Ten years ago, a seed-stage startup needed a product, a team and a pitch deck to raise capital. Today, that’s just the start. Technology and strategy have become inseparable, each fueling the other, and the rules that once defined success have quietly shifted under everyone’s feet.

Last month, my firm reviewed more than 2,500 inbound applications. Here are the key shifts we’re seeing in the startup ecosystem at the seed stage.

Broadening capital strategy

Equity is a powerful tool for building high-growth companies. But it’s no longer the only option. Non-dilutive growth capital is increasingly playing a strategic role for companies with revenue visibility and clear ROI channels.

For example, we recently financed a company with $1 million in growth capital it needed immediately to expand its team and infrastructure. Raising that through equity alone would have likely taken months, with significant time and execution cost along the way.

We’re now writing financing checks like this on a near-weekly basis.

Distribution focused

Leading with a “better” product isn’t enough to propel growth. The breakout companies are investing in building stronger distribution systems — aka what founders refer to as “traction.” Distribution is a critical moat for early-stage startups. Rapid scaling is no longer achieved by launching new products; it’s through distribution loops.

Distribution is something startups can now architect intentionally with social platforms, marketplaces and other ecosystems. One of the most common founder mistakes we see is delaying the distribution strategy until after the product launch. At that stage, the architecture is harder to retrofit. Strong startups design distribution before they scale their product.

For example, some of the fastest-growing startups now design their products around existing ecosystems from day one — building apps that tap into merchant marketplaces, AI tools distributed through or Teams integrations, or fintech products embedded directly into banking and payroll workflows. In many cases, the distribution channel becomes more valuable than the underlying product itself.

One of the most common mistakes we see is founders postponing distribution strategy until after the product is built. By then, the architecture is far harder to retrofit. The strongest startups design distribution into the company before they scale the product itself.

Learning over speed

“Move fast” is often dolled out as the best startup advice. Operating in a fast-paced environment remains a strategic asset, but it is not enough to maintain a competitive advantage.

Everyone is fast. It’s no longer a unique attribute. Instead, learning velocity is becoming the defining advantage in early-stage startups. How quickly can you reduce uncertainty? Competitive edge is achieved not by executing blindly, but by closing knowledge gaps faster than everyone else. Execution without learning equals wasted motion.

The founder focus advantage

Last year, my team reviewed close to 25,000 applications for our investment funds and bespoke accelerators. The ones that stand out are the companies doing the fewest things exceptionally well. The most-fundable companies can describe their business in one tight sentence. They can also defend exactly what they are not doing.

Disciplined constraint is one of the highest-leverage traits in venture-backed companies. When we review applications, this pattern consistently stands out.

When a company is focused, the residuals compound: stronger early retention, faster iteration cycles, cleaner capital deployment. In a capital-selective market, focus compounds faster than ambition.

Based on tens of thousands of applicants, close to 82% of the ones that stayed in business a year later had a strong go-to-market foundation in their deck. GTM is built on agility and learning fast.

AI as infrastructure, not experimentation

There’s no lack of interest in AI. But there is an implementation problem. We’ve seen companies struggle when AI is approached as experimentation rather than architecture. Rather than bolting tools onto already fragmented stacks and workflows, designing intelligent systems should be mapped from the ground up.

More than 78% of the founders applying to today are leveraging AI in at least one way in their startup.

The most successful playbook combines execution with operational clarity and emphasizes infrastructure over experimentation. We’ve seen successful implementations that center around two practical paths. The first is validation, with rapid prototypes and identifying market signal opportunities before investing in a full build. The second is system, designing and integrating custom AI agents directly into operating workflows for revenue-generating companies facing operational complexity. Both are required to move AI agents from concept to capability. A disciplined system design often matters more than flashy tooling.

Marketing is the moat

Marketing execution is one of the largest performance gaps we see across early-stage startups. Startups lose when they don’t distribute fast enough once there is something worth selling. Marketing is the propeller for the distribution engine.

Most startups fail at marketing because it is a business function that becomes a founder hustle with support from one junior hire. But breakout growth requires process, cadence and accountability. That’s not achievable without an experienced team and clear plan.

One of the biggest mistakes founders make is treating marketing as something that starts after launch. Founders must create unique strategies, test them and then analyze what works and what doesn’t. From there, they must keep iterating and creating to unlock the most product-market fit and traction.

If we see classic strategies in a pitch deck, it is an auto-reject. And beyond being unique, your strategies must have been tested by your team.

