sector. These instruments swung 10–30% daily based on Federal Reserve announcements or news about Lehman Brothers’ impending collapse. In the weeks surrounding Lehman’s bankruptcy, he made several hundred thousand dollars.

The success proved intoxicating. Instead of college, Naimi launched a hedge fund straight out of high school. He obtained his Series 65 license and began raising capital from the network he’d built around a local financial advisor’s office in Tiburon. In total, he raised $3 million from 45 investors. He was the largest LP in his own fund, having poured his $500,000 of trading profits into it.

In January 2009, aged 19, he launched the fund at the nadir of the financial crisis. He now concedes the absurdity: a 19-year-old kid ripping options all day long, trading iron condors and selling naked puts on levered ETFs and volatile tech stocks.

The fund produced 59% annualized returns over three years—no small feat for a self-taught teenager. But the emotional cost was brutal. Naimi lived chained to his six trading screens, taking only two vacations in four years, both cut short by panic attacks when separated from his terminal. There were months when the fund dropped 27%; others when it rose 28%. 

The transition away from his screens was catalyzed by growing curiosity about the technology industry buzzing around him in the Bay Area. He cold-emailed VCs, angel investors, and founders, trying to understand what was happening in Silicon Valley. Most ignored him, but a few took meetings, including Stuart Peterson, who ran ARTIS Capital.

“He showed me the scale of private companies, how many were staying private longer, and how much alpha was getting picked up in private markets,” Naimi remembers. More importantly, Peterson identified something about Naimi’s personality that he hadn’t recognized himself: “My personality is much more suited to being out hustling and meeting founders and learning about new technologies than sitting by myself for 15 hours a day.”

He wound down the fund in 2013 with $1.5 million in the bank and quickly stumbled into a significant problem: his lucrative detour had severed him from the credentialing system that governed Silicon Valley access. He had returns but no relationships; quantitative skills but no understanding of how venture capital actually worked.

Davidov and Naimi in the entranceway to Abstract’s Jackson Street office.

The rejections came wrapped in encouragement and condescension. “Hey, listen, your background’s not that relevant to venture capital,” became a familiar refrain. “Maybe you should start a company.”

Naimi spent months trying to penetrate an ecosystem that prided itself on meritocracy. “Rinky-dink hedge fund manager,” as one person characterized him, didn’t fit the template of Stanford computer science degrees, Harvard MBAs, successful startup exits, or family money.

If starting a company was the price of admission, he’d pay it. In 2014, during the marketplace lending boom, he identified what seemed like an obvious market inefficiency. Companies like Lending Club and Prosper were growing rapidly, issuing loans with three- to seven-year maturity periods. But unlike banks, which traded debt instruments as liquid assets, peer-to-peer lending offered no secondary market.

Naimi built Lenders Exchange to allow debt investors to trade their positions before maturity. He self-funded the company, assembled a team, and worked through the complex regulatory framework required to operate a trading platform.

Fourteen months later, when he finally had a product ready for external financing, the sector collapsed. Lending Club’s stock plummeted 85% from its IPO peak. Sequoia began writing down its Prosper investment to zero. The category became untouchable.

“I had spent my entire net worth building a supplemental product to a collapsed industry,” Naimi says. The failure wiped him out completely, but worse was the debt he’d accumulated keeping the business alive. Credit cards ballooned to $300,000 as he tried to bridge toward a fundraising round that never came. 

The emotional devastation matched the financial ruin. His parents, who had watched their youngest son make millions, now watched him sink into insolvency. The Persian immigrant values that had driven his early success cut him deeply. Money mattered in their household, and he had lost all of his. His mother never criticized him directly, but her heartbreak was visible. His father was “livid,” but later acknowledged Naimi’s resilience: “99 out of 100 people just would have gone into a hole for the next six months,” he reassured him. “You were grinding the next day.”

The bankruptcy lawyer’s office on Van Ness Avenue represented everything Naimi had spent his life trying to avoid: fluorescent lighting, plastic chairs, worn carpet that reeked of stasis. Everyone looked resigned to circumstances beyond their control.

He was flat broke when he Googled “bankruptcy lawyer San Francisco” and clicked the first result. The rumpled lawyer matched his surroundings perfectly. Naimi explained his story. The lawyer listened, then smiled with unexpected warmth.

I had spent my entire net worth building a supplemental product to a collapsed industry.

–Ramtin Naimi

“I’m not the lawyer for you,” he said. “I do Chapter 7 bankruptcies. Chapter 7 is for people who are down on their luck and not getting back up.” He reached for his phone. “Let me call Brent. He’s the person you need to see. You need to file Chapter 13, not Chapter 7. You’re going to start making money again quickly. I know you will. You just need to restructure your debts.”

The lawyer surveyed his waiting room, then looked back at Naimi with something approaching respect: “You’re not like these guys.”

An hour later, he was in a much nicer law firm, explaining the same story to Brent, who wore a proper suit and worked from an office with actual artwork on the walls. Chapter 13 turned out to be straightforward. Within two years, Naimi had cleared every debt.

But sitting in that waiting room, surrounded by people whose circumstances felt permanent, Naimi faced the possibility that his confidence had been misplaced, that his early success had been luck rather than skill.

The unexpected faith from a grizzled lawyer righted him. The near two-year saga that had emptied his bank account had also carved something permanent into his character: a chip on his shoulder that would prove more valuable than any credential.

One of the VCs who had passed on his marketplace lending company was Arjan Schütte, Founder of Core Innovation Capital. Naimi was interviewing for account manager roles at any startup he could find (and getting rejected) when Schütte offered him a modest role at Core: part intern, part associate. Naimi grabbed it like the lifeline it was.

“He had a very strong style and a Rain Man-like memory for numbers,” Schütte recalls. “I remember being struck by an unusual outlier talent, and he just needed a home somewhere.” For Naimi, it is still the kindest thing anyone has ever done for him. The salary was minimal and he was mostly left to his own devices, but the credential was invaluable. After years of rejection, he finally had a venture capital business card.

Core was a fintech-focused firm doing Series A investments. It didn’t take long for Naimi to realize that wasn’t his calling. He wanted to be a generalist seed-stage investor, catching companies at their earliest and most malleable stage. But the traditional path—working at a venture firm for several years before being granted check-writing authority—didn’t suit his timeline or temperament. 

As he absorbed what he could from Core, he began reverse-engineering his path to seed investing. He meticulously researched power law outcomes—companies worth more than $5 billion—and made a counterintuitive discovery: multi-stage venture firms were better at seed investing than dedicated seed funds.

“At the time, if you eliminated Uber and Roblox, whose seed rounds were led by First Round Capital, you couldn’t point to a single power law outcome within a venture timeline where the seed round was led by a seed-stage venture firm,” he explains.

The pattern suggested that seed funds claiming proprietary deal flow were, in Naimi’s characteristically blunt assessment, “full of shit.” How could a three-person seed fund have better coverage than a 50-person multi-stage firm?

Instead of competing with these giants, Naimi decided to align with them. He chose to become a seed investor that treated multi-stage firms as partners rather than competitors, accepting smaller ownership stakes in exchange for access to higher-quality deals. It was a radically different approach that required abandoning the traditional seed fund playbook.

But first, he needed deal flow. He analyzed the backgrounds of founders backed by top-tier VCs, identifying patterns in education, work experience, and career trajectories. He built a LinkedIn scraper to track roughly 7,000 people who fit the profile. 

“Anytime one of them changed their job title to founder, I got a push notification,” he explains.

Nine times out of ten, the change happened before they raised funding. Naimi discovered that “an unfunded seed stage founder is the easiest person in the world to get a meeting with.” But meetings didn’t equal investments. He needed capital, and banks weren’t lining up to fund a recently bankrupt 25-year-old.

The breakthrough came via AngelList. Naval Ravikant’s platform allowed anyone to raise SPVs for individual deals—no permission required, just quality deal flow. When Naimi posted his first opportunity, Ripple, he raised $470,000 within four hours. 

“I was like, holy shit, what was that? This is incredible. I can’t believe this actually works.” The economics made it even sweeter: He would earn 15% carry on every SPV he syndicated through the platform.

Naimi sobered up quickly when forwarded emails began arriving, accusing him of hijacking the deal. An experienced AngelList investor alleged that he had been raising his own Ripple allocation when Naimi’s SPV went live. They contacted Ripple’s CEO Brad Garlinghouse and Schütte at Core, claiming the young investor had misrepresented his allocation and had “no right” to syndicate the deal. Naimi’s stomach dropped. He worried about being fired from Core and offered to retract the SPV.

Salvation came from Garlinghouse himself. Ripple’s CEO validated Naimi’s allocation and praised his transparency, providing crucial backing when his credibility hung in the balance.

Naimi then went turbo. 

Gil Penchina, a legendary angel investor, helped him navigate the platform’s mechanics and lent credibility to his efforts. “Gil had a big following on AngelList. He let me market my first few SPVs to his LP base as a way to get me on the platform,” Naimi recalls.

Lee Jacobs, who managed syndicate partnerships at AngelList, watched with amazement: “At first it was almost unbelievable. Every week, this random dude would send lists of 15 companies backed by tier-one firms. He wasn’t a founder, and hadn’t worked at a tech company; it came out of nowhere.” But the deal flow proved real, and Naimi quickly became one of the platform’s top investors.

For six months, Naimi represented roughly one third of AngelList’s total volume. Between August 2016 and June 2017, while technically employed by Core, he invested in 47 seed-stage companies either via an SPV raised on AngelList, or as an angel. Two of them—Solana and Ripple—went on to have coin market caps worth more than $100 billion each. A third, Rippling, is now valued around $17 billion. He also caught early rounds of future unicorns like Clay, Cherry, Material Security, and NewFront. His SPV investments have already returned just under $100 million to investors.

Word of Naimi spread quickly through Silicon Valley’s network. By early 2017, everyone wanted to hire him. By August, when he had momentum doing his own thing, he was declining those offers and fielding proposals from VCs wanting to become LPs in a fund he didn’t yet have. He wasn’t convinced the approach would scale to a full fund, but when the conversation evolved to buying equity in his management company, the equation became too attractive to ignore.

In the deal that emerged, Marc Andreessen, Chris Dixon, David Sacks, Keith Rabois, Michael Ovitz, Kevin Hartz, Bill Ackman, Stanley Druckenmiller, and Kevin Warsh bought 20% of his management company, Abstract, for $10 million in upfront cash. Naimi was 26 years old with a theoretically worthless company that had maybe $10 million in total assets under management. 

“It was a group that I’d only read about and would have given the equity to for free if they’d asked for it,” Naimi says. “But if they were willing to pay for it, then that made it even better.” The deal included a seven-year sunset provision, whereby the investors earned economics on everything Abstract did in that window. By the end of 2024, Naimi owned 100% of his firm again.

This is the single most important person you’ll meet, whether you succeed or fail. Ramtin is the key to the kingdom you want.

–George Sivulka, Hebbia

The consortium came together through a web of introductions: Cyan Banister, whom Naimi knew from AngelList, introduced him to Hartz; Hartz made introductions to Rabois and Dixon; Dixon brought in his partner Andreessen; Andreessen suggested Naimi meet his mentor, Ovitz; Ovitz connected him to Ackman, Warsh, Druckenmiller, and Sacks.

Hartz, who founded Xoom and Eventbrite, remembers thinking: “If you were this good not knowing anybody, I wonder how much better you’ll get if you know all the right people.” Ackman, founder of hedge fund Pershing Square, was drawn to a familiar story. “I was once a 26-year-old trying to raise money,” he told me. “Ramtin seemed smart and had a lot of energy. He was very entrepreneurial. I liked his strategy and the way he was building his business.”

For his part, Ovitz said, “I got an instantaneous sense that this was a guy to develop and mentor. I went out on a limb with him, bringing quite a few large investors to the table, which I rarely do without really knowing somebody. But it turned out I was right.”

Naimi remembers his first meeting with Ovitz differently. He arrived at The Battery restaurant in San Francisco so nervous he could barely think straight, overwhelmed by the aura of the man who’d built CAA from nothing and negotiated deals for Sony, Steven Spielberg, and countless others.

The Hollywood legend settled into his chair, took a deep breath, and delivered a line that almost broke Naimi’s composure: “To what do I owe the pleasure?”

“What do I say to that?” Naimi thought, scrambling for an answer that wouldn’t sound ridiculous.

Midway through the conversation, they stumbled upon the topic of cars. Ovitz mentioned he’d dabbled in collecting vintage cars but that storage was a pain, so he’d never taken it seriously. He’d once been offered a collection of 12 classic Ferraris from an estate sale. 

“Guess what I could have bought the collection for?” Ovitz asked.

Naimi clarified what year and which specific cars were included. Eleven were standard classic Ferraris. One was a 250 GTO—among the world’s most valuable.

Naimi calculated rapidly. “I don’t know, they probably wanted $18 million.”

“That is spot on!” Ovitz replied, extending his fist for a bump.

“From there, we were just kind of buddies,” Naimi recalls.

Eight years later, their relationship has deepened into something closer to family than business. Ovitz describes Naimi as his “non-biological son” and marvels at his intensity. “Everything he gets into,” Ovitz told me, “he approaches with the same vigor, but it’s not the kind of intensity that’s a turn-off. He’s grown into one of the best investors out there.”

Naimi counts Ovitz as one of his best friends. The pair, separated in age by 44 years, talk every day in conversations that range far beyond venture capital.

“We talk about art, for example,” Ovitz says. “He wanted to collect art, and I got him started. I’ve gotten a lot of guys started but most of them don’t follow through. Ramtin is building a very impressive collection. He has worked hard to learn. He listens well.”

Both Ovitz and Stuart Peterson—Naimi’s original mentor who introduced him to venture capital—possess world-class collections. “Every time I visit them, I just try to learn a little bit more about what’s in their house,” Naimi says. “They could both spend two, three hours walking you around their home and talking about what they have and how they got it and the story behind it, the history behind it, the significance of it.”

Naimi now owns works by Christopher Wool, George Condo, Mark Grotjahn, Larry Bell, and other masters. He approaches art collecting with the same systematic intensity he brings to venture investing: studying markets, building dealer relationships, and hunting for pieces before they become widely recognized. Abstract’s office is littered with striking art, although Naimi classifies them as “pieces that won’t make me mad when someone bumps into them.”

Naimi’s other best friend (the third being his wife, Lizzy) is Hartz, who works a block away on his own early stage venture firm, A*. In addition to being a close friend, he serves as a tactical sounding board and deal-making partner. Naimi calls him “Kevie” and talks to him four hours a week. 

That Naimi has cultivated close relationships with people who could have easily dismissed him as just another ambitious upstart seeking their stamp of approval says as much about his personality as his capabilities as an investor.

The $10 million in upfront cash from selling equity in his management company solved Naimi’s immediate financial problems and gave him the resources and network to build Abstract into a real venture capital firm.

He made his first hire in 2017, recruiting Alex Davidov from Core Innovation Capital as a Founding General Partner. Where Naimi was the front-facing relationship builder and deal sourcer, Davidov would be the operational backbone that could systematize Abstract’s approach and scale its capabilities. “Alex and I are polar opposites,” Naimi explains. “I’m an eternal optimist and he’s kind of a default skeptic, which I think is a healthy balance to have.”

Both have trading backgrounds—Naimi from his hedge fund years, Davidov from four years at Bridgewater Associates—which give them a shared framework for thinking about venture capital as a methodical problem rather than a collection of isolated bets.

Their first fund targeted $100 million. Most advisors suggested starting smaller, but Naimi was determined to make a statement. “I wanted it to be significant enough that people had to pay attention to us,” he says. “It wasn’t just going to be another $20 million seed fund. It was going to be a $100 million seed fund.”

The same individual investors who’d bought equity in his management company became anchor LPs, providing roughly half the capital. The remainder came from another set of individuals: Joshua Kushner of Thrive, Matt Cohler of Benchmark, Neil Mehta of Greenoaks, Chase Coleman of Tiger Global, Santo Politi of Spark. Four institutional LPs participated. Naimi also attracted operators like Jerry Yang from Yahoo and Frederic Kerrest from Okta.

The final close came down to the calendar deadline. The terms they’d set with investors gave them exactly one year from first close to complete the fundraise. On Christmas Eve 2019, Naimi was visiting family when his phone rang. An Israeli family office wanted to commit the final $2 million. “We nailed $100 million on calendar day,” he says with satisfaction.

Abstract’s first fund systematized and scaled Naimi’s SPV approach: Identify companies before the multi-stage firms find them, then introduce those companies to the right partners at tier-one firms.

We are the best firm in the world at getting founders from seed to Series A.

–Ramtin Naimi

But the strategy required reimagining traditional venture capital portfolio construction. Abstract’s model focused on “relative ownership” rather than absolute ownership: If he could get 5% ownership in deals where Andreessen Horowitz got 15%, he might have one-third their ownership, but out of a fund that was one-fifteenth the size, his LPs got 5x more exposure to those companies than they would as investors in Andreessen’s fund.

Naimi executed this strategy perfectly 22 times in the early days: tier-one co-lead, 15% for them, 5% for Abstract. But the division of labor chafed. “I was doing all the work and only getting 5%,” he recalls. The solution was to start leading deals himself while still keeping the tier-one firms involved as co-leads at slightly reduced ownership levels—typically around 10%, which he discovered was the threshold below which top firms would walk away from seed deals.

The first four deals he led raised Series A rounds from Benchmark, Sequoia, and Andreessen Horowitz. It was immediate validation that the companies he was identifying and leading at seed were not adverse selection. They were genuinely high-quality opportunities that top firms wanted to back at later stages.

Since that initial $100 million fund in 2018, Abstract has raised capital at an accelerating pace: $270 million in 2021, $300 million in 2023, and $500 million in 2025. The firm manages $1.8 billion in assets today, including $600 million in realized and unrealized gains, making it one of the largest seed investors in the world. 

Half of Abstract’s funds rank in the top decile for their vintage; the other half in the top quartile. The portfolio includes seed and early-stage investments in breakout companies like Neon (bought for $1 billion), Replit, Krea, xAI, Partiful, Hebbia AI, Garner Health, Crusoe AI, and Polymarket.

As the firm has grown, its value proposition has sharpened around a single metric: “We are the best firm in the world at getting founders from seed to Series A,” Naimi says before launching into the data that shows how Abstract’s portfolio companies graduate to Series A at twice the market rate, with median valuations 75% higher than comparable deals. They’re 10 times more likely to raise from tier-one firms. By Series B, they raise 1.75 times more capital for the same ownership sold—meaning founders keep more of their companies.

This evolution from follower to orchestrator has happened steadily. In Fund I, Abstract led perhaps 20% of its deals, mostly co-investing alongside established firms. By Fund III, it was leading 80–90% of investments. 

For six years, it was just Naimi and Davidov on the investment team. But in 2023, as Abstract closed its largest fund yet, Naimi began recruiting in his own image: Hire one exceptional 26-year-old every year, give them meaningful responsibility immediately, and review their hustle after three years.

David Kwon came first, through a connection at Coatue. He’d cut his teeth in growth investing at Stripes and Greycroft. Andrei Kozyrev arrived next, in 2024, through a connection at Sequoia. He brings a technical computer science background and experience from Scale AI. 

The office itself reflects the firm’s evolution from scrappy upstart to established player. Abstract occupies the top floor of a converted warehouse on Jackson Street, across from Jony Ive’s LoveFrom headquarters. The building is owned by Stuart Peterson, who operates out of the fourth floor—a piece of real estate poetry that places the apprentice above his mentor. The space itself reflects the influence of Lizzy Naimi, the firm’s unofficial brand officer, and appears a tranquil oasis of walnut and limestone.

But beneath the polished surface runs a culture of almost manic urgency. At 10:30am, Naimi opens his sugar-free Red Bull. The four-person investment team meets every morning at 11—not weekly like most firms—to review every deal in the pipeline. Everyone works from the office five days a week. Response times are measured in minutes, not hours.

“If a founder intro comes through at 9pm and you’re not asleep, I want you to make sure the meeting gets scheduled,” Naimi instructs his team. “Because if you don’t respond until the next morning, it won’t get scheduled until the following day.”

The culture reflects Naimi’s belief that venture capital is ultimately a service business where execution excellence creates sustainable competitive advantages. When Abstract competes with larger, more established firms, speed is a differentiating factor—even when it seems there’s nothing to do.

At HF0’s mid-June demo day, 50 investors packed into the startup accelerator’s house on Fulton Street to watch 10 founders pitch for seed funding. Only one company had already closed its round: Max AI. That Naimi had funded the business weeks earlier surprised no one.

That’s what I mean about having a shark on your cap table. The good kind, one who knows how to swim alongside apex predators without getting eaten.

–Victor Perez, Krea

But Naimi has learned that founders are also increasingly choosing seed funds based on operational support, and he doesn’t care to lose for that reason.

His solution differs from the traditional model where junior staff handle seed companies while senior resources go to later-stage investments. Instead, Naimi has hired what he calls “coaches”—senior specialists that founders will listen to.

Caroline Stevenson, who scaled Dropbox from 20 to 2,000 employees, handles talent advisory. Anthony Heckman, who built unitQ’s first million in revenue when “no one had heard of them,” leads go-to-market strategy. The full Abstract team now numbers 11, each person carefully selected for a specific role in the machine Naimi and Davidov have built. 

“His team is super sharp,” says Scott Morton, the former SpaceX engineer who founded Revel. “They had very good technical questions, and Caroline has been helpful in thinking through recruiting strategies.” Victor Perez, Co-founder of AI company Krea, credits Heckman with enterprise guidance: “He’s been back-channeling with customers to help us close deals. After our Series B, we had huge inbound but no enterprise experience—Anthony filled that gap.”

The investment team: Kozyrev, Kwon, Davidov, and Naimi.

The most common criticism of Naimi and Abstract is that they are mere “signal chasers” who follow the lead of tier-one firms rather than develop their own independent judgement. That Abstract’s portfolio is large, roughly 200 companies across its funds (not counting Naimi’s early SPV investments), only adds fuel to the murmurs that it is an index investor, not an active manager.

The criticism clearly irritates Naimi, though he acknowledges its partial truth in Abstract’s early days. The systematic approach to identifying founders and deals did rely heavily on pattern-matching to successful outcomes. But times have changed, and Naimi is now the heat that other firms cozy up to.

“Ramtin is uniquely effective at helping founders with their subsequent rounds,” says David Sacks, Co-founder of Craft Ventures. “Abstract is one of our first calls when we see deals that are too early for us. He has built an elite early-stage firm.”

The transformation is proven by Naimi’s founders.

October, 2020: George Sivulka was eating microwave meals in a closet when the texts started coming.

The 22-year-old had dropped out of his Stanford PhD program to build Hebbia, an AI company that barely existed beyond a concept and a tiny pre-seed round from Floodgate. He was sleeping on the floor of a master bedroom closet in East Palo Alto, surviving on Trader Joe’s microwave meals, when Naimi began his pursuit. What happened next depends on memory and perspective.

“He messaged me every day for a week and I ignored him,” Sivulka recalls. “I couldn’t work out how he had even found me. Finally he texted, ‘I hear you’re sleeping on the floor, let me take you to a nice restaurant. Meet me at Kokkari in Jackson Square.’”

Naimi tells it this way: He had spotted the Floodgate announcement and noticed Kevin Hartz was an angel. A quick text to Hartz yielded an introduction. From there, he took Sivulka to dinner at Kokkari and made his pitch over expensive Greek food. “I put in a preemptive term sheet to lead George’s seed round. And I thought I had it in the bag.”

That’s when the daily texting began. Not to get Sivulka’s attention, but to keep it, “because the longer he took, the more I realized that there was somebody else around the table.”

Suddenly Naimi found himself competing against one of the Valley’s most respected investors. “I wasn’t quite in a position in my career to go head-to-head with Index’s Mike Volpi yet, but I still fought hard.” He finally lured Sivulka out for another fancy dinner, this time at La Mar on San Francisco’s Embarcadero, where he was told: “I think we’re gonna go with Volpi, but we’ll get you in one way or another.”

Naimi got the second-largest check. Volpi won the deal. But five years later, with Hebbia valued around $700 million after its Series B, Naimi has been cut a bigger check in every subsequent round, becoming one of Sivulka’s closest confidants. When asked to give references about Naimi to other founders, he tells them: “This is the single most important person you’ll meet, whether you succeed or fail. Ramtin is the key to the kingdom you want.”

October, 2022: Shreya Murthy, Co-founder of New York-based Partiful, sat across from Naimi while on a trip to San Francisco, walking him through her company’s metrics. The event planning app had solid traction, but his assessment was direct: “Your metrics are really strong, but no one wants to invest in consumer right now. Just keep executing. Let’s wait for the market to improve.”

The next day, a Friday, Murthy called and told Naimi she had just received a verbal offer for their Series A.

“I have never seen a more visceral metaphor of a shark that smelled blood in the water when I told him we got that verbal offer,” Murthy recalls. “Ramtin just did a 180. He’s like, ‘Okay, we’re doing it. Here’s what’s about to happen.'” 

Naimi mapped every relevant investor in Silicon Valley, identified the specific partner at each firm who should meet Murthy, and personally made introductions to senior GPs rather than letting her work through junior associates. More importantly, he created information asymmetry by controlling the flow of data between Murthy and potential investors.

“I would have a meeting with someone he connected me to or someone I knew,” Murthy explains. “And then I would call or text him after the meeting to debrief. Meanwhile, he was debriefing with the person I met. And then we were debriefing together.”

The intelligence gathering allowed Naimi to provide tactical advice. Before Murthy’s lunch meeting that Tuesday, he called to warn her: “I just had breakfast with the person you’re meeting at 1pm. Just so you know, he’s been burned by a bunch of consumer stuff. He’s really bearish on the space right now.” She went into the pitch with nothing to lose.

Naimi called as she was heading to the airport that afternoon. “Where are you right now?” he asked. She was three blocks from her Uber pickup in the Mission District of San Francisco. “Can you push out your flight?”

“No, I have jury duty tomorrow,” she said.

“I just got off the phone with the person you met. He loved you and has cleared his schedule for the rest of the day so he can spend more time with you. Where are you right now exactly?”

“I’m three blocks from Dandelion, waiting for my Uber.”

“Just cancel your Uber and hold tight.”

Five minutes later, a GP whose name would be recognized by anyone in Silicon Valley—an LP in Naimi’s fund—personally picked up Murthy in his Tesla and drove her to SFO, continuing their conversation during the 20-minute ride to the airport.

Partiful’s Series A ultimately attracted six term sheets, with the final valuation 67% higher than the initial verbal offer. “We were in name-our-terms territory,” Murthy says. “We could have gotten over double what our first offer was.”

Before that week, Murthy had spoken to Naimi a dozen times since he’d invested in their seed round. During the Series A, they talked and texted about 100 times a day and have stayed close ever since. It’s a common pattern with Naimi. Founders talk about how fast and frank he is initially, but once they’ve been through one of his fundraising odysseys, they talk about Naimi in tones of trust, fun, and admiration.

When his wife was in the delivery room with their third child, Naimi helped Murthy negotiate a critical deal. “Before you get to know him, he’s not going to feel like your best friend,” Murthy says. “But as soon as you start winning with him, he’ll become one of your favorite people to get dinner with.”

November, 2023: On a Monday morning, Thanksgiving week, Naimi’s phone was buzzing off his charcoal-black desk.

Victor Perez sounded panicked. His AI company Krea had just released a real-time image generation product that had gone viral. Their waitlist exploded from zero to 300,000 people in a week. Their burn rate jumped tenfold. They needed GPUs and money, fast. An investor had handed them a pre-emptive term sheet at a $60 million valuation, expiring Friday.

“These investors aren’t well-known, but they’re nice and the offer’s fair,” Perez told Naimi, who’d invested in their seed round. “We’re going to take it. Who’s going to give us a term sheet this week? We don’t even have a deck or a data room.”

Naimi asked for bullet points on their business. Perez and his co-founder Diego Rodriguez emailed at 4am Tuesday. By the time they woke up, Naimi had arranged 15 meetings with top-tier investors. 

“Over the next two days, we met with Marc Andreessen, Founders Fund, Sequoia, Benchmark, Lightspeed, Menlo Ventures … everybody,” Perez recalls. “This was Thanksgiving week!”

They called Naimi after every meeting. By Tuesday night, Andreessen Horowitz had made a verbal offer. On Wednesday, Marc Andreessen flew from Malibu to close the deal over dinner at Selby’s in Palo Alto. Thanksgiving morning, they signed at a $135 million valuation—more than double the original offer. 

“That’s what I mean about having a shark on your cap table,” Perez says. “The good kind, one who knows how to swim alongside apex predators without getting eaten.”

Anish Acharya, General Partner at Andreessen Horowitz, was at each of those meetings. “Ramtin’s got a knack for being around all the most important deals,” he told me. “Whenever Ramtin says something is important, we take it incredibly seriously.” Acharya also explained that “Krea’s founders were largely unreachable by investors in Silicon Valley until Naimi made introductions to what’s now seen as one of the top consumer AI companies in the world.”

The daily meeting.

The sun has nearly set over San Francisco Bay, painting the water in shades of copper and gold. Behind him, Naimi’s concrete-and-glass house glows from within. He first glimpsed it at 18, trailing his mother through Marin County’s Designer Showcase—their annual ritual of touring architectural showpieces they couldn’t afford.

“I thought it was fucking cool,” he recalls. The house stuck with him, and when he finally had enough liquidity to dream seriously, he asked his realtor to call if it ever became available.

In 2021, just as he prepared to break ground on a Presidio Heights construction project, he got the call. The owner—a European biotech investor who used the house for board meetings—wanted out of California. He needed five months to move his belongings to Europe.

“Make him an offer today,” Naimi said. “What he paid in 2016. He can have the full five months.”

“That’s offensive,” the realtor said.

“We have five months to negotiate,” Naimi replied.

The offer was accepted the next day.

His parents’ journey from that cramped one-bedroom apartment to watching their son muscle his way to this view represents the American Dream in its purest form. “My dad tells everyone about me now,” Naimi says. “I’ll meet new friends of his who already know my whole story. My mom just says ‘evil eye’—she’s worried about jealousy.”

The whispers don’t just emanate from his proud father. Scott Morton had never heard of Abstract when Naimi first reached out. But he kept hearing the same phrase: “Ramtin’s got the hot hand in San Francisco.” That’s how Silicon Valley anoints its own—through whispered endorsements that carry more weight than an MBA.

In Persian culture, Naimi explains, only two things truly matter: family and money. Success without family is hollow; family without financial security is precarious. Watching his two children play in the distance while Lizzy tends their newest addition, it’s hard to argue he hasn’t mastered both equations.

Yet those who’ve bet on him carry different scorecards.

“What I think is most impressive is he’s just as much a scrappy, determined, street fighter,” says Kevin Warsh, a Partner at Stanley Druckenmiller’s Duquesne Family Office and former Federal Reserve Governor who backed Naimi when he was nobody. “In investing, people tend to lose those rare qualities with success. But, so far, Ramtin is the same guy I met when he was 26.”

Bill Ackman offers a different kind of pressure: “The problem with venture is it takes a decade before you really know. So I would say the rubber will start meeting the road very soon.”

Eight years in, with $1.8 billion in assets under management and a portfolio studded with unicorns, that timeline feels both daunting and liberating. But if Naimi feels the weight of approaching judgment, he doesn’t show it. Instead, he thinks about his brother, who recently sold a majority stake in his vertical SaaS company for just under $30 million.

“He just cashed out,” Naimi says, still perplexed. “I was asking him, ‘Why didn’t you stick at it for longer? If you just doubled again, it’d be worth $60 million!’” Where his brother saw an exit, Naimi saw an inflection point.

“I actually think we’ve tempered how large we could have been by now,” he says. “I think a lot of our LPs appreciate that. Our latest $500 million fund was basically oversubscribed before we started raising it.”

“Sequoia was founded in the early seventies; it’s more than 50 years old today. Benchmark in the mid-nineties—30 years ago. Abstract is eight, but I’m 34. By the time I’m 54, Abstract could be a 28-year-old brand: One of the defining venture capital firms of Silicon Valley.”

He watches lights flicker across San Francisco. “Five years ago, that was a pipe dream. Now, if I squint really hard, I can actually see a world where it becomes real.”

Dom Cooke is the managing editor of Colossus.

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For years now, I’ve had an on-again, off-again relationship with the world of what my editor dubbed “White Collar PEDs,” or productivity-enhancing drugs. Though I’d never heard the phrase before, I knew immediately what he was talking about—the seemingly infinite profusion of supplements and “nootropics,” prescription “study drugs,” and illegal or semi-legal drugs that have become popular among young professionals seeking not to get high, but to optimize their brains and bodies for work.

Every year, there’s a drumbeat of worried think-pieces about the rise of these substances. But it’s no real mystery about their popularity. The human mind does not want to spend eight hours or more per day planted in front of a computer screen, when there are far more inherently interesting activities it could be dedicating its attention to. Learning to suppress these impulses and knuckle down anyway is, of course, the work of civilization, and yet our minds nonetheless frequently revolt. We feel bored, tired, distracted; we are assailed with bouts of “brain fog,” in which we seem to be operating a standard deviation below our normal IQ; our brains keep us up at night with worries or misplaced energy, ruining our sleep and thus sapping our performance the next day. Nonetheless, we must work. And so we seek a little help.

I also have a bit of personal experience in this world. Back during my first few weeks of graduate school, a classmate, on hearing me express awe at the sheer amount of reading that was assigned every week, advised me that at least half of my peers were getting chemical assistance. He suggested I could make my life a lot easier for myself if I did the same. Roughly a week later, I was walking out of a local doctor’s office with a monthly prescription for Vyvanse, a “prodrug” that metabolizes into dextroamphetamine, the drug distributed to Allied bomber pilots during World War II. Now, it would be helping me tear through dry scholarly tomes on the concept of convivencia in Early Modern Spain.

Learning to suppress these impulses and knuckle down anyway is, of course, the work of civilization, and yet our minds nonetheless frequently revolt.

I spent most of the next five years, from ages 24 to 29, as a semi-regular Vyvanse user, taking 40mg a day anywhere from one to five days a week, depending on how much work I had. I’ve read plenty of confessional essays from other writers about their addictions to prescription stimulants, but in my case, there were no horror stories. The drug worked well for its intended purpose, which was to help me override my natural tendencies toward sloth and forgetfulness and do whatever it was I needed to do. But it came with enough off-putting side effects that I never reached the point of taking it every day. When the pandemic hit in 2020, I’d just moved cities and had not yet found a new doctor who I could ask to renew my prescription remotely, so I just stopped taking it and figured out a way to cope.

Still, ever since ditching the Vyvanse, I’ve suffered at times from my own absentmindedness and reluctance to deal with the sort of low-level administrative tasks necessary for modern life. I’ve often wondered if I should go back and experiment with a lower dosage, or if there was some alternative drug or biohack that could help me answer all of my emails on time while also allowing me to sleep. So, offered the opportunity to test-drive as many substances as I could get my hands on, gonzo style, I figured—sure, why not?

Vyvanse

The brand name for lisdexamfetamine, a prodrug that, once ingested, slowly converts to dextroamphetamine, one of the active ingredients in Adderall. Originally developed as a longer-acting and less easily abused alternative to dextroamphetamine, lisdexamfetamine is now the third most commonly prescribed stimulant in the United States, according to the DEA, with around 15 million prescriptions dispensed in 2023. With insurance, a 30-day supply of Vyvanse usually runs around $50.

Given my prior experience with the drug, I assumed I’d be able to briefly get back on it for the purposes of this article. Back in 2015, I’d found the process of getting Vyvanse incredibly easy. I went in for one appointment, took a questionnaire, and walked out with a prescription. Once I was in the system, a renewal was only ever a phone call away. Now, a decade later, it was nearly impossible. I hadn’t had an active prescription in more than five years, so no provider I spoke to was comfortable writing me one without a new evaluation. 

I picked a doctor near me, who had me fill out an adult ADHD self-diagnosis test, which found “symptoms highly consistent with ADHD in adults,” and that I warranted “further investigation.” The “investigation” turned out to be an exploratory interview with a nurse—in which I was honest, and which also ended with the conclusion I likely had ADHD—and then a battery of tests, one measuring my ability to press a spacebar in response to patterns of beeps and silences, the other measuring my ability to press a spacebar in response to letters flashing across my computer screen. I apparently passed these, because in my follow-up meeting with the nurse, I learned that my beep-test skills were so magnificent that I was not eligible for a stimulant prescription. Instead, the nurse offered me Strattera, a non-habit-forming norepinephrine reuptake inhibitor that some patients found helpful for managing the symptoms of ADHD. But more on that later.

Thwarted by mental health gatekeeping, I reached out to friends. One offered me a few tablets of dextroamphetamine, which I accepted, and which I’ll review in a moment. But eventually, after about a week of searching, I was able to procure two 30mg Vyvanse, a little less than my old dosage. I waited for a day when I had a particularly heavy workload, set myself an early alarm for 5:30am, and popped the first of the pills before drifting off for an extra 30 minutes of sleep, a trick I’d learned back in the day. When you’re on these drugs, you need all the sleep you can get. 

Reader, don’t let anyone ever tell you that Vyvanse doesn’t work. Normally when I get up in the morning, I’m stiff and bleary-eyed, and remain that way until sometime between my second and third cups of coffee. With the Vyvanse in me, I woke up ready to go. What had seemed, the day before, like an overwhelming pile of tasks all of a sudden seemed easy; the day stretched out before me like hundreds of miles of empty highway. Within the first hour, I’d answered several emails I’d been putting off, made an extensive outline for the newsletter I wanted to write later that afternoon, and then folded and put up some laundry during a five-minute break that I normally would have frittered away on social media. By 10:30am, a time when I’m normally just getting started, I was all but finished with my formal job responsibilities for the day and had begun digging into random tasks I’d been putting off for months—filling out insurance forms for healthcare reimbursement, filling out forms to register my car in New York City, pruning my personal email inbox of clutter from endless newsletters and promotions. After another hour, I’d finished with that, and began assembling a reading list for a longer-term project I was working on. Then I started fiddling with my investments. It was like my work day had been extended by six hours, without the loss of any free time.

What’s more, I felt great, at least during the first half of the day. Due no doubt to the hypernormal amounts of dopamine flooding my brain, I was confident, motivated, and easily able to push aside the sort of ambient distractions that normally sap my productivity. Instead of my normal post-lunch crash, I simply plowed ahead and did more work. The euphoria started to taper off a bit by the late afternoon—I no longer felt excited to be working, and I began to notice that I was grinding my teeth—but I remained alert. When I finished, I went to the gym to burn off my excess energy, which proved almost impossible to do. I ripped off five sets of heavy squats, an exercise I’d been avoiding since a sciatica flare-up earlier in the year, and then, not yet tired, did a whole battery of split squats, teardrop squats, hamstring curls, and back extensions, finishing off with a mile on the treadmill. I thought to myself, I made a horrible mistake ever going off this drug.

Vyvanse has a smooth come-up, cresting into a peak that lasts several hours, followed by a long and slow taper during the latter half of the day. ‘Dexy’ felt more like railing a line of cocaine.

By nighttime, however, I was starting to remember why I had. The euphoria had now completely worn off and I felt a sort of dull mental fatigue, but I couldn’t really relax, either. It was like my brain wanted to start shutting down but was being artificially prevented from doing so. I tried watching TV with my fiancée, but I became impatient with the slow pace of the medium, and instead decided to read, which at least gave me more to focus on. My normal bedtime, 11pm, passed without the wiry feeling going away, and I began downing as many sleep supplements as we had in our apartment—magnesium glycinate, L-theanine, glycine, chamomile tea, melatonin. I tried to go to sleep around midnight, but realized after 15 minutes it wasn’t going to happen and went into the living room to read on the couch. I read about 100 pages of A Bright Shining Lie before making another attempt, this time well past 2am. I must’ve eventually fallen asleep, because next thing I knew I was waking up on the couch, with my 7am backup alarm blaring and feeling like I got hit by a truck.

Under the general principle that what goes up must also come down, I felt like dogshit the next day. Sleeping in was not an option, and I considered simply taking my second Vyvanse pill to power through the day. But I feared the prospect of yet another insomniac night and figured that living through the hangover was more journalistically honest. So I decided to soldier on. My focus felt shot—even more so than in normal low-sleep situations—and I found myself stumbling through my morning routine, initially trying to make coffee without putting water in the machine and then accidentally setting my phone down in the refrigerator while taking out my orange juice. My knees and lower back ached from overtraining them the previous day, and my jaw hurt from the teeth-grinding, which I assumed had continued throughout the night. I had a bad case of brain fog and was not motivated to do much of anything.

Strattera

This one will be brief.

Generic name atomoxetine, Strattera is a selective norepinephrine reuptake inhibitor initially developed by Eli Lilly to treat depression, but later approved as an ADHD treatment when it was found to be ineffective for its intended use. Strattera is far less commonly prescribed than Adderall, Vyvanse, or Ritalin—4.3 million prescriptions were dispensed in 2023, per the DEA—but may be favored for patients with a history of addiction, due to its low potential for abuse. It’s also cheap; with my insurance, a month’s supply of Strattera cost less than $10. 

Strattera is what the doctor gave me when I passed the ADHD beeping test, and it’s sort of like a diet Vyvanse. Like Vyvanse, it floods the brain with norepinephrine, improving energy and focus, but without the sugar, i.e. the dopamine. Like most diet products, it sucks.

In a narrow sense, the drug worked for me. For about four hours after taking it, I felt a small but noticeable improvement in my ability to focus. But without the dopamine, I had no real desire to do so. If anything, working felt worse on Strattera than sober. I wasn’t distracted by my phone, and I could grind for hours without taking a break. But the subjective experience was of a grim death march through the minor annoyances of email-job life, Sisyphean in its pointlessness. I felt like a character in Office Space. 

In my experience, a drug’s hangover is usually roughly proportionate to the high it delivers, with mild drugs producing correspondingly mild comedowns. Not so here, which is why Strattera gets my vote for the worst substance of all time. The active phase of the drug was moderately effective and mildly unpleasant. After the four-and-a-half-hour mark, though, things got genuinely miserable. The first symptom was a feeling of crushing hopelessness, like being thrown back into the worst breakup of your life. Far from being able to focus on work, I was suddenly ruminating on every anxiety tucked away in any back corner of my mind.

The overwhelming fatigue that came shortly afterward was thus something of a relief. I had to splash cold water on my face to wake myself up to finish the work day, then realized I was up for neither exercise nor cooking. I laid on the couch, ordered a burrito on UberEats, and went to bed at 8pm with the sun still up, no longer able to sustain focus on the television, let alone anything “productive.” When I woke up the next morning, I threw the rest of the pills in the trash.

Dextroamphetamine

Basically like Adderall but stripped of levoamphetamine, a less potent amphetamine isomer that helps to smooth the overall effects of the drug. Dextroamphetamine, or “dexy,” has been around since the 1930s, and was issued to US bomber pilots in World War II to help keep them awake on nighttime missions. It’s still around today, but far less common than Adderall or Ritalin (methylphenidate). Around 915,000 dextroamphetamine prescriptions were dispensed in the United States in 2023, according to the DEA. 

Given that this is the drug that Vyvanse metabolizes into, I was expecting it to be more similar to the Vyvanse experience than it was. There was certainly a family resemblance, but the best way I can explain the difference is that while Vyvanse feels like a real prescription drug—something that’s been designed in a lab by scientists to boost your energy and attention—dextroamphetamine feels like a street drug, something that teenagers would take to get high. 

My friend had given me two 10mg pills of the stuff, which I took on two separate days, spaced about two weeks apart. On the first day, I took it in the morning before heading into my office, hoping it would help me drown out the distraction of being around coworkers. It had almost precisely the opposite effect. Vyvanse has a smooth come-up, cresting into a peak that lasts several hours, followed by a long and slow taper during the latter half of the day.

“Dexy” felt more like railing a line of cocaine. Riding the subway into Manhattan, I noticed that my palms were sweating and my heart was racing, and when I arrived in the office, sitting down and staring at my computer was the last thing on my mind. I wanted to talk to people, to listen to music, to leave the office and walk around, to get a beer at lunch. I did all of these things but the last one, because by lunchtime, I hadn’t accomplished a single thing I’d set out to do that morning. By 1pm, I was in the midst of a hard crash, which I used to actually get my work done. 

I slept fine that first night, which made me overconfident in my second go-round. Figuring that the effects of the pill had fully worn off after about six hours, the next time I took a pill, I did it around three in the afternoon on a day I was expecting to have to stay up late to meet a deadline. I did have to stay up late, and I ended up filing my story around 1am. But the dexy kept me up for another three hours after that, which included a frightening episode of tachycardia around 3am in which I briefly considered waking my fiancée and telling her to drive me to an emergency room. The episode subsided and I eventually fell asleep, but I figured that was a sign I should end any experimentation with dexy, which hadn’t helped much with work anyway.

Modafinil

Unlike the other drugs on this list, Modafinil is not primarily an ADHD treatment. It’s a non-amphetamine stimulant and “wakefulness-promoting agent” developed in France in the 1980s as a treatment for narcolepsy, but its current claim to fame is as the “upper” of choice for the US Air Force to manage pilot fatigue on long missions (it’s also frequently prescribed for night-shift workers). Prescription modafinil is generally cheap with insurance, but the variant I bought—a supplement containing adrafinil, a closely related substance—cost $40 for a jar of 30 pills. 

Modafinil was not on my radar for this article until I chanced across a series of posts from an X user calling himself @modaminister, who appeared to be Sebastian Campos, the co-founder of an energy drink company called Adrafül. Campos was hawking his company’s signature energy drink, “The OG,” which promised “enhanced focus, extended-release energy, and no crash” for members of “the productive classes” who wanted to experience an “uninterrupted flow state.” What wasn’t clear on the website was how Adrafül worked so well; the website merely stated that it included a “low dose of caffeine and nootropics,” and a search for “Adrafül ingredients” led me to a company page that purported to explain the product’s effect with reference to its specific ratio of caffeine and L-theanine. On X, however, one user pointed out that the ingredient list included adrafinil, which metabolizes into modafinil in the body.

While it’s almost certainly illegal to sell an adrafinil energy drink—adrafinil is unscheduled, but the Food and Drug Administration considers it “unlawful” to include it in food—I decided I should probably give it a whirl. Unfortunately, Adrafül remained sold out for the entirety of the time I was working on this article, but I managed to find an online nootropics store that sold me an adrafinil-containing nootropic supplement. I vaguely wondered if every successful nootropic worked by dosing unwitting customers with precursors of a Schedule IV drug. 

The adrafinil supplement, however, was quite effective. I took it daily for about a week, and while I wouldn’t say I achieved hours of effortless productivity or uninterrupted flow state, it did help me feel wakeful in the morning and to power through my typical post-lunch crash. There was no real high or euphoric feeling, as with Vyvanse, nor was there the terrifying depression and crushing fatigue of Strattera. It gave me a “clean” feeling, not quite as good as the natural energy that comes from good sleep and exercise, but a better approximation of that than the other drugs. Tolerance built quickly, though, with my Friday dose giving me considerably less energy than my Monday dose had. I was also starting to experience persistent dull headaches by the late afternoon, which was apparently a common side effect. It was irritating enough that by the end of the week, I was ready to go back to my normal “nootropic”—caffeine.

Zyn

The meme; the legend. This, too, will be brief.

Zyn is the original brand of smokeless tobacco pouches, introduced by a Swedish company, Swedish Match, as a tobacco-free alternative to snus in 2016 (its major competitor, Velo, is also Swedish, though both companies are now owned by international tobacco conglomerates). In the United States, Zyn is sold in tins of 3mg or 6mg pouches, though the European version of the product—which I purchase from my local Yemeni-owned bodega in New York City—also comes in 9mg, 11mg, and 14mg varieties. Retail, in New York, a tin costs around $9—up from $5–6 only a few years ago.

I am well aware that some people use Zyn and other nicotine products as a “PED.” Technically, as even health nuts like Stanford’s Dr. Andrew Huberman attest, nicotine is a “nootropic” with cognitive-enhancing properties, allegedly improving focus, attention, working memory, and processing speed. Anecdotally, I’ve heard of programmers who will stash away nicotine pouches the way normal people might hide away a spare Adderall, i.e. as a crutch to help get through particularly intense periods of work.

For me, however, 6mg Zyn—rising to 9mg in times of crisis—has become a necessity akin to drinking water. I no longer know what Zyn “feels like,” per se, since I only feel its absence, in the form of scattered attention, forgetfulness, and low-level irritability. When I am on deadline or otherwise swamped with work, I rarely go 10 minutes without a pouch in my mouth. Half-hearted attempts to quit or moderate my usage are, inevitably, thwarted by periods of high stress, when I return to the Zyn lest I punch a hole in the wall or fling my MacBook through an open window.

The Wolf of Wall Street (2013)

If you’ve ever poked around in the world of nootropics and productivity enhancers, you’ll know that the substances themselves are only one part of a broader universe of “alternative health” and holistic self-care practices designed to make us fitter, happier, and more productive. These are the sorts of things that periodically bubble up on The Joe Rogan Experience podcast and were briefly popularized by Huberman, before he was taken down a peg by New York magazine for simultaneously keeping several girlfriends in different states.

You know the sort of thing I’m talking about: saunas. Cold plunges. Sunlight first thing in the morning. Dopamine fasting. Real fasting. Breath work. Breath work (Wim Hof method). Nose breathing. Taping your mouth shut at night. Strength training. Flexibility training. Eliminating seed oils. Fixing your gut health. Fixing your testosterone. Learning jiu-jitsu. 

Indeed, in much of this world, the PEDs are frowned upon as a sort of shortcut to enlightenment. Sure, stimulants and nootropics can offer a temporary boost, but you don’t want to be reliant on popping a pill every morning to get through your work day, especially when similar effects can be achieved through more natural methods. I naturally incline toward this view, and none of the drugs I tried seemed like the sort of thing I’d want to take every day, especially since—thank God—I’m not a junior investment banker working 90 hours a week at a job I hate.

I am not a regular Huberman listener, and upon doing a little digging, I realized that his recommendations, laid out via hundreds of hours of podcasts and then debated at length by his devotees on forums, were going to be too complicated for me to master in time for my deadline. Instead, I found a dumbed-down version of Huberman’s basic protocols on Reddit, and decided to combine those with the recommendations from Anna Lembke’s Dopamine Nation, a book I’d frequently heard cited in the world of bro wellness. For two weeks, I’d stick with my normal supplement routine in the morning: creatine, L-theanine, methyl B-12, and coffee. Every day, I’d follow the Hubermanian advice to go outside immediately after waking, so that natural sunlight could help regulate my circadian rhythm. I’d abstain from alcohol completely during the week, and try to abstain as much as possible from refined sugar, while getting the sugar I needed from fruit and dairy. I would tightly regulate my phone usage, and my screen time generally, and try to avoid all screens for at least an hour before bedtime, which would be consistently set at 11pm. I would exercise every day, use the sauna at my gym, and follow it up with a cold shower. I’d sleep every night with a breath strip on my nose. 

I no longer know what Zyn ‘feels like,’ per se, since I only feel its absence, in the form of scattered attention, forgetfulness, and low-level irritability.

After an initial adjustment period, I found most of these recommendations easy to follow—with the exception of the cold shower, which remained excruciating no matter how long I’d spent sweating in the sauna immediately beforehand. The major problem, I soon realized, was going to be regulating my screen time. I had not previously thought of myself as a particularly screen-addled person—I have not had Facebook for nearly a decade, I check Instagram only about once a month, and if I spend more than three minutes on TikTok I feel like I’m having a schizophrenic break. And yet, as soon as I tried to impose a rule on myself that I could not check my phone during the work day or look at screens after 10pm, I was forced to confront the fact that I now live the overwhelming majority of my life on screens. 

I work on screens—laptop, monitor, iPhone—and, as my day job is mostly remote, all of my work-related communication is mediated through them. There are the emails and Zoom calls, of course, plus a work group chat that, due to the time-zone differences of its members and the unconventional sleep habits of Tablet’s editorial staff, is active virtually 24/7. I keep up with my family through a wide array of chats, with largely-but-not-entirely overlapping members—Dad, Mom, and brother to discuss holiday plans; Dad, brother, and cousin to talk sports; Mom, aunts, and uncles to share updates from this or that branch of the family. Socially, my closest friends are scattered up and down the East Coast, and for most of the year, we keep up via a long-running group chat that, on a particularly active day, will have 1,000 messages or more. 

My drug of choice, however, is X—though using it doesn’t really feel like much of a choice. In my day job, I’m the editor of a daily politics-focused newsletter, where my duty is to provide readers with a more or less comprehensive digest of everything they need to know from the day’s news. On a normal day, the first thing I do when I wake up in the morning is check X. The last thing I do before going to bed is check X. I browse X while I sip my morning coffee. Throughout the day, I take breaks from writing to see if anything new has hit X that I might need to incorporate into my writing. After I’m done for the day, I keep monitoring X throughout the evening to get ahead of the next day’s stories. When I try to ignore X and source my writing from the “mainstream” press, I inevitably find that The New York Times or The Wall Street Journal has omitted some critical piece of context without which it is impossible to truly understand the story. If I take too much time away from X—on weekends, for instance—I inevitably find I lose the thread of the news, and have to work doubly hard on Monday to catch up. 

For the first week of my experiment, however, I did a reasonably good job suppressing my desire to scroll. I read physical books and magazines at night. During breaks in my work day, I’d roll out a yoga mat and do stretches rather than check my phone. I monitored X in time-limited increments—10 minutes per hour, max—and only from my laptop during normal work hours.

I felt fantastic. For the first time in what felt like a year, I got at least eight hours of sleep five nights in a row, after regularly getting less than seven. I’d read in Lembke’s book that cold showers promote a release of dopamine, and I used that knowledge to force myself through at least a minute of one each day, even if every time, my body reacted to the shock by sending panic signals to my brain that I was about to die. Five minutes after stepping out, I felt incredible. Having heard somewhere that training while nose breathing can improve testosterone, I took to going on jogs with my mouth closed, running for as long as I could breathing only through my nose. Whether it was the placebo effect or not, that felt good, too. Also for the first time in what felt like a year, I went nearly a whole week without once losing my keys inside my apartment.

By week two, though, events intervened to ruin my little holistic bro science Eden. Late one night, as I was coming home from dinner with my fiancée, news broke that Israel had attacked Iran. I’d been covering Middle East news on a near-daily basis for almost two years now, and I felt I had a professional duty to “monitor the situation,” as X users described their compulsive need for by-the-minute updates on the war. After a week and a half of minimal screens at night, I soon had something of a war room set up in my living room—the news on the TV, my laptop open to a dozen feeds at once via X Premium, and my phone pinging with hundreds of updates from various group chats. I even stuck in a single AirPod to listen to a Middle East-focused X Space with one ear, while listening to the cable news broadcaster with the other. The first night of the war, I blew straight through my 11pm bedtime and stayed up until nearly three in the morning. Four hours later, I woke up and, skipping my morning sunlight, immediately got back on my screens to see what had happened while I slept. Things continued pretty much like this for the rest of the war, even as I spent the last week of it at the Paris Air Show. Jet-lagged in a foreign country, I was still glued to my phone, trying to gauge from social media reports the extent of the US bombing damage to the Fordow nuclear facility. 

It was of course my fault that I fell off the wagon in the way I did; with better discipline, I feel I could have maintained a semblance of the routine that had seemed to be working so well for my health and productivity, and I fully intend to reinstitute such a routine as soon as I’m able. After all, there was no reason I needed to read about Trump’s overnight Truth Social posts immediately upon waking rather than 20 minutes later, after I’d had my morning dose of staring into the sun.

At the same time, the experience was a good reminder of why people reach for the PEDs in the first place, rather than simply “fixing” one’s “underlying issues.” I’m in my early thirties and don’t yet have children, which is what allowed me to even attempt to set up such a neat little system in the first place. Even with those advantages, things happened that I simply had to pay attention to at inconvenient times and in suboptimal ways for my health, out of a sheer sense of professional responsibility. By the time I sat down to put the finishing touches on this essay, I was once again tired, jet-lagged, and really, really wishing I’d remembered to pack the rest of my adrafinil.

Park MacDougald is editor of The Scroll, a daily afternoon newsletter from Tablet Magazine, and a fellow at the Manhattan Institute.

Kevin Kelly isn’t known for one “big thing,” and doesn’t aspire to be. He’s as intelligent, hard-working, ambitious, and prescient as history’s most iconic entrepreneurs—only without any interest in building a unicorn himself. Instead, in his words, he works “Hollywood style”—in a series of creative projects. What follows is a sampling of his life’s work.

Kelly was an editor for the Whole Earth Catalog in the early 1980s, helped start WELL, one of the first online communities, in 1985, and co-founded WIRED magazine in 1993. He’s written a dozen books and published hundreds of essays on topics from art to optimism, travel, religion, creativity, and AI (even before it was a thing). Kelly rode a bicycle across the United States in his 20s. He was Steven Spielberg’s ‘futurist advisor’ on Minority Report, and the inspiration behind the famous “Death Clock” on Futurama, after the show’s creator Matt Groening caught wind of the Life Countdown Clock Kelly keeps on his computer desktop. He organizes tightly curated group walks across Asia and Europe, regularly covering ~100km in a week. He sculpts, draws, paints, and photographs. And he’s a longtime friend and collaborator of Stewart Brand (whose famous line, “Stay hungry, stay foolish,” Steve Jobs quoted in his iconic commencement address at Stanford).

To encourage long-term thinking, Kelly is helping build a clock into a mountain in western Texas that will tick for 10,000 years. Brian Eno and Jeff Bezos are active collaborators. He’s a born-again Christian. He’s been married to his wife, Gia-Miin, for 38 years, and they have three children together. He was pivotal to a fringe-turned-mainstream movement to identify and catalog every living species on earth (now owned and operated by Smithsonian). He was early to think and write about the quantified self, which gave rise to products like Fitbit, Strava, Apple Watch, Eight Sleep, and the Oura Ring. Kelly’s idea of “1,000 true fans” practically christened the creator economy with his 2008 insight that “if 1,000 people will pay you $100 per year, you can gross $100k—more than enough to live on for most.”

The people who become legendary in their interests never feel they have arrived.

Kevin Kelly

Naval Ravikant has called him a “modern-day Socrates,” Marc Andreessen has said that “everything Kevin Kelly writes is worth reading,” Eno called him “one of the most consistently provocative thinkers about technology and culture,” and Ray Kurzweil said that “Kevin Kelly understands the direction of technology better than almost anyone I know.”

Kelly’s Hollywood style of working has always resonated with me; it’s the way I aspire to work and largely have since starting my career. Yet now, 15 years in, I’ve become self-conscious about it. Working in Silicon Valley will convince you that starting a company with its sights on unicorn status is the only possible way to make an impact, and the only work worthy of an ambitious individual. 

Kelly is a cheerful and enterprising repudiation of that path, and I didn’t get very long into my interview preparations to realize that I wasn’t only writing about a personal hero; I was seeking a way to make peace with my own professional choices. After a day together, I realized that my pilgrimage to meet the man in his element might also grant permission to others in our line of work who are interested in charting a different course to impact.

I started my career at Google selling AdWords to small businesses, and finished my first quarter as the number three seller in North America. Professional opportunities immediately unfolded—early nods for management, trips to global offices to present my “best practices,” my face on slides next to impressive metrics, and attention from more senior leaders.

It’s hard to say why none of that seemed very interesting, but it didn’t. What I did like was starting a campaign to rename the conference rooms and helping my coworker launch his internal content series, G-Chat with Charleton, in which he would interview Google executives while sitting with them in a two-person snuggie. I had earned myself a ticket to the fast career track at one of the coolest companies in Silicon Valley, but climbing the corporate ladder just wasn’t for me.

So I spent the next 10 years chasing what seemed most fun. After 14 months at Google, my work bestie, Jenny, and I left Google together to give the startup thing a try. We went to a mobile gaming company where I learned to make my way around spreadsheets, play Magic: The Gathering, and cash in on a blockbuster ‘pet hotel’ game. Eighteen months later, it was a six-person startup that was known as “the black sheep of Y Combinator.” In my free time, I coached a JV high school soccer team, volunteered at Dandelion Chocolate (all that working on software made me want to make something with my hands), and finished writing a novel.

My resume of under-two-year gigs spooked recruiters, except for one at Stripe. “We’re impressed by how much ground you’ve covered,” was the backhanded compliment I got. I started on the Account Management team in early 2015.

I spent nearly five years at Stripe, but the lily-padding continued—only this time it was all under one roof. A year into my tenure, I was given the choice between management or a nebulous role focusing on projects that would impact company culture. Like evolving our tradition of work anniversary celebrations, standing up company planning, establishing Stripe as a carbon-neutral company, getting non-developers to participate in our annual hackathon, defining our version of the “bar raiser” interview, and printing and distributing a book (which eventually became Stripe Press). With very little pressing, I learned this nebulous role had emerged from the growing pile of projects that the former McKinsey consultants on the Business Operations team were avoiding.

Guess which role my friends and parents thought I should choose? Guess which one I chose.

Kelly would say it’s good to have an “illegible” career path—it means you’re onto interesting stuff.

I started to take pride in this “cool girl” approach to work. I joked about having never been promoted, but could feel my scope, impact, and relationships with colleagues growing. I remember rejecting a (well-meaning) manager’s suggestion to build out a five-year career plan. I scoffed at people who cared about titles, did things for money, and had professional headshots on their LinkedIn. I mocked MBAs, bragged about “staying off the org chart,” and being good at “giving away my LEGOs.” I became the person you asked to have a coffee with when you wanted to quit your job and do something weird. Once I mentioned “enjoying working in the wings,” and a (well-meaning) executive suggested I “keep that to myself if I wanted to be seen as a leader.” I ignored the advice.

And then, I’m not sure when the switch flipped, but I started to have a sinking feeling that I had it all wrong the whole time. I looked around and felt I was being outpaced by my colleagues—specifically by the MBAs and the people who chased titles, promotions, money, and building teams. And it wasn’t just a vanity thing. They genuinely seemed to be focused on bigger, more interesting problems. And they were having more impact. They were mentoring young talent, influencing top lines and bottom lines, and had their fingerprints on all kinds of cool industry-recognized work. They seemed to always have invitations to exclusive gatherings and job offers in their inbox. Several started companies, and rumor had it that some had term sheets before investors even opened their decks. I didn’t only feel jealous of their work; I felt unqualified to do it. That stung.

I started to reflect on my own trajectory with fear that it didn’t mirror my ambition, work ethic, or deep care about the role of work in a life. Had I pointed my ambition in the wrong direction? What did I have to show for all my effort? Had I made some irreversible, unforced error with my career? How much money had I left on the table? Would the people I respected respect me back for much longer? Despite working my butt off for a decade, I had no expertise and no line of sight into where I was going. I felt immature for placing such a high value on “fun” and “bouncing around,” and full of regret about not picking a lane (or even better, a ladder). It had become hard to explain what I was good at—most importantly to myself. My sister had recently made partner at a prestigious law firm, and it seemed easier for my parents to be proud of her than of me. I couldn’t really blame them.

Kevin Kelly would say it’s good to have an “illegible” career path—it means you’re onto interesting stuff. But I wasn’t so sure anymore.

I pull up to Kelly’s Pacifica, California studio—the last house at the very edge of Vallemar off Route 1. It’s a big, barn-looking structure pressed up against a steep hill, which is covered in wild flowers and towering trees. It was overcast and smelled like the ocean and eucalyptus. The only way I knew I’d come to the right place was the very small sign on the door that read “kk.org,” on which I’ve spent dozens of hours over the years.

Stepping inside, I felt like I’d time-traveled back to the early 1990s and entered my little brother’s dream bedroom. There were huge LEGO towers, K’nex sculptures hanging from the ceiling, and a massive wall of books spanning two floors. Most of the books were faded from use or sunlight, the dust jackets bent, and they were all stacked and tilted in a way that suggested they’d actually been read. There were knickknacks piled up everywhere, and even more haphazardly tucked into bins or captured in jars.

It was hardly the image of a futurist’s office, and in sharp contrast to the Japandi workspaces you see going viral on X. Yet despite the sheer amount of stuff lying around in Kelly’s haven, nothing appeared like junk. Every object seemed to vibrate with meaning, begging you to ask, “What’s this for?” or “Where’d you get that?”

As I was scanning the lower rungs of the bookshelf, Kelly materialized on the indoor balcony and invited me upstairs to talk. He was wearing socks that were way too big—the spaces where his toes should have been were empty and flopped around in front of him—and his pants were stained from actual paint (i.e., not in the Rag & Bone way).

As I walked up the stairs, I asked him what the oldest object in the studio was, but he immediately deflected. No interest in nostalgia from the futurist, I guessed.

I slowed down as I walked by the second-floor wall of knickknacks and started scanning. Kelly caught me doing so, pulled some leather doohickey about the size of my hand off the shelf, and handed it to me.

“What do you think this is?” he asked. I twirled it around and desperately wanted to answer correctly, but figured that wasn’t the point. Still, I fumbled around nervously and couldn’t even eke out a guess. Probably sensing my anxiety, Kelly jumped in. “It’s a leather cap for an eagle.” He got it in Mongolia where there’s a tradition of using eagles to hunt, he explained. Now things were feeling looser. I got the feeling I could pull this thread about the Mongolian eagles or get another story. Kelly made my decision for me when he directed my attention to a small jar containing a little creature’s bones. “This is from a bird that flew into that window,” he said, pointing to a window over his desk. I nodded along with enthusiasm. “I freeze-dried them!” he said proudly.

We strolled over to his desk, where he asked me to try to lift a small but dense ball that was sitting on the floor next to it. I could barely get it above my ankle. Kelly told me it was made out of tungsten. “It has a similar density to gold,” he continued. “Now every time you see a criminal in the movies running away with a bag of tungsten, you’ll know how unrealistic it is.”

Greatness is overrated. It’s a form of extremism, and it comes with extreme vices that I have no interest in.

Kevin Kelly

It was so much fun connecting with Kelly over these random little objects—I felt I was learning something about him I couldn’t through his books and blog posts; like I was getting to the real spirit he brings to his life and work. But before I could think too much, we were onto the next.

There was a train track running along the wall, just below the ceiling, and I asked if it worked. I half-expected him to yell, “Alexa, start your engines!” Instead, Kelly walked over to his desk and picked up a controller and turned it on. Nothing happened. He replaced the batteries, gave the controller a smack like it was a Nintendo 64 cartridge, and tried again. The train, looking like something my dad might have built at the model shop down the street in the 60s, immediately started choo-chooing around the room. Kelly stood and smiled proudly again as he watched it go. Eventually we took our seats next to his desk to talk. 

I started off by asking him whether there is a unifying theme to his seemingly diffuse life’s work, which has included old-school magazines and books, bleeding-edge technology, conservationism, photographing Asia, and teaching. “Following my interests,” he said simply.

It sounded awfully cutesy for someone so accomplished. I said that there is an idiosyncratic magic to the way he follows his interests, which is that they’re not just an input; Kelly turns his interests into an output that he can share with others. When I asked if I was onto something, I learned that Kelly doesn’t think in outputs. For him, doing is part of learning. “I don’t really pursue a destination,” he said. “I pursue a direction.”

I asked him the difference between “following your interests” and being scatterbrained or having shiny object syndrome, like I sometimes worry I do. “The people who become legendary in their interests never feel they have arrived,” he said. When he talked about the power of passion and obsession in that process, I asked him if passion is enough. “Enough for what?” he asked, somewhat rhetorically. He had an impression of what I meant. “I think one of the least interesting reasons to be interested in something is money,” he said, and cited Walt Disney. “We don’t make movies to make money. We make money to make more movies.”

Money isn’t actually what I meant, but I appreciated that he took the conversation there. I let the silence hang for a minute before he continued. “What I’m talking about is taking your interests seriously enough to have the courage to stay moving. You can give stuff away. You can abandon things. You can tolerate failure because you know that tomorrow there is more.”

I asked Kelly about the tradeoffs of focusing on a single thing if you want to be great (which is what I had been getting at before). “Greatness is overrated,” he said, and I perked up. “It’s a form of extremism, and it comes with extreme vices that I have no interest in. Steve Jobs was a jerk. Bob Dylan is a jerk.”

The way Kelly approaches work differently was starting to come into focus.

Accounts of people pursuing their life’s work often include phrases like “maniacal focus” or “relentless pursuit.” I hear investors say they’re looking for founders with “a chip on their shoulder.” Facebook’s iconic “Little Red Book” from 2012, which still serves as a pillar for peak tech culture, features a full-page spread that says “Greatness and comfort rarely coexist.”

A recent xeet from Reid Hoffman reads, “If a founder brags about having ‘a balanced life,’ I assume they’re not serious about winning.” Jensen Huang says he wants to “torture people into greatness.” When I was on the job hunt many years ago, an investor was pitching one of his portfolio companies by saying, with a wink, that the founder would do “whatever it takes to win.” I genuinely didn’t know what he meant by that, but it sent a shudder down my spine. Once I heard a serial founder say he started his second company “out of chaos and revenge.” I heard about another prominent CEO that looks in the mirror every morning and asks himself, “Why do you suck so much?” I read a biography of Elon Musk; he seems tortured. There’s some rumor floating around about how Sam Altman was so focused on building his first startup that he only ate ramen and got scurvy. According to Altman, “I never got tested but I think (I had it). I had extreme lethargy, sore legs, and bleeding gums.”

Compared to this, Kelly’s version of doing his life’s work seems so joyful, so buoyant. So much less … angsty. There’s no suffering or ego. It’s not about finding a hole in the market or a path to global domination. The yard stick isn’t based on net worth or shareholder value or number of users or employees. It’s based on an internal satisfaction meter, but not in a self-indulgent way. He certainly seeks resonance and wants to make an impact, but more in the way of a teacher. He breathes life into products or ideas, not out of a desire to win, but out of a desire to advance our collective thinking or action. His work and its impact unfold slowly, rather than by sheer force of will. Ideas or projects seem to tug at him, rather than reveal themselves on the other end of an internal cattle prod. His range is wide, but all his work somehow rhymes. It clearly comes very naturally for him to work this way, but it’s certainly not the norm. 

If this is a way of living and working that’s available to all of us, why do we fetishize the white-knuckling and pain?

I know I’m not the first person to have the brilliant idea that we can do better work when we like it. I know that the whole “find your passion” movement fell flat in its naivete. But I think somewhere along the way, the message about what it feels like to be great has become a bit perverted.

A few years ago, I forced myself to try and write down a professional goal. After several hours of forced meditation on the topic, all I could muster was “have a good day, most days.” And don’t get me wrong, by “good day” I don’t mean sitting by a pool drinking an Aperol Spritz. I feel alive when I launch something exciting, close a big deal, or build an elegant model. I enjoy the feeling of caring so much about something that it wakes me up in the middle of the night (it happened multiple times writing this piece). And yet, I imagined sharing my ambition to “have a good day, most days” in a job interview—and decided to keep it to myself, because it probably doesn’t speak well of me.

But there I was, in front of a personal hero, whose most striking quality is that he seems to be having a nice day, most days. Why can’t we work and enjoy it? And I don’t mean in the masochistic sense.

I thought I was here to go deep on working Hollywood style, but as I sat there with Kelly in a room of what are best described as his toys, I realized that the most interesting thing about him is that he seems happy. At ease in the world and in his skin. I wasn’t there with Kelly for permission to work Hollywood style. I was there for permission to work with both ambition and joy.

If this is a way of living and working that’s available to all of us, why do we fetishize the white-knuckling and pain?

This shouldn’t make us defensive or self-conscious, but it does. I, like many others, want to be great. I want to feel commitment and camaraderie and work hard and be my best and impact top and bottom lines. But I don’t want to also feel tormented or be tortured into greatness or look in the mirror and wonder why I suck. But what does that say about me?

I want more role models like Kevin Kelly. People that proudly whistle while they work. Who have boundless energy and healthy gums. Whose enthusiasm is contagious. Who are well-adjusted and emotionally regulated. Who have solid relationships and happy families. Who are hungry and impactful and care deeply, without being jerks. And I want more people to talk about these qualities with respect and reverence.

I have never been a billionaire or built a unicorn, so I can’t speak with any conviction about what it requires. I won’t be eulogized anywhere important and no one 300 years from now will talk about what great things I did. But I want to live in a world where you can have an impact and be happy. Maybe that’s naive, but I’m sticking to it.

All of this occurs naturally to Kelly, and he doesn’t have complicated feelings about it. I’m hoping to get there myself by channeling him more. “The more you pursue interests,” he told me on the good day we spent together, “the more you realize that the well is bottomless.”

Brie Wolfson is the chief marketing officer of Colossus and Positive Sum.

This article features first-ever photos taken from inside Anduril’s R&D facilities in Costa Mesa, California. All photos by Ryan Young.

On a Saturday afternoon in April 2024, I was on the rooftop pool deck of a Marriott hotel, setting up radar equipment aimed above the Hollywood Hills in Burbank, California. My five-year-old son, still damp from swimming, darted around as I calibrated the system.

“What are you doing?” he asked, touching the electronics with wet hands.

“Tracking … flying objects,” I said, carefully moving his hands away from the sensitive equipment. “It’s a special radar that will help our drones find targets better.”

Working on a thousand-dollar radar that could potentially transform a landmark missile platform during a father-son weekend was fairly typical in those days. The technology that my son wanted to touch, and which other poolside guests gawked at, was a throwback to the AGM-114 Hellfire missile system from the 1960s—a simple direction finder that could be guided by a ground system that paints targets with radio frequency (RF) instead of lasers. If we could get this to work, we could reduce the cost of our Roadrunner system—a reusable, twin-turbojet, vertical-takeoff-and-landing microfighter—by 30x.

Between trips to the pool and Chick-fil-A, I eventually managed to collect enough data to prove the concept worked: We could detect aircraft at 10 kilometers with a thousand-dollar sensor. It was the kind of breakthrough that could change how we approached reusable weapons and low-cost solutions for air defense—an ongoing R&D project I remain consumed by.

It wasn’t company-sanctioned work. I was officially on family time, having left Anduril as SVP of Engineering the month before to start a robotics company, Physical Intelligence (PI). Yet Anduril was never just a job; it was part of my identity. My badge still worked, I continued on in an emeritus role, and I still spent around 15 hours a week working with the engineering team I’d helped build.

When I joined Anduril in the fall of 2018, I was employee #20, the company was valued at $250 million, and we had lofty, but hypothetical, ambitions of reinventing the defense ecosystem. Less than six years later, the 4,000-person, $28 billion company has deployed 30-plus products with thousands of fielded systems, and changed the arc of American defense technology. It’s worth looking back now at those years of explosive growth, in order to give other founders, engineers, investors, operators, and everyone else a glimpse of what zero-to-one at Anduril was actually like.

Team member welding in Anduril’s R&D prototyping shop.

I’ve always been drawn to the kind of science that translates into strategic impact, and to problems too dangerous to ignore. After studying at MIT, I worked on flood disaster relief in Pakistan, then became a founding engineer at a biotech startup developing affordable genome sequencing technology. When the company was acquired, I left for Tesla, where I worked on projects from the Falcon wing doors in the Model X to electromechanical architectures, autopilot sensors and advanced technologies enabling future vehicle platforms. It was cutting-edge work with one of the most innovative companies in the world, and I was genuinely happy there.

A coffee in July 2018 with Anduril’s founder Palmer Luckey changed everything. What was supposed to be a quick 30-minute chat turned into a six-hour conversation that made it impossible for me to go back to Tesla. These were the days when supposedly bleeding-edge work in Silicon Valley was still largely dominated by consumer apps and services. By contrast, the picture of the world that Palmer drew kept me up at night.

While the threat from China wasn’t yet front-page news, Palmer and his team had already recognized the need for better defense technology to deter a great power conflict and to maintain American hegemony. They understood that America’s technological edge in defense was eroding, and that traditional defense contractors were too glacial and bureaucratic to meet the challenge—a culture I’d previously witnessed firsthand (and had forever sworn off) during an internship with an aerospace manufacturer.

I was struck by the Anduril team’s strategy of developing defense products on its own dime and selling them off-the-shelf, turning the traditional business model of defense contractors on its head. I was also impressed by their choice to build the company in Southern California, deliberately removed from Silicon Valley. My coffee with Palmer came only a few weeks after Google canceled Project Maven, which would have assisted the Department of Defense with AI-based drone-footage analysis. When I eventually told colleagues at Tesla that I was leaving to join a 20-person defense technology startup working out of a hangar near Santa Ana airport, they looked at me like I was insane.

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When I joined the company in September 2018, we worked out of a small building at 3000 Airway, then expanded into hangar B8, which was adjacent to a dog kennel at the Santa Ana airport. There was no heating or air conditioning, just incessant barking. I claimed a closet that received a little heat through proximity to another part of the building. That became my lab.

On my first day, I found myself back at MIT, on stage beside Palmer, explaining our vision to skeptical engineering students confused why anyone would work in the defense sector. By the end of that same week, I was on the southwest border installing one of our pilot systems, a surveillance tower. It wasn’t the polished product defense contractors typically wait to unveil—in fact, our first tower was literally a telephone pole with a gaming PC housed in a weatherproof box, a pan-tilt unit normally used as stage lighting, with spikes on it to prevent bird shit from blocking the sensors. A lot of it came from Home Depot.

That makeshift tower, which we built on our own dime to prove what was possible, helped intercept nearly 1,000 pounds of marijuana and led to dozens of drug trafficking arrests—ultimately earning us a pilot program with Customs and Border Protection. It was primitive, but it worked, and reflected our approach: get to a minimum viable demonstrator, something that creates end-to-end capability, then iterate ruthlessly. By then I understood that Anduril would be the fastest, most intense environment I’d ever experienced.

A few days each week, we’d pile into vehicles and drive to our test site in Apple Valley—a remote California desert location where temperatures reached 110 degrees on summer days, then dropped to 30 at night. We stayed in the cheapest hostel-like accommodations we could find and worked 16- to 18-hour days in complete isolation from distractions. We operated out of dusty trailers with minimal equipment. If something broke, we couldn’t just order a replacement part—someone had to drive 200 miles back to Santa Ana, rebuild the component, then drive 200 miles back.

Brian Schimpf, Anduril’s co-founder and CEO, functioned as our chief engineer, with an intuitive understanding of how every component fit together. Brian shaped our strategy and had a remarkable knack for pulling together engineering pieces and connecting them to business outcomes. When obstacles appeared, the other founders would come up with a strategy to unblock the engineering so Brian could focus on solving technical challenges—like the time co-founder and COO Matt Grimm chartered a plane in order to fly oversized batteries across the country for a critical demo.

Our first tower was literally a telephone pole with a gaming PC housed in a weatherproof box, a pan-tilt unit normally used as stage lighting, with spikes on it to prevent bird shit from blocking the sensors. A lot of it came from Home Depot.

Even in those early days, the company was single-minded and self-selecting. No one cared about meetings or performance management or building a well-rounded company. We lived and died by our ability to quickly fire a “tracer bullet” through the heart of each problem, illuminating a clear path to the full solution.

It was a bad day when we’d be testing a quadcopter drone and it would crash a couple of kilometers from the takeoff zone. That seemed to happen most often at night or on weekends at the Capistrano Test Site (CTS), an environmentally protected zone of beautiful rolling hills, where we had to recover every scattered bolt by hand. At 2am, we’d be searching in the middle of the cactus brush for a drone that had fallen out of the sky.

We came up with creative solutions, like gluing glow sticks to the “birds” (our word for drones) so we could see them in the dark, or putting beepers on them so we could hear where they landed.

Anduril’s first surveillance tower, with spikes to prevent birds from relieving themselves on the sensors and gaming PC.

Early radio frequency (RF) chamber, purchased on eBay.

Like diamonds, all great products are born from heat and pressure. Consider the Tesla Model 3: The battery engineers pushed for maximum energy density; the chassis team insisted on minimal weight and thickness; and the safety team required uncompromising crash resilience. Each group had conflicting demands, yet this friction ultimately yielded an exceptional battery pack—powerful, efficient, and safe. No stakeholder was completely satisfied, but through that creative rigor and tension, something extraordinary emerged.

The same was true at Anduril, where an additional layer of pressure came from the international political reality. America’s adversaries evolve tactics in weeks, and the company had to operate with that same urgency. We couldn’t deliver solutions in years—we needed to prototype, test, and deliver in months or weeks. Each product therefore had to embody our chief working principles: move fast with purpose; question everything; take ownership; keep it simple; hold high standards; and design with deployment in mind.

One of the first products I worked on was our counter-drone interceptor, Anvil. This was important because the US government had spent billions of dollars on counter-drone technology with limited success—in some cases, they were literally training falcons (real birds in this case) to take down drones, or using Patriot missiles costing millions of dollars to destroy $500 quadcopters. The inefficiency was absurd, but the problem was serious. In 2018, a rogue drone shut down London’s Gatwick Airport for two days, and they were being used on the battlefield more and more.

We had a simple idea: What if we just used a quadcopter to crash into another quadcopter? Our first prototype used an Intel RealSense camera to look up and fly into targets. The approach was primitive—if the target moved, we’d miss—but at a bake-off at White Sands Missile Range, our system successfully intercepted targets about 40% of the time, while competitors had single-digit success rates.

The government customer was in shock, but to us, a 40% hit rate was nothing to dine out on. They quickly deployed our solution as a stopgap overseas, which created pressure to improve, because the system could fall short in the field: the cameras and sensors couldn’t reliably detect targets due to glare or clouds, and our guidance system was too basic.

We had three months to stop the bleeding, and I spent my paternity leave developing a 3D radar system. I realized we could leverage the same radar technology used in self-driving cars, build an RF frontend with a non-uniform antenna, and create our own algorithms for terminal guidance instead of collision avoidance.

In three months, we went from a 40% kill probability to knocking out 35 of 35 targets. There wasn’t a quadcopter you could throw at our mechanical bird that it couldn’t take out. We even hired one of the top First Person View (FPV) drone pilots to try to evade our system. Anvil caught the drone every time.

As Elon would often say at Tesla, “If the schedule is long, it’s wrong; if it’s tight, it’s right.” Speed was our weapon. Even our recruitment process reflected this: We’d openly talk to candidates from defense contractors and show them around our facilities, confident that we were moving faster.

“Our first prototype used an Intel RealSense camera to look up and fly into targets. The approach was primitive—if the target moved, we’d miss—but at a bake-off at White Sands Missile Range, our system successfully intercepted targets about 40% of the time.” Anvil V1.

Repairing the bistatic radar seeker with a soldering iron in the electrical engineering R&D lab.

We paired speed with another key principle: question everything. This meant engineering from first principles—breaking down every problem into physics, math, and operational reality, then building solutions from there.

In 2019, the US Air Force wanted to explore new solutions for detecting low-altitude cruise missiles—a critical capability with threats from Russia and China that could penetrate our borders. The official requirements for the Advanced Battle Management System (ABMS) program called for a radar with high azimuth and elevation accuracy and full hemispherical coverage, which would mean multimillion-dollar systems.

But no air defense radar manufacturer wanted to sell to us—some because we were a small no-name company, others because they wanted to capture the full value of delivering their own systems, still others because they saw Anduril as a potential future competitor. We needed to create our own solution, but traditional radar development takes years.

Instead of taking the Air Force’s brief at face value, we asked: “Why do they need hemispherical coverage? What’s the actual threat?” The primary concern was low-altitude cruise missiles coming across unprotected territory, which meant we only needed to cover the first few degrees above the horizon, not the entire sky.

We modified a $5,000 commercial boating radar—the spinning “candy bar” type you see on fishing vessels. Boat radars are designed to detect small objects far away on water, but not in the air. By modifying the waveguide assembly to create a narrower beam, we concentrated more energy in a specific direction and extended the range by about 10x.

When we showed up to the bake-off with our cheap modified boat radar mounted on a rickety welded truck, competing against traditional defense contractors with multimillion-dollar systems the size of a shipping container optimized for 360-degree hemisphere coverage, the other attendees laughed.

Yet we won. We understood what the customer needed to accomplish, but mostly ignored what they thought they wanted in their requirements. Our system could be scaled along any border as a true cruise missile detection network at a cost that was orders of magnitude lower than traditional solutions.

Inner guts of a tower assembly.

The author flying the Anvil V3.9.

Every project at Anduril had a directly responsible individual (DRI)—a single owner accountable for the outcome from end to end. Sometimes ownership meant taking extraordinary measures when the stakes were high.

One of the most dramatic examples arose at a critical test for a US defense customer of our V2P interceptor drone—a quadcopter that could fly at 150 miles per hour and intercept other drones with impressive accuracy. This was a billion-dollar contract opportunity that could transform Anduril from a border protection startup into a serious defense company.

V2P was an evolution of our Anvil system. After successfully developing Anvil to intercept small quadcopters, we discovered a much bigger threat emerging: larger Group Two and Group Three drones. The former are often used for intelligence reconnaissance and surveillance missions, and the latter with larger payloads and longer ranges. Countries like Iran were developing the Shahed series—massive drones that could carry substantial explosive payloads and fly kamikaze-style into bases.

Anvil was already successful, but it wasn’t designed for these larger targets. It needed a complete overhaul: more speed, better guidance, and enhanced durability.

We lived and died by our ability to quickly fire a “tracer bullet” through the heart of each problem, illuminating a clear path to the full solution.

Over three and a half months, we developed the V2P. We redesigned the propellers, motors, and the entire power architecture, which pushed the boundaries of what a small drone could do.

The result was unheard-of: a 5.2 kilogram vehicle that could achieve a speed of 147 miles per hour. (The world-record quadcopter at the time weighed 800 grams and did 173 mph). More impressively, it maintained sub-degree accuracy on pitch, roll, and yaw, even at steep angles of attack, where quadrotor dynamics become extremely challenging.

As a US defense customer’s Systems Integration Partner (SIP) competition approached, we needed to build 50 drones. One of our lead engineers from the test team and his co-worker came in on a weekend and built 28 complete vehicles in a single day—a feat that would have taken weeks through conventional processes. In total, they built 53 vehicles in 14 days, test-flying each one three times while I analyzed all the flight logs in real time. When we arrived at the competition, our competitors showed up with elaborate, expensive systems that had been years in development. Our V2P interceptor dominated the event. It destroyed 30 targets with extra interceptors to spare.

The highlight came on the evening before the last day of the bake-off, when US government officials asked if we could take down a far bigger Group Three aircraft—much larger than our system was designed for. With our existing approach, the V2P would simply bounce off such a large target. But we had a potential solution: radar firmware that could identify propellers through micro-Doppler signature and target them specifically. The night before the upcoming test round, we needed to finish writing the updated firmware and flash all of our drones with it.

At 3am, the same lead engineer who built 53 vehicles in two weeks went down to the hotel room where the drones were stored and proceeded to disassemble 18 birds. He took apart each radar, separated the processor and RF boards, hooked them up to his computer, flashed them with the new firmware, verified the changes, and reassembled everything. At 7am, he casually walked out as if he’d just woken up like everyone else, as the competing teams came down the elevators.

On the last day of the bake-off, our modified V2P took out the Group Three target on the first attempt, hitting it directly through the propeller. The entire room erupted in celebration.

That win was the cherry on top of a 200-person effort that ultimately secured a billion-dollar program of record for Anduril, transforming the company’s trajectory. It also exemplified the level of ownership we cultivated in our engineers—people who felt in their bones that they owned the outcome, and therefore cared so deeply about it that they didn’t blink at doing the kind of dirty work that their counterparts in other companies might consider drudgery.

This kind of approach is never without risk. But in our environment, we gave people agency and trusted their judgment. When a single engineer saw a 50/50 chance of success versus the near-zero probability with the original firmware, he made a call that changed Anduril’s future.

Team member building a Bolt for development testing.

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In engineering, simplicity is strength. At Anduril, we continuously asked what we could eliminate or simplify.

Consider the challenge of defending vast territories against cruise missiles. Conventional systems, like Patriot PAC-3 and NASAMS batteries, typically cost millions of dollars per installation. So we asked ourselves a simple question: What if we could create a forcefield of low-cost drones to intercept cruise missiles worth millions?

The concept seemed absurd at first, even to our team—the overmatch appeared too extreme. But we stripped the problem again to its fundamentals. Cruise missiles are fast, but they follow predictable flight paths. If we could accurately determine that flight path using two ground-based IR passive sensors (what we called Wide-Area Infrared System for Persistent Surveillance, or WISPs), we wouldn’t need expensive targeting systems on the interceptor itself.

We modified our Anvil drone to carry no sensors at all—the drone would simply position itself in the projected path of the incoming missile, aligning with where the missile would pierce our virtual “force field.” Despite the initial skepticism, we demonstrated the concept successfully, destroying a target that could fly an order of magnitude faster than our interceptor.

The beauty of this solution wasn’t just its low cost, but its elegance. We didn’t need to match speed with speed or complexity with complexity. We found the simplest possible point at which to intervene and disrupt the threat.

We applied this thinking to all our products, and constantly fought against feature creep. Most product managers naturally want to add capabilities—one after the next after the next. But we ruthlessly focused on the 20% of features that delivered 80% of the value, and made those exceptional.

A prototype compressor for Roadrunner turbojet development.

Team member utilizing a 5-Axis DMG machine to make challenging parts.

Yet there’s a line between scrappy and crappy. At an all-hands meeting a while back, one of our team members asked, “Why don’t we just build perfect products?” The answer reflected our core philosophy: We had an ethical obligation to get the best solutions into warfighters’ hands quickly. We could build one or two gold-plated systems over the course of years, or we could deliver 10 near-perfect solutions that actually make it to the field in a battle-relevant timeframe.

We were scrappy to the core, but we also had a very clear understanding of what “deployability” meant for each system. We were uncompromising about those standards while tolerating rough edges elsewhere. A disciplined approach to trade-offs allowed us to deliver capabilities that competitors with 10 times our resources couldn’t match. The key was attention to detail. Teams without a painfully clear understanding of what’s important have a bias toward frills, whereas we went after the aspects that delivered the most value to the warfighter—avoiding the classic mistake that sales-led organizations often make by building pretty products that fall short in functionality or usability.

To take one example, when we learned the Marine Corps was seeking a new loitering munition, we initially didn’t want to compete. The thinking was that a different company specializing in thermal imaging cameras and sensors had been working on this project for five years, and Anduril would simply waste millions of dollars trying to beat them.

Yet the opportunity for a competitive edge remained. Quadcopters usually flew slowly into targets horizontally and could be seen coming from far away. What we needed was a drone that could dive with such blistering speed that by the time you heard its scream, it was already too late … If we could come up with a game-changing new capability for top-down kills that would be much harder to anticipate, we could win.

I asked Raichelle Aniceto, my chief of staff, to procure the competitor’s drone, and within days, we had one completely disassembled in our lab. We hot-glued the components onto a trifold board using her wedding invitation kit. What appeared to be a science fair display nevertheless clearly demonstrated how each component was not only suboptimal, but dependent on multi-tiered foreign supply chains—and that we could build something lighter, faster, easier to manufacture, and more reliable, while de-risking tangled Chinese supply chains.

Like diamonds, all great products are born from heat and pressure.

But there was a catch—the Marines expected a product, not a proposal, and we had nothing more than a concept. We went a week without sleep to create high-performance renders, building life-like mockups, and drafting a technical proposal for a drone that didn’t yet exist.

The proposal was submitted 60 seconds before the deadline. We won the first phase, but now we had to actually build what we’d promised.

This became Bolt—a loitering munition that could precisely target ground vehicles. I saw it as a weekend project at first: take our existing drone platform, retrofit it with a vision seeker, and have it crash into ground targets instead of aerial ones. We created a quick demonstration video for a proposal and were one of the few companies down-selected.

As the project progressed and the stakes escalated, we learned that what worked for aerial targets wouldn’t work for ground targets. The team tried to apply the same guidance approach that worked for Anvil, but when diving at steep angles toward the ground, the drones kept missing by several meters.

During a critical customer demonstration, our bird completely missed its target. The room went silent. We knew we were at risk of losing a half-billion-dollar program milestone if we couldn’t fix Bolt quickly.

The engineering team tried applying more aggressive corrections, but the misses only grew wider. It turned out there was a fundamental issue: When a quadcopter dives downward faster than its propellers can “bite” through the air, the propellers act as air brakes, inverting the effect of guidance commands. We needed to completely reverse their guidance commands when crossing this threshold.

That weekend, I developed an entirely new guidance approach we called “Dive,” which allowed the drone to fall along a target vector with precise lateral corrections. Along with optimizing the propellers for inflow velocity with dynamic throttle margin, the problem was solved.

Later on, when preparing for a demonstration with a four-star general, another issue emerged: The airframe was vibrating and we were losing attitude control. The team drove the drone (with no lethal payload on it, of course) from Southern California to my home in Los Altos in the middle of the night. The next day was the baby shower for my second child, and we spent the hours and minutes before the guests arrived tuning the drone in my backyard.

The ferocious commitment from our team paid off again: The Marine Corps down-selected Bolt for its Organic Precision Fires-Light (OPF-L) program.

“The ferocious commitment from our team paid off again: The Marine Corps down-selected Bolt for its Organic Precision Fires-Light (OPF-L) program.”

It was never enough to create solutions that worked in the lab. Each one had to work reliably in the field, at scale, and at a cost that made sense.

This was part of Anduril’s secret sauce, and antithetical to how traditional defense contractors operate. The defense primes typically optimize for high-margin, low-volume production with expensive maintenance contracts. Anduril brought Silicon Valley’s mindset of scalable technology to defense—solutions that could be mass-produced and widely deployed.

When designing hardware, we broke the product development process into three distinct stages. In the conceptual phase, the most important metric was lead time—how quickly we could get the components needed to build a prototype. In the new product introduction phase, when building 10 to 100 units, we focused on ramp time—how quickly we could work with vendors to reach the rates required for a pilot. In the third stage, full-rate production, the focus shifted to scrap rate and cycle time.

Traditional defense programs often fail because they create exotic systems that are too hard to produce or too expensive to deploy at scale. We were determined not to make that mistake.

By designing with scale in mind from day one, we aimed to create a virtuous cycle: our products could be deployed more widely because they were affordable, which generated more data and experience, which improved the next generation of products.

These core principles guided our product development. But principles alone aren’t enough. To apply them consistently across hundreds of engineers and dozens of products, we needed to design an organization that could sustain this approach at scale.

Electrical test rack with a new board.

As we approached 60 employees, it became impossible for everyone to report to Brian. What began as my leadership of a handful of electrical engineers quickly expanded to 75 people, then to all hardware engineering, and ultimately to all product engineering—electrical, mechanical, and embedded systems combined. When I became SVP of Engineering in June 2022, I had 164 people in my department. By the time I left in March 2024, it had grown to 550 engineers working on 30 products across 15 different families.

Building a high-performance organization was as important as solving technical problems. Throughout my time, I had to maintain this dual identity—an engineer on the frontlines driving design and development, and also a leader responsible for creating the organizational structure that would enable others to do the same. I needed to build a leadership team that could own full lifecycle product development and deliver world-class systems at the pace and precision demanded by our mission. Every hire was made with this blueprint in mind.

The first priority was to anchor the organization with deep technical credibility. “Badass engineers want to work for badass engineers,” as the saying goes—the best will only work for leaders they can learn from and respect technically. We needed to avoid the common mistake made by organizations which fail by promoting or hiring managers without the technical skills to understand problems, build strong teams, or avoid making flawed engineering decisions.

For the electrical team, we wanted to position ourselves at the leading edge of avionics design. I recruited Shaun Donovan, a veteran of General Atomics and an early employee at Anduril, who had been involved in nearly every major electrical design to date. His mandate was clear: take hard-won lessons from legacy systems, and build the next generation of electrical architecture from first principles. On the mechanical side, we needed a leader deeply experienced in rapid prototyping and fabrication; someone who could translate ambitious concepts into functional hardware quickly and effectively. That was Matt Zipfel, whose career at SpaceX was defined by turning bold mechanical ideas into working prototypes under intense timelines.

With the core engineering leadership in place, the next critical step was embedding product thinking across the organization and developing a scalable product platform. We needed product leaders who could unify technical execution with the production rigor needed to scale. I brought over Danish Tejani, Anduril’s first hardware product manager and a former NPI lead at Tesla, to help build our product development function.

As the team matured and the product portfolio expanded, it became clear that maintaining alignment with the defense customer was both a strategic imperative and a growing risk. Many of our new hires came from non-defense industries, and while that brought valuable innovation, it also introduced a potential gap in mission understanding. To close that gap, we hired Joe Bayer, a former GA program executive and F18 pilot. His deep domain knowledge and firsthand understanding of defense customers allowed us to stay laser-focused on delivering solutions that mattered.

The final piece of the leadership architecture was operational scalability. By this point, we had grown to hundreds of engineers across dozens of teams. We needed a chief of staff who could partner with me to build and run the engineering organization, without losing the technical context that made us successful. It’s common practice to hire Jared from Silicon Valley types, but I needed someone who could communicate with engineers on their level and play the role of a technical leader. That was Raichelle Aniceto—an MIT-trained aerospace engineer who led Relativity Space’s ambitious Terran R rocket program.

Test racks for electrical assemblies in the R&D building’s electrical engineering lab.

Dev space in the R&D building’s electrical engineering lab.

Anduril is “an amusement park for engineers,” as I once remarked, because we worked on such diverse and challenging technologies. But behind the thrill and excitement had to be a carefully designed system that could turn ambitious ideas into deployable products. As we grew from a handful of engineers to over 500, maintaining our speed and innovation became increasingly challenging. Traditional organizational models wouldn’t work—dedicated teams for each product would have required thousands of engineers and created silos that slowed innovation. We needed something different.

We rebranded our engineering organization as “Product Engineering” to make our purpose clear: delivering products that meet customer needs. We then consolidated scattered teams into a cohesive group with three clear frameworks: products, core technologies, and key capabilities.

Products were our mission-focused integrated systems. Core technologies were our standardized building blocks—our LEGO pieces—that could be rapidly assembled into new products. Instead of starting each drone from scratch, we created reusable components like flight computers and propulsion systems. Key capabilities were our internal engineering services, like a machine shop that could transform digital concepts into physical prototypes within hours, or teams that could “shake, bake, and shock” components to ensure reliability.

What made this work was our matrix organization. Instead of creating dedicated teams for each product, we built functional organizations (across electrical, mechanical, and embedded systems) with deep expertise that could surge resources toward critical projects when needed. When we began developing Roadrunner, we pulled engineers from electrical and mechanical pools for intensive development, then shifted them to other projects when those phases ended.

The results were unprecedented in hardware: In early 2023, fewer than 200 people were responsible for over 25 different products, some of which were deployed across the world in the order of thousands.

We maintained integrated product teams where specialized expertise was required, like our Electronic Warfare group led by Sam El-Akkad, with deep RF and signal processing expertise. Or the Imaging team led by Bill Ross, with expertise in developing sensors down to the pixel-level silicon design. We also established product architects who were responsible for trade studies and system-level decisions—engineers who had proven themselves technically and could now lead cross-functional efforts.

With this foundation, we maintained small, focused teams while leveraging the broader ecosystem around them. But structure alone wasn’t enough—achieving this level of performance required recruiting the right people and building strong leadership capable of operating in a dynamic environment.

The author setting up a Pulsar, Anduril’s electronic warfare product, in a RF anechoic chamber.

The leadership approach at Anduril centered on a few core principles.

First, we prioritized relentlessly through a daily red-light/green-light system based on the Objectives and Key Results (OKRs) we set. Every product and project had clear metrics that we reviewed constantly. When something showed red, we’d immediately assemble the team to identify root causes. This consumed 60–70% of my time—figuring out the biggest obstacles and eliminating them alongside the team. I was notorious for being chronically late to scheduled meetings because I wouldn’t cut short work on critical problems.

Second, we maintained technical credibility through hands-on involvement. By day, I’d handle the corporate aspects—meeting with leaders across the company to drive product and technology development, and continuing a constant discussion about what was slowing us down, what was blocked, and what was broken. But after 5pm, my engineering work began. I’d return to the building at 7:30pm after dinner and wander the labs until the early hours, sitting with teams debugging problems. I’d whiteboard calculations, write Python scripts, and sometimes even solve structural dynamics questions. Engineers knew leadership understood their challenges at a fundamental level because we were there doing the work with them.

Third, many of us deliberately stayed out of the spotlight. If you look through photos of our major victories in those days, you won’t find me in them. This wasn’t false modesty; it was strategic. I wanted teams to own their achievements completely. By giving them full credit, they grew more confident and capable for the next challenge.

The results were unprecedented in hardware: In early 2023, fewer than 200 people were responsible for over 25 different products, some of which were deployed across the world in the order of thousands.

Underlying these principles was a fundamental belief: I work for my team. These 550 engineers were dedicating the best years of their careers to Anduril, and I felt responsible for making those years meaningful. My job was to harness the unique superpowers of each individual and create an environment where they could thrive.

This philosophy has to start at the top, and the impact of this approach, exemplified by Brian, cascaded through our organization. When leaders instead prioritize expanding their scope and influence over developing their teams, they create a vacuum that attracts similar self-interested leaders. The result is inevitable: layers of political fiefdoms that don’t value winning at an outcome level and don’t care about their people.

We elevated our approach into the management structure by promoting engineers who had earned respect through technical excellence, and creating a “scaffolding” system to support these technical leaders as they developed management skills.

The latest Anvil, equipped with energetics and Launch Box.

Backside of the bistatic seeker.

When it came to recruitment, we obsessed over what I called “the highest density and intensity of talent.” While talent density is a common concept, we focused equally on how to leverage a team’s capabilities through the environment we created. As Steve Jobs illustrated with his rock tumbler story, ordinary rocks become polished gems through friction against each other—just as talented teams polish each other through productive conflict to create something exceptional.

Our hiring approach was uncompromising. We installed “bar raisers” on interview panels—people who would reject candidates unless they were exceptional. I personally spent about 20% of my time on recruiting.

Even when scaling to 30–40 hires monthly, I interviewed most engineering candidates. I built Python dashboards to analyze our talent sources and maintained a LinkedIn Recruiter account. We expanded geographically, opening offices strategically to access new talent pools when my personal network was completely depleted.

We sought engineers who combined technical excellence with passion for our defense mission. We particularly valued former startup founders for their demonstrated agency and self-motivation.

Our performance management philosophy sat between two extremes: Elon’s approach of firing anyone on the left side of the performance curve versus Jensen Huang’s philosophy of trying to uplevel everyone. Elon believed that if you had five apples and one might be rotten, you have to throw out the whole batch. It’s a ruthless but logical view—bad apples can spoil the bunch. My approach aimed to be pragmatic—we maintained annual 10% attrition targets, but focused on clear expectations and respectful transitions.

We created a quarterly performance coaching program that identified the bottom 10% who needed support. We established clear OKRs so everyone understood their metrics for success. With transparent expectations, underperforming engineers often recognized on their own when they weren’t the right fit, leading to more respectful separations. This avoided the toxic alternative, common in other companies, where managers undermine struggling employees behind their backs rather than addressing issues directly.

Preventing organizational bloat proved equally critical. B-players who don’t understand product requirements tend to inflate headcount needs. When business lines claimed they needed 50 people for a project, we evaluated the engineering requirements and often found half were unnecessary. Our hiring estimates typically ran at half of what stakeholders requested and a third of industry standards. I reviewed every engineering hiring proposal personally, rejecting many while we still grew rapidly.

Building this culture was like coding our organizational DNA—you need the right sequences at the start. The legendary stories from Anduril’s early days became the foundational code, continuously retold and refreshed with new chapters, embedding traits that transformed new engineers into problem-solvers capable of the impossible. When I became responsible for new teams, engineers would approach me saying, “We’ve heard so many stories about what it was like at the beginning.” These stories weren’t just entertainment; they transmitted our values and showed what was possible. As our team grew by over 2,500%, these legends ensured that each new hire understood what made us different.

Thermal imager with a 900mm lens, in the R&D building’s dev test lab.

Throughout my career, I’d worked on cutting-edge hardware systems—drones, missiles, autonomous vehicles—that could theoretically perform any task, but in practice were often constrained by limited autonomy and human-guided control. Despite extraordinary advances in robotic hardware, the foundational intelligence to power these systems lags far behind. Most autonomous robots remain essentially puppets on strings, awaiting human instruction or executing elaborately choreographed behaviors.

In February 2024, I’d been approached about advising a robotics startup and called Brian to discuss it. We talked about physical intelligence and how, while AI companies everywhere were chasing language models and reasoning, half of the world’s GDP was generated by physical labor—yet no one had cracked the foundational model for robots to function effectively in the real world.

Brian listened carefully, then said something that will stay with me forever: “I’ve always seen you as a founder.”

For me, this wasn’t just encouragement—it was perspective from someone who had followed the same path, moving from leading engineering at Palantir to co-founding Anduril. Brian ultimately supported my growing belief that building a universal intelligence to unlock the limitless capabilities of robotic hardware—and fundamentally reshape humanity’s experience of physical labor—was an opportunity I couldn’t pass up.

I thought then of my son, and the innumerable times he accompanied me, often at strange hours and with curious equipment in tow, to desert test sites, landing strips, and hotel rooftops. The best problems, I’ve always told him, are the ones everyone else avoids, until they become impossible to ignore. When he tells me he wants to build things like me when he grows up, I feel the weight of the work I did at Anduril, and what I still have left to do.  

In May 2024, I left the company as a full-time employee to co-found Physical Intelligence (PI). PI is now building the universal intelligence model that can finally close the gap: a single, powerful “brain” capable of bringing genuine autonomy to every physically actuated device, from drones and industrial robots to household appliances. Solving this problem means more than a technological breakthrough: it’s about fundamentally redefining humanity’s relationship with physical labor, enabling a productivity revolution on the scale of industrialization itself.

The point is that Anduril’s own mission is so impactful, and meant so much to me, that I couldn’t have left it for any other mission short of PI’s. Still, the decision to leave wasn’t easy. I’d helped build a team of 550 extraordinary engineers who had become family. But the culture of ownership we’d created meant they didn’t need me anymore.

Adnan Esmail is the co-founder of Physical Intelligence and Emeritus SVP Engineering at Anduril.