In Silicon Valley, if a founder wants to build the next unicorn, there’s a formula: find a bold idea, surround yourself with well-heeled advisers and investors, and raise a war chest. With cash and fundraising buzz, go after a large market in search of product-market fit. That journey sometimes leads to winning pilots, more rounds and real customers. More often, the company pivots into a different niche or quietly dissolves. A whiff of failure sends employees to the exits and funding evaporates. That’s the VC-backed model. It fuels the dreams of college dropouts and frustrated engineers, rewarding luck and timing when they meet in the hottest niches.

But not all companies can or should be built that way. Only a handful of winners make fairy tale successes. The more common path is bootstrapped or self-funded: Start with an existing customer problem and get paid more than it costs to solve it. Find more customers with the same problem, build systems to improve the solution, repeat.

For entrepreneurs without the luxury of risk capital, product-market fit can’t be an odyssey. It has to be a starting point. Much of the global economy has been built this way. The path may take longer than the VC “go big or go home” approach, but many small companies scale into middle market businesses, and a few of the best find their way to market leadership, even in tech. Consider and . For every VC-backed startup, there are hundreds of bootstrapped founders building profitable businesses without any outside investment.

Customer-focused and experienced founders

Ask VC-backed founders how they built their company, and you’ll hear about the team and investors first. Bootstrapped founders tell it in reverse: the customer comes first, and the team is built around them.

Some of the most successful VC-backed founders are younger, benefiting from inexperience by seeing opportunity as a blank sheet of paper rather than a wall of entrenched obstacles. A 25-year-old with no mortgage, no reputation to protect, and no comfortable job to leave can withstand a failure and start again. These risk-taking enterprises spare no expense to attract the best hired guns money can buy and build fancy offices, all with a focus on hitting milestones for the next round of financing. When it works, the outcomes are spectacular: think of the Collison brothers at taking on payments, or ‘s young team taking on development tools.

But these are exceptions, not the rule. Industry experience, domain knowledge and customer relationships are essential to building a company. Bootstrapped founders typically know their customer before they build. There’s no search for product-market fit, because the product is built for problems the founder already knows intimately. Growth comes from deepening existing relationships, a surer path to revenue than risk capital is meant to fund. The team is hired out of profits to serve paying customers, not to test if demand exists.

Bootstrapped founders have a different profile. Typically mid-career, they have more at risk: a mortgage, a reputation, a family depending on their income. They lack the appetite for a long-shot bet. Instead, they gravitate toward businesses with a real chance of working, aiming for profitability quickly, often starting small rather than earth-shattering, with lower barriers to entry. The result is a business run for profitability, not growth. Leadership has often worked together before or shares common backgrounds. Growth is often linear and slow for years, until the company reaches a scale where it can pursue more strategic opportunities.

The AI advantage for bootstrapped companies

In an AI era where code-generation and product design tools bring down the cost of building and deploying new products, most companies should require less risk capital, not more. In the past, a non-technical founder with an idea needed outside capital to build it. Product development required an engineering team, and an engineering team meant a payroll early revenue couldn’t finance. That was the justification for raising a seed round before lining up a single customer. With AI, capital is no longer the limiting factor for innovation.

The VC-backed market, though, is moving the other way, with larger seed rounds and bigger early-stage funds than ever. Increasingly, risk capital is used for less rational reasons that speak to the speculative bubble we live in: not to fund product development, but to buy time to market, fuel “land grab” velocity in sales and marketing, and subsidize deployments that would otherwise be uneconomic for customers.

A founder today can build a working application with a small team, deploy with real customers, and validate whether further investment is needed. The product/market gap that once required millions of dollars and world-class hires can now be closed by a handful of competent people. , the with $1 billion in revenue, is an extreme example of what is possible. Niche markets once too small for VC-backed startups now can be addressed by bootstrapped companies.

That doesn’t mean every business should be bootstrapped. A founder with a genuinely untested, capital-intensive idea and no existing customer base still has real use for outside risk capital to fund the search for a market. But AI has lowered the cost of entry and should spur an unprecedented number of bootstrapped companies built outside the VC ecosystem, profitable and lean from the start. The best of them will become the.

is the founder, managing partner and chair of the investment committee at . He launched Lateral with a strategy to allocate first institutional growth capital to independent, owner-operated middle-market businesses underserved by typical buyout firms. Previously, he served as a managing director at , a venture capital and growth equity firm that has invested in more than 300 companies including , , , , and . De Silva also previously co-founded , a marketplace for construction equipment that was sold to for nearly $800 million. He received an MBA from , a master of philosophy from the , and an undergraduate degree from .

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The current focus on AI IPOs is largely centered on public market performance. Investors want to know whether these companies justify their valuations and how their shares will trade after listing.

But everybody is watching the wrong metric. The more consequential story begins after the bell rings, when limited partners receive distributions and decide where to deploy that capital next.

At sufficient scale, AI IPOs become a capital formation event for the broader venture ecosystem. If several of the largest AI companies reach the public markets over the next few years, those exits could reshape venture fundraising and further concentrate capital among the industry’s largest firms.

The real story begins after the IPO

The more meaningful process starts when investors receive distributions from successful exits. Pension funds, university endowments, sovereign wealth funds and family offices rarely leave that capital sitting idle for long. As portfolios are rebalanced, investment committees begin evaluating new commitments across private markets.

Venture has spent several years waiting for meaningful liquidity. Higher private valuations may improve paper returns, but they do not return capital to limited partners. Only successful exits complete that cycle.

’s $85.7 billion IPO illustrates both the potential and the limits of a single listing. One IPO alone is unlikely to transform venture fundraising. But a sustained wave of listings involving companies such as , , and could steadily return capital to investors and give limited partners fresh resources to recommit.

Liquidity drives the next fundraising cycle

The importance of the next AI IPOs lies less in their individual performance than in their combined effect on venture fundraising.

As capital flows back to limited partners, investment committees gain both the liquidity and the flexibility to make new commitments. How those commitments are distributed will shape the industry’s next phase.

Recent fundraising trends suggest capital is likely to remain concentrated. According to the , the 10 largest U.S. venture funds captured nearly one-third of all capital raised in 2025, while first-time fund formation in more than a decade. If a new wave of liquidity reaches the market, established managers with proven track records are likely to receive the largest share.

offers a useful illustration. The firm recently raised over $15 billion across five funds, an amount equivalent to more than 18% of all U.S. venture capital dollars raised during 2025. Stronger distributions could leave the industry’s largest firms in an even better position to raise successor funds.

Capital will not flow evenly

Limited partners typically increase commitments to managers with established track records before expanding relationships with emerging firms. Successful exits reinforce confidence in those managers, making them the natural destination for a disproportionate share of new allocations.

The effects extend beyond fundraising. A $15 billion fund approaches ownership, pricing and portfolio support differently from a $500 million fund. Large funds need meaningful ownership and outcomes capable of returning multibillion-dollar vehicles. They can lead larger rounds, pay higher prices, defend ownership through multiple financings, and support companies for longer.

This is not a liquidity flywheel. It is a concentration flywheel. Successful investments generate distributions. Those distributions help the industry’s largest firms raise larger successor funds, reinforcing their competitive advantages. Over time, liquidity strengthens fundraising, and fundraising strengthens market position. The market may become larger without becoming broader.

Founders will feel the effects. Large investment platforms can finance companies for longer and compete more aggressively for ownership in the relatively small number of businesses capable of producing returns at their scale. The result could be a more pronounced barbell market: a limited group of companies attracts enormous amounts of capital, while businesses outside the dominant sectors face a more constrained financing environment.

Pay attention to LP liquidity, not just IPO pricing

Public investors will remember this AI IPO cycle by its opening prices. Venture investors may remember it for something else entirely.

It may be the moment capital began concentrating around a handful of firms at a speed the industry has never experienced.

The IPOs themselves will make headlines. The redistribution of power inside venture capital will shape the next decade.

is a general partner at , a VC firm investing in early-stage startups in AI, cybersecurity and digital health, and helping them expand into the U.S. market.

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The road to the top rarely runs in a straight line, but there are less circuitous routes to becoming CEO. According to analyzing every CEO succession in the S&P 500 since 2000, there are four feeder roles: COOs, divisional CEOs, CFOs, and “leapfrog” leaders promoted from below the C-suite.Ìę

But a separate 10-year study called the suggests that the fastest climbers ± “sprinters” who reached the CEO seat well ahead of the 24-year average — didn’t get there by climbing the corporate ladder to the top. They got there through bold, often unconventional career moves, such as taking on a failing division or building something from scratch.Ìę

In fact, what set them apart wasn’t pedigree but building a specific skillset that made them CEO material: decisiveness, reliability, adaptability, and the ability to engage people around a plan.

Product management doesn’t appear in the CEO-pathway research, likely because none of the major studies breaks the role out as a separate category. It’s a relatively newer function, and it tends to get folded into general management or engineering in career datasets.Ìę

But once you look at what the job demands — ownership of a tangible outcome, obsession with what customers value, the willingness to make tough decisions — it maps directly onto the traits the CEO Genome Project found in its sprinters.

I’ve spent my career leading product and technology at companies including , , and . Those roles landed me my first CEO position without having to fill the typical CEO-starter pack jobs because I was able to articulate my skill set to the board.Ìę

In short, product management is about making a product successful; being CEO is about making a business successful. The ingredients are the same.Ìę

Don’t take my word for it. joined in 2004, leading product management for the Google Toolbar, years before he became CEO. spent eight years as YouTube’s chief product officer before taking the top job there in 2023.Ìę

Yes, the scope of the job differs, but that’s true of every promotion, especially for the hardest job on offer. A CEO carries the full weight of the business: financial performance, legal and regulatory exposure, the board, and the market. A PM’s remit is naturally narrower: one product, one roadmap, one team to rally.Ìę

But scope isn’t the same as skillset. The job gets bigger, but the muscles you exercise remain the same: setting a vision under uncertainty, prioritizing ruthlessly, making calls with incomplete information, and getting people who don’t report to you to deliver anyway. Learn to do that for a product, and you’ve already learned to do it for a business, just on a bigger scale.

That mindset isn’t new to start-ups and scale-ups either, where a PM is often the closest thing to a mini-CEO, making calls across product, growth, and operations simply because no dedicated function exists yet to do it for them.Ìę

Part of the reason I think the PM-to-CEO pathway is so often overlooked is that the function is judged by its worst practitioners. Plenty of people with “product manager” on their CV spend more time managing processes and stakeholders than owning outcomes and building amazing products. And it’s that version of the job that shapes how PMs get perceived, and why few are inspired to make the leap. The PMs who have done the job – by taking ownership of the outcome rather than the process – are building something that truly resembles the job description of a CEO.Ìę

The best advice I can give to aspiring executives and entrepreneurs today – whether they’re PMs or not – is to choose a metric that they want to be accountable for. In my previous role, I focused on monthly active users; now I’m focusing on EBITDA. Strip away the layers and remain outcome-oriented.Ìę

The CEO pathway research keeps looking for the right sequence of job titles, but that model is increasingly outdated in the modern-day work environment. Rather than focusing on traditional CEO pathways, aspiring executives should focus less on glitzy job titles to add to their CVs and more on the concrete skills they can gain. Product management, done properly, offers the perfect crash course.Ìę

is the CEO of , a London-based technology company building a global payments infrastructure to facilitate money transfers. He previously led product and technology for companies including , , and .

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Think of the most famous boxers you know, likely the heavyweights: Muhammad Ali, Joe Louis, Mike Tyson. In a clash between titans, the advantages seem easy to understand, since the bigger fighter looks like the stronger one.

But size alone is not a strategy. Sugar Ray Robinson was a middleweight, not a heavyweight, and in the 1950s, A.J. Liebling said he looked “more like a loose-limbed dancer than a boxer.”

Robinson’s advantage was completeness: speed, footwork, intelligence and stamina. In a famous 1951 match against Jake LaMotta, Robinson schooled the reigning, heavier middleweight champion with a 13th-round TKO.

Completeness also applies to companies. The market tends to assume that big companies will capture the biggest gains from AI. But AI is tough to get right at any size.

Look at , valued at $6 billion in 2024. It made headlines claiming its -powered chatbot could handle millions of conversations and do the work of 700 customer service employees. Customers hated the rollout, and by 2025, Klarna was . Or , which watched its value after ChatGPT commoditized its main offerings.

If everyone can get AI wrong, who wins?

Enter the scrappy middleweight

Each year we speak with thousands of operators and founders, and one pattern is clear: The biggest long-term gains from AI will not flow to heavyweight incumbents or many AI-native startups but to scrappy middle-market technology companies, the middleweights.

The next phase of AI disruption will be challenging, but middleweights can gain serious ground.

One objection: Won’t hyperscaler companies go after certain verticals? If Copilot inside 365 or 1Ìęagents can run a workflow, how does a middleweight company survive? The answer depends on what constitutes durable advantage. Horizontal platforms are built for generalized work, not the messy, regulation-heavy, category-specific workflows of the real world. Middleweights can win by making their software the system of record that AI calls into instead of software that AI replaces.

The odds for making big, impactful gains with AI right now favor the middle market, where proven growth companies can use customer trust, domain expertise, capital structure and speed to transform their businesses, taking market share from slower incumbents. With three-quarters of AI’s economic gains now being captured by just per , entrepreneurs who stand still may already be losing the round.

What makes for a winning middleweight company?

The best middleweight technology companies share the five traits below, all working together as a system.

Disciplined self-assessment. Middleweights are designed to act quickly on honest feedback, and their boards help them test where AI generates value versus where it merely consumes engineering capacity and budget.

Seat-based pricing is one area for brutal assessment. When autonomous agents do the work, the revenue model should reflect outcomes, not users. In 2023, customer service platform made a bold switch, pricing its AI agent Fin at 99 cents per resolved conversation. That agent became the company’s core offering, and it recently . Outcome-based pricing might seem painful at first (and reorganize your GTM team and their incentives), but it anticipates an agentic future.

Agility. Enterprise companies are weighed down by technical debt and legacy infrastructure. Middleweights have enough scale and proprietary data but not so much organizational mass that every experiment needs 10 layers of approval. Their agility is as much cultural as structural.

These are ambitious, scaling companies growing 20% or more with strong unit economics, and a tech-first mindset runs through the entire business, not just the engineering org. Take the restaurant software , where early AI gains came from product leads ; those product teams then built a flywheel connecting new product features to external communications, with LLMs continuously editing and improving instructions for AI agents.

Workflow ownership. In the AI era, the strongest moat is owning a complex workflow. Middleweights have spent years gaining this position — integrating into customer systems, accumulating exception-level data, learning operational nuances that take a claims process from 95% accurate to 99.5%. (The last 4.5 points are the moat.)

An company, , doesn’t just apply AI to contracts; its moat is absorbing the decision workflow around each contract. As a contract moves through approvals, negotiations and redlines, the important part is learning from the history of why internal teams decided the way they did. Well-positioned companies will hold the institutional memory that AI agents need to query to do their jobs.

Technical capacity. Most large companies are stuck in AI pilot purgatory, and the market still underestimates how operationally demanding AI deployment is. Middleweights have something most AI-native startups lack: years of working with real customers. , another FTV company, started in 2007 as a service-heavy cybersecurity business that has learned deep detection logic from operating in more than 1,000 customer environments, including some of the largest global enterprises. As the company saw rapid automation from machine learning, then more sophisticated AI, it moved in-house SOC analysts into higher-value product development roles, allowing engineers with deep cyber expertise to drive key R&D.

A well-capitalized balance sheet. Companies with cleaner balance sheets can move faster, absorb experimentation costs, pursue selective M&A, and keep investing through periods of disruption. Large legacy software companies carrying heavy leverage, optimized for cost-cutting and growing at 5%-10%, can’t be light on their feet and will struggle to reallocate capital aggressively enough into AI R&D.

The imperative

Plenty of boxers can be complete for one season. Sugar Ray Robinson executed consistently in 200 professional fights, mastering the sweet science with a reliable system. That same high standard now applies to companies in the AI era.

The window for transformational gains with AI is not open indefinitely. Speed is a middleweight leader’s advantage. Do not wait to perfect your AI strategy; start executing.

If you don’t know where to start, pick key workflows, map them and ask whether AI makes them more defensible or more exposed. The answer may determine whether you give up a round or win the match.

is managing partner at , where he oversees the firm’s global strategy and investment decisions. He has been a growth equity investor at FTV for more than 20 years, leading investments in enterprise technology and services and financial technology and services. Bernstein has over 25 years of private equity experience. Prior to FTV, he was a partner at and its predecessors where he managed the business and financial services group. He began his private equity career with and started his professional career in the investment banking division of in New York.

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Frontier labs and hyperscalers promise world-shifting innovation. And most deliver it. But, as we’re seeing with the policy and the evolving and security , they operate without stability.

That’s deeply concerning because technology organizations that build their entire AI operations and business on top of Anthropic, OpenAI and other paid models need to be able to depend on their reliability.

Meanwhile, open-source organizations like and offer cost-free models with similar quality. The difference in price is stark. And the gaps in utility, safety and accessibility that kept the enterprise away are closing fast.

This evolving dynamic leaves CTOs, CIOs and engineering leaders with a question: How can we keep reliability up and costs down when it’s impossible to predict whether hyperscalers will drop or raise prices of their next models?

The answer isn’t clear-cut — yet. But it’s never been clearer that engineering leaders need systems that allow their teams to quickly swap models and shift how AI agents work with people and access real data and tools. Building the right foundational layer keeps organizations nimble enough to evolve alongside the industry without cutting corners by chasing the latest trends.

Tokens cost more than time and money

Engineering leaders at Big Tech companies and within enterprises learned the hard way that building toward their organization’s long-term stability is a much better plan than chasing trends like “tokenmaxxing,” which results in unsustainable spend and team burnout.

While a fair amount of damage to company accounts and executive reputations has been done, the pendulum is already swinging back from tokenmaxxing to more sober approaches. At the same time, companies like that publicly went all-in on team-wide AI use are reinvesting in engineering team culture.

The goal: boosting morale while removing competition from token use.

Instead of jumping on the next hype train and creating the inevitable bottleneck, organizations should invest in modernizing their infrastructure to empower teams to sustainably iterate on and experiment with AI tools at scale.

The future of enterprise AI empowers people and agents to work seamlessly together. What this looks like:

• Accepting that agents have most of the same capabilities as people, with the added value of being able to test against real infrastructure with access to “real” data swiftly and at scale.
• Ensuring agents have the same guardrails as teams, including making sure credentials and permissions are only granted when needed; under the right circumstances and with full visibility into actions taken when things go wrong.
• Building systems that are able to swap in the latest AI models and frameworks to take advantage of new advancements without losing the custom work done in-house.
• Making sure their companies aren’t locked into a single provider long-term in order to reduce risk from outages, expensive contracts or dated products.

Models change. Update your architecture

Building resilience starts with accepting that models and how we use them will change. Engineering leaders need to embrace that it will sometimes make sense to go with the latest hyperscaler model. Other times, it will make sense to bring in open-source models with novel harnesses that run at no cost but change how people collaborate with them.

Meanwhile, agents shouldn’t be limited to toy problems or synthetic environments. They need the ability to test against real infrastructure, interact with realistic datasets, and participate meaningfully in real business workflows.

The winning approach: Level the playing field between agents and engineers.

Give agents access to the same environments people use and mandate that they operate under the same guardrails teams follow. Permissions should be granted only when necessary. Credentials should be tightly controlled. Every action should be observable and auditable. When something goes wrong, accountability should follow with clear visibility into what happened and why.

Hold both parties to the highest standards. Build resilience with your team.

There’s strength in flexibility

The days of custom workflows, automation and operational knowledge being trapped behind a single vendor relationship are over. We’re entering an AI agent-plus-engineer era that demands building systems and teams around flexibility, elasticity and adaptability.

In other words, it’s time to eliminate long-term lock-in for good.

Organizations that preserve the flexibility to adopt new models, integrate emerging tools, and respond to changing market conditions without rebuilding everything from scratch build resilience with every model release. It’s the way of the future. Engineering leaders should adopt this approach today.

is the CEO and co-founder of , which aims to bring enterprise quality infrastructure to autonomous agents and engineers. He was previously an early engineering leader at , and .

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I have spent more than 40 years in financial services, and I have learned to be skeptical of people heralding the word “revolution.” Branchless banking was going to end the branch. The blockchain was going to disintermediate the whole system. Big Tech was going to extinguish the bank altogether.

However, consider the most meaningful waves of financial services innovation this generation. The information-based strategy we pioneered at turned data into the engine of a consumer bank, reimagining it from the inside.

Then the internet dissolved the branch as the unit of distribution, putting the bank on a screen. Then digitization moved that screen into the customer’s pocket, unlocking all banking services with a mere touch stroke. Then the cloud collapsed the cost of computing and let a handful of engineers do what once took a data center and an army. AI will be bigger than all of these waves.

The change is here

AI will become the operating system which global finance runs on, rewriting the value chain end to end until the industry that emerges looks nothing like the one it replaced.

You can already see it happening, one layer at a time. Wealth management is being rebuilt around tools like 1, which turns the messy reality of client conversations into structured, actionable intelligence.

Investment banking is being rewired by the likes of and , compressing analytical work that once consumed floors of junior bankers. Filing taxes is being reenvisioned by companies like . AI neobanks like and are automating more pieces of the consumer banking relationship. The call center is being reimagined by companies such as and , resolving the complex, regulated queries that first-generation chatbots could never touch.

The plumbing of the financial system itself is under siege. is building the clearing bank for the AI age, and and are becoming the back-office stack that businesses run on, folding cards, expenses, procurement and accounting into a single semi-autonomous system. Companies like and are rebuilding risk and compliance, identity verification, AML and KYC, for an AI world where the counterparty on a transaction may not be a person at all.

Further out sits the largest prize, an agentic commerce layer where software transacts on our behalf or quietly arbitrages idle deposits away from inert institutions. Layer by layer, the financial system will be systematically uprooted by AI, each piece first made faster and cheaper, then reinvented from the ground up.

Zero marginal costs

The first thing AI does is brutal and simple. It takes the marginal cost to underwrite a loan, clear a compliance review, serve a customer at 2 a.m., and drive it toward zero.

We have spent decades treating those functions as fixed operating costs. Capital One’s insurgency 30 years ago proved that a one-size-fits-all model breaks the moment marginal economics lets you price and serve customers individually. AI applies that logic to the whole stack ruthlessly and simultaneously, remaking business models and org charts all at once.

The second order effect is that AI unlocks products that could not exist before. At Capital One we sought to deliver the right product to the right customer at the right price at the right time, which was always something of an exaggeration, because all we really had was direct mail and statistical inference.

Now it can genuinely be done: products tailored to a customer’s specific needs, credit that moves with daily cash flows, insurance priced to the individual rather than the actuarial average. The frontier of the buildable has moved further in three years than in the prior 20 and founders catching this wave are turning that capability loose. AI is a kind of alchemy, turning lead into gold. We are watching it happen across our own portfolio, expanding the frontier of what’s possible for many companies.

Upstart fintechs have historically had the most to gain with rising technological waves. Fintech’s nimbleness, compressed decision timelines, and sheer force of will give them a commanding head start in adopting and implementing AI.

But incumbent financial institutions shouldn’t be discounted. They sit on the richest proprietary datasets in the economy, decades of transactions, balances, defaults and recoveries that no fintech can buy. If data is the fuel of the AI age, the big banks and insurance companies own the refineries.

And yet I have spent a career watching these institutions confuse consumer loyalty with inertia and watching the advantage that should have been decisive die quietly in committee. Earned-wage access, buy now, paylater, C2C remittances, digital brokerage: whole categories the incumbents never bothered to enter, and where fintechs now sit firmly in command.

That ceded ground has helped mint fintech centicorns like , , and 2. Owning customer data and being capable and willing to act on it are different things, and most of it sits trapped in legacy cores, inside organizations built to protect and defend the existing model, not break it.

Every link in the value chain

The hardest thing for an incumbent is summoning the will to pivot or self-cannibalize. Those treating this as an existential mandate, rebuilding their technology and their talent around AI, will stand alongside leading fintechs in remaking the future of finance over the coming decade. The rest will come to understand what has changed only as they watch their market share erode and the sector consolidate.

If there is one thing I have learned in 40 years, it is that technology rarely rewards whoever owns the asset; it rewards whoever is willing to rebuild around it. AI will rewire every link in the value chain, from the way consumers transact to the way money moves and businesses run, and what emerges on the other side of this technological tidal wave will only vaguely resemble the system we know today.

I have watched four waves reshape this industry, and no word I used for them feels strong enough for this one. The question that matters now is who will summon the conviction to dismantle what works today to build what wins tomorrow.

is the co-founder and managing partner of , a fintech venture capital platform focused on disruptive, high-growth financial services companies. QED has made numerous unicorn investments, including , , , , , and . Morris is also the chairman of and , serves on the boards of , and , and is a board observer for and . Prior to QED, he co-founded in 1994. Under his leadership as president and chief operating officer, Capital One pioneered an information-based strategy that transformed the consumer lending industry. He holds an MBA with distinction from London Business School, where he is also a Fellow.

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In 2026, conversations about Black founders and venture capital have focused on access to funding. But as AI reshapes startup economics, the bigger challenge is no longer simply getting a first check, it’s raising enough capital at the seed stage to successfully reach Series A.

AI has fundamentally lowered the cost of building software companies. Founders can launch products faster, automate operations and accomplish with five employees what once required teams of 30. Yet while AI has reduced the cost of building a startup, it has not reduced the cost of scaling one. Companies still need resources to acquire customers, hire experienced talent, invest in go-to-market strategies, and generate the revenue and growth metrics institutional investors expect before leading a Series A round.

For Black founders, who continue to receive a disproportionately small share of venture capital, the inability to secure fully funded seed rounds has become one of the greatest barriers to building venture-scale companies.

AI is making seed capital more valuable, not less

One of the biggest misconceptions about AI is that startups simply need less money. In reality, AI has shifted when capital matters most. Because startups can now build products more efficiently, investors are increasingly rewarding founders who demonstrate real traction instead of polished ideas. Seed funding is no longer financing an experiment, it is financing proof.

That means founders need enough capital to move beyond building a product and toward building a business. Today’s Series A investors are looking for recurring revenue, customer retention, capital efficiency and repeatable growth. Those milestones require time, execution and sufficient capital.

The startups that reach them are increasingly those that raised enough capital early to stay focused on customers instead of constantly fundraising.

The numbers tell a stark story

The challenge is particularly acute for Black entrepreneurs. According to ÂÜÀòÊÓÆ” data, U.S. startups with a Black founder or co-founder received just $942 million in venture funding in 2025, only 0.32% of all venture capital invested in the nation. That represents one of the lowest funding shares in years and a dramatic decline from 2021, when Black founders raised $5.2 billion during the post-George Floyd investment surge.

While 2026 has shown encouraging signs, with Black-founded startups raising approximately $643 million by late May, the strongest quarter since mid-2022, the improvement was driven largely by a handful of unusually large financings, including a $350 million AI round. Across the broader ecosystem, Black founders remain significantly underrepresented in venture funding.

The issue isn’t simply that too little capital is available. It’s that many Black founders raise partial seed rounds that leave them without enough operating flexibility to achieve the milestones required for institutional Series A financing.

The real gap is between seed and Series A

Historically, venture capital rewarded bold ideas and rapid expansion. Today’s market rewards disciplined execution. Investors expect startups to demonstrate product-market fit, meaningful revenue growth, and efficient operations before committing Series A capital. That has made the journey between seed and Series A longer and more demanding.

Black founders who raise only enough money to survive often find themselves trapped in a cycle of continuous fundraising. Instead of focusing on customers, product development and hiring, they spend valuable months chasing additional capital just to extend their runway.

In an AI-driven market where product cycles move faster than ever, that lost time can determine whether a startup becomes a category leader or gets left behind.

Oversubscribed seed rounds are a competitive advantage

This is why oversubscribed seed rounds are taking on new importance for Black founders. Traditionally, oversubscription was viewed primarily as a signal of investor demand. Today, it is becoming a strategic advantage.

Additional capital gives Black founders flexibility to weather slower fundraising markets, invest aggressively when opportunities emerge, and continue executing without returning to investors every few months. It also allows founders to pursue growth intentionally rather than reactively.

Capital efficiency remains important, but efficiency is most valuable when paired with enough capital to execute.

The AI economy requires longer vision

The venture industry often celebrates AI for making entrepreneurship more accessible. In many ways, that’s true. The barriers to launching a company have never been lower. But lowering the cost of starting a company does not eliminate the capital required to build an enduring one.

Closing the Series A funding gap is therefore not simply about increasing investment in Black founders. It’s about ensuring founders have enough money to reach the milestones that unlock future institutional capital. That’s how you create more Black unicorns.

For Black founders, the conversation should no longer focus solely on access to capital. It should focus on whether they have enough capital to compete. In the AI economy, the Black-led companies that endure won’t simply be those that build the fastest, they will be the ones with the resources to keep building long enough to win.

and are the co-founders of (Black Ops VC), an early-stage venture capital firm. Norman is a managing partner at Black Ops VC. He is also the CEO of , an AI-powered market research platform used by industry giants such as and that’s designed for the media and entertainment spaces to gather audience feedback on video content, and a partner at , an accelerator that provides intense programming, resources and capital to overlooked founders.

Along with serving as general partner at Black Ops VC, Green is the founder and CEO of , an AI-powered CRM and inventory management platform specifically designed for art galleries, dealers, auction houses and collectors.Ìę

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Everywhere you look, venture headlines imply that seed rounds have meaningfully changed shape.

raised $1 billion for a company that didn’t exist a week earlier. launched with $6.2 billion out the gate. hit $475 million two months after founding.

It’s easy to read those headlines and conclude the venture model has been rewritten, that AI is a once-in-a-generation opportunity requiring once-in-a-generation capital.

We disagree. And so does the data.

The biotech parallel

At , we’ve built deep domain expertise in biotech, the sector with the longest history of mega first rounds in venture.

Biotech mega-seeds are common because the science requires it, you can’t run a Phase 1 trial on $3 million, but the return profile is often humbling. Large first rounds in biotech have produced a handful of strong outcomes for first-check investors 
 and a very long tail of modest ones. Our experience with this trend in biotech motivated us to compile a dataset and pressure-test our intuition more broadly.

We pulled every publicly available $100 million-plus first round we could find over the last 15 years (roughly 200 deals) and found that only 20% had recorded exits. Of those, only a few delivered what we’d call a venture-like return: 10x MOIC or better for the first-round investor. In other words, approximately 1% of companies that publicly raised $100 million or more in their first financing round generated returns that justify the asset class. Capital intensity, as it turns out, actually worked against venture outcomes.

That distribution will improve with a few well-placed AI outcomes this year. and alone will essentially double the number of outlier returns in this data set when they exit. But even there, the return math is nuanced for first round investors. According to reports, first-round investors are looking at 30-40x returns at OpenAI’s projected IPO valuations.

That’s a fantastic outcome, but it’s also a fraction of what early institutional investors made on the generational outcomes of prior eras.

and each turned roughly $12.5 million of their checks into around $4 billion, driving reported returns somewhere north of 300x. reportedly turned a roughly $500,000 investment in into $2.5 billion — nearly 5,000x.

These are exponentially larger outcomes. Why? The difference wasn’t a byproduct of company quality but of entry price. Those historical investors got in at a price that left room for the upside to actually compound.

The mega round is real, but not replacing the market

The number of $50 million-plus seed rounds has exploded since 2018. But traditionally sized first rounds are also growing. The headline-grabbing rounds are a small fraction of what’s actually getting funded, and an even smaller fraction of what will return venture-scale capital.

Moreover, the companies people now hold up as AI winners started small, only further reinforcing this point.

‘s first round was less than $10 million. ‘ was $2 million. ‘s was $11 million. ‘s was $25 million. Even at the frontier-model layer, ‘s first round was $5 million. Today, every one of those companies is valued north of $5 billion and generating hundreds of millions in revenue.

Cursor at less than $10 million is the more representative data point. Project Prometheus at $6.2 billion is the exception.

Capital intensity is not a moat

Raising a massive first round doesn’t inherently make a company more likely to generate venture size returns for its investors. Sometimes it’s a necessary cost of doing business, but the venture math is unforgiving.

High entry prices leave less room for the upside to accrue, regardless of the underlying opportunity. The playbook that has worked across every prior technology wave is to buy meaningful ownership in capital-efficient companies at prices that leave room for the upside.

That playbook doesn’t make for dramatic headlines in 2025. But it’s what the historical data, from Google to Uber to Cursor, consistently vindicates.

A few of today’s mega-seeded AI companies will absolutely deliver 10x-plus MOICs, just as a few winners have in every era. But the data’s been consistent for 15 years, and building a portfolio around the exceptions, rather than the pattern, is a bet with a long losing track record.

is a principal at , where she draws on a decade of infrastructure and technology investing experience as well as a systems engineering background to support exceptional entrepreneurs building the next generation of frontier technology companies. Prior, McDonald was an investor at , where she focused on growth-stage climate tech companies. She began her career in‘ power and utilities group and then at , where she developed deep expertise across energy, infrastructure and project finance.

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Last month, of , the corporate venture arm it launched in 2016 and grew to more than $850 million across three funds. The company hired to explore selling portfolio stakes on the secondary market, putting positions in companies such as and in play. The news also arrived weeks after .

Two corporate venture programs shutting down inside six weeks invites speculation that corporations are retreating from venture capital, but in fact the opposite is true.

Measured in dollars, corporate venture has never been stronger. According to , corporate investors participated in — venture’s strongest funding year since 2021.

, , , , and all led billion-dollar rounds into AI companies last year, per ÂÜÀòÊÓÆ” data. Nvidia by itself made more than 40 startup investments and appeared in. Meta paid $14.3 billion for its stake in Scale AI. 1Ìęand s venture arm backed Anthropic’s.

Amid this strength, though, corporate venture is also quietly splitting in two, and the proof is buried inside the record numbers. Bain attributes the elevated corporate participation , and the billion-dollar rounds trace back to the same short list of names.

Take that handful out of the data and the year looks very different. Venture capital itself went through the same sorting over the past decade, as mega-funds absorbed more and more of the capital while everyone else competed for allocation, and corporate venture is now following the same script. The people with the most at stake are the smaller funds and startups downstream.

And notice that the wind-downs are coming from serious programs. PayPal’s arm ran for a decade and , and Fidelity International manages hundreds of billions of dollars. Size never protected either one, and the dividing line runs through the mandate. For Nvidia, Alphabet, Salesforce and Cisco, startup investing is a core strategy, funded off enormous balance sheets, because their businesses depend on owning a position in the technology cycle. Nvidia backs the companies that build on its chips, and that commitment survives budget season. For most other corporations, venture is one strategic priority among several, competing for capital with the core business itself.

To be clear, there’s nothing wrong with that. When a new chief executive commits to finding , winding down even a well-run program can be the disciplined call, and disciplined capital allocation is what shareholders ask of public companies. Corporate venture has always moved in cycles, and the waves of closures after 2000 and 2008 said far more about parent balance sheets than about the returns on offer. Individual programs are mortal, but the asset class keeps growing.

When I started my career, technology drove roughly 2% of the American economy, and today it drives a double-digit share of GDP and nearly 40% of the stock market.

Who feels it first

For smaller funds and their portfolio companies, the split is already changing the math. ‘s finds corporate funds pursuing fewer, more targeted deals, and the share using the secondary market jumped from 15% in 2024 to 22% in 2025; PayPal’s Jefferies mandate takes that same path at the scale of an entire program.

When a corporate arm winds down mid-life, its portfolio companies lose a strategic backer and a source of follow-on capital at once, the smaller funds that syndicated alongside it lose their anchor for the next round, and a secondary sale replaces a committed partner with a financial buyer.

I spend my days working with early-stage venture funds, and I’m watching this pattern develop in real time: strong companies outside AI, with a departing corporate backer on the cap table, heading into rounds their existing syndicate can’t fill alone.

The lesson for startup management teams and VC fund managers is to plan for corporate capital to come and go. The pro rata rights that funds hold in their best companies become most valuable at exactly these moments, when a strategic investor steps back and ownership in a breakout company becomes available to whoever can fund it.

Smaller funds should line up committed follow-on capacity before their winners come back to market, so a corporate partner’s exit becomes a chance to buy more of a company they already know well. Founders should run the same exercise from the other side of the table and know today which investors on their cap table can carry the next round.

Corporate venture will keep growing because the forces behind it keep growing, and programs will open and close along the way, as they always have. What’s changed is the sorting: permanent capital consolidating at the top of the market, and everyone else learning to plan around that fact. The funds and founders who prepare for it will come out the other side owning more of the companies that matter.

is the founder and managing partner of , a growth-equity firm that co-invests in venture-backed companies by leveraging the unused pro-rata rights of more than 1,000 early-stage VC partners.

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We’ve spent the past 12 months navigating a relentless wall of worry: a series of macro shocks that have brought venture capital LPs into a sit-and-wait posture. When you drill down, however, the innovation economy hasn’t had a sudden collapse in fundamentals. Investors’ flight to perceived safety fundamentally misunderstands the risk profile of the moment.

The flight to ‘safety’

Feeling uncertain, the herd does what herds do: run toward the megafunds. ÂÜÀòÊÓÆ” data shows that through April of this year, 80% of all U.S. venture investment went to rounds of $500 million or more, spread across just 29 companies.

Some have called this the bifurcation of venture. Frankly, it’s a flight from venture to something else entirely.

It’s an understandable psychological defense mechanism. If you’re an investment officer, it’s hard to be criticized for backing a brand-name firm. But let’s be honest about what that trade actually is.

When a fund manages billions of dollars, it’s no longer “venture capital” as we’ve known it. To return a fund of that size, you need massive outcomes. You are no longer investing in high-conviction, early-stage firm building; you are buying an expensive index of the tech sector.

To be fair, for some LPs that index is the rational choice. The largest institutions often can’t write checks small enough for emerging managers, and can’t even reach them through a fund of funds, so broad venture exposure is a reasonable, eyes-open decision. The LPs worth challenging are the ones who could invest in next-generation managers and choose not to.

And so it comes as little surprise to me that for two years running, LPs have their venture allocations are underperforming their benchmarks. But the apparent “wisdom” of the crowd persists — invest in the big name funds. Meanwhile, more than half of them say they aren’t considering investing in emerging managers.

The result? LPs who flocked to these funds to avoid risk have simply traded venture risk (Will this specific company work?) for returns risk (Will this massive vintage actually outperform the S&P 500?).

The signal in the noise

While the herd is busy overcrowding the megafunds or sitting on the sidelines, something interesting is happening in the quiet corners of the market. True venture — the smaller, disciplined, sub-$100 million funds — keeps working. The , a study of nearly 2,500 VC funds from 2000 to 2024, found that emerging managers had an average IRR of 17.15% as compared with established managers’ 9.94%.

At my platform, we see emerging managers who haven’t stopped deploying just because the headlines got scary. They’ve continued to find and attract founders who are resilient enough to build through this market cycle that’s overwhelmingly funding the giants.

These managers are the ones still capturing the original spirit of venture: high-alignment, high-conviction investing that isn’t dependent on asset gathering fees to survive.

The savvy money is already moving

The savviest allocators are . They recognize that the “safety” of the megafunds is an illusion and that the real alpha lies in the managers who are hungry, specialized and right-sized for this specific market.

For those willing to leave the herd, opportunity awaits. Let the tourists buy the index. We’ll be over here building the future.

is the co-founder and managing partner of , a 100% woman-owned platform investing in and supporting next-generation managers in venture.

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