The key is simple: Test ideas early, measure what actually works, refine aggressively and scale the strategies that compound over time.

, a serial technology entrepreneur since age 12, is now a value-driven venture capitalist with a track record of backing more than 1,000 startups across the globe. He is a co-founder and general partner at , one of the world’s most active venture capital firms. Before becoming involved in venture investing, he built and scaled into the world’s largest debate-focused social network and edtech platform, reaching millions of users and serving students across more than 500 school districts, colleges and universities.

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One of the strangest things about the current AI cycle is just how fast the math changes for the people building it. You can be a 20-something founder who feels like you are still in the warm-up phase of your career, yet on paper your equity is already life changing. Or a mid-level engineer who went from holding startup options to staring at a substantial personal balance sheet practically overnight.

The gap between life experience and the sudden reality of managing serious wealth is widening as AI-native companies reach major valuations faster and private company liquidity arrives earlier.

A of more than 3,400 founders and senior leaders across 20 countries found that AI-native startups are reaching billion-dollar valuations in about 3.5 years, roughly half the time it took before generative AI. They are doing it with about half the staff.

We have seen an even more compressed version firsthand. We recently advised founders who went from launching their company to a major liquidity event in less than a year.

When the money outpaces the mindset

For decades, tech wealth followed a more predictable script. Significant personal wealth often accumulated alongside a long career. Equity vested over years, responsibilities grew and additional grants often followed. If everything went right, an acquisition or IPO marked a visible transition into a very different financial reality.

Today, that boundary is much less clear. AI capabilities allow companies to grow at a much faster pace.

A young founder can suddenly face questions that used to come much later in life. What are their long-term personal goals? What should the new capital be used for? What does financial independence mean for someone who may still be figuring out what they want their life to look like?

These are not always questions that can be answered overnight.

Compounding this is the fact that one doesn’t have to wait for an IPO to de-risk. Tender offers and secondary transactions allow founders and employees to turn part of their equity into cash while the company remains private.

Take as an example. While still only 3 years old, the company authorized a $100 million secondary sale for staff at a $6.6 billion valuation. By February 2026, it had at an $11 billion valuation.

For someone inside a company moving at that speed, the sequence can look very different from the traditional startup script. It is a dizzying loop of grants, valuations and a sudden liquidity window. All of this can happen long before an IPO.

Flexibility is the name of the game

A sudden liquidity event can make financial independence a realistic goal. It may make buying a home possible, even while someone is still single or has no idea where they want to live long term. It may allow them to take care of parents or fund another entrepreneurial chapter.

The pace of these cycles can also be contagious. Opportunities seem to be everywhere. At the same time, a founder may still be taking substantial risks with the current venture and have very little idea what life will look like in five years.

When we advise technology executives and founders in this position, we try to leave room for several possible paths while the broader picture is still developing. Some capital may eventually support long-term family security. Some may need to remain available for opportunities or life changes that do not exist today.

A future business endeavour, a career change, a relocation to another country, or other less conventional ideas can change the picture again. Some of these moves can be made today, but others need time to develop.

A company may compress 10 years of growth into three, but people cannot compress 10 years of life into three. Ignoring that gap is where real risk can build.

Valuations and liquidity can move incredibly fast. Decisions about family wellbeing, security, career and the future still move at a human pace. Your financial architecture needs to respect the difference.

is co-CEO of , where he works with founders, senior technology executives and families on wealth strategy, liquidity events and long-term financial planning. Before moving into wealth planning, he spent many years in the technology industry and writes about the intersection of technology, equity and personal wealth.

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An overwhelming majority of early venture-backed startups utilize a standard four-year vesting schedule with a one-year cliff. It seems like the ultimate one-size-fits-all template. Yet almost no one talks about how this default framework routinely causes bitter legal battles over founder equity, wasting hundreds of thousands of dollars on litigation that could have been avoided.

By the time a founder leaves or gets terminated, the damage is already done, leaving the company stuck with costly “dead weight on the cap table.”

What dead weight actually costs your company

When a co-founder with substantial ownership leaves — voluntarily or involuntarily — they often walk away with a massive, permanent piece of the company.

From a VC’s perspective, and that of the remaining partners, this is pure dead weight. You now have someone holding 15% to 20% of the equity who is no longer providing any value. Of course, contractually it’s theirs, and they’ve usually earned it.

Practically, it can break the company in three distinct ways:

• It kills motivation: The remaining team has to grind for years toward an IPO or acquisition, knowing that a fifth of the exit payout is going to someone sitting on the sidelines.

• It breaks future dilution pools: When you need to bring in new executives or raise a new VC round, your outstanding share count is artificially bloated by a departed founder. Issuing a simple 1% option pool suddenly requires 20% more shares than it otherwise should.
• It creates voting and control nightmares: If a departed founder owns 20%, you need their signature on standard investment documents and major shareholder votes. Even if they didn’t leave under bad circumstances, their risk tolerance and timeline are completely misaligned with the active team.

The shrinking threshold of tolerance

Five to 10 years ago, investors might have tolerated a departed founder holding 5%, 10% or even 20% of the company. Today, that threshold has collapsed. Many VCs will now insist that a former founder hold no more than 2.5% of the cap table.

However, because the standard four-year vesting agreement has no contractual mechanisms to claw back shares, companies start looking for alternative ways to do so when a founder leaves.

Initially, this usually involves pressuring them to give up shares “for the goodwill of the company.” When that fails, they sic investors on them, threaten their professional reputation, and sometimes resort to litigation.

I see this over and over. We frequently see litigation that is nominally about intellectual property or confidentiality, but everyone knows the real goal is simply to get the equity back. These are multi-hundred-thousand-dollar lawsuits that never would have been filed except as a desperate attempt to claw back departing founder equity.

How to fix the problem

The four-year vest, one-year cliff standard is a very lemming-like system in which founders follow the same standard as everyone else.

They often pull the language in equity agreements off automated legal platforms because it’s cheap, fast and requires minimal thought. If we want to fix this problem — and I believe every startup should — the industry needs to converge on a new, more nuanced position built into founding documents from day one.

We can split the proposed solve into two categories:

Fixing control and voting (the easy part)

Founding documents can automatically strip voting power upon departure. It’s simple enough to build in an obligation to hand over a voting proxy to the current CEO the moment a founder leaves, along with a mandatory drag-along clause that requires them to comply with future sales or investment rounds. Alternatively, a class of nonvoting shares can be created for departed founders and other service providers.

Fixing the economics (the hard part)

Four years is too short. It does not match the actual lifetime of a modern startup heading toward an exit. There are structural changes to the standard founder equity and vesting templates that could address this problem:

• Extend and back-weight vesting: Move to a five- or six-year schedule and stop using even distributions. Force back-weighting — such as 5% in year one and 10% in year two — to reward longevity and protect the cap table if someone leaves early.
• Pre-agreed buyouts and forfeiture over time: Agree upfront on a methodology and price for the company to buy back vested shares post-termination, perhaps leaving the departed founder with a permanent floor of 2%. Alternatively, tie the equity to timing: If the company sells three months after a founder leaves, they keep their 20% because they built that value. If it sells four years later, a portion of that equity should automatically forfeit back to the pool.
• Automatic share class conversion: Build a mechanism where a departing founder’s equity automatically converts into a separate class of stock with no voting rights and inferior economic rights.

A watch-out for minority founders

Minority co-founders face the highest risk of litigation aimed at clawing back their equity. They should push for pre-agreed severance, clear definitions of “cause,” and accelerated vesting protections before signing paperwork.

Even if the dominant founder refuses those terms to satisfy institutional investors, having the conversation is a critical de-risking tool. Simply observing how your co-founder reacts to these structural negotiations can provide a lot of intel. Are they hostile and defensive? Are they secretive, claiming “the lawyers said no” without CC’ing you on the emails?

How a co-founder handles the equity conversation at the outset can provide valuable insight into how they will handle conflict when the stakes are much higher.

Protecting the cap table from day one

Right now, the venture ecosystem is still operating within an outdated, broken structure. VCs want clean cap tables, remaining founders want motivated teams, and departing founders want to be fairly compensated for the early risks they took. But the current four-year vest, one-year cliff template satisfies none of them. Instead, all it does is ensure that when a founder relationship ends, companies are left hobbled by dead weight on their cap table.

Startups face high-stake, bespoke risks. Equity structures should reflect that reality. Engaging a lawyer to properly customize and document these relationships at the outset isn’t hard or expensive. What is expensive is spending hundreds of thousands of dollars later on a lawsuit, searching for leverage to claw back equity that should have been protected from the very beginning.

is a partner at and an accomplished startup lawyer and litigator specializing in corporate, transactional, intellectual property and complex commercial matters. He has advised startups on multimillion-dollar financings and acquisitions and represented clients in sophisticated intellectual property and corporate disputes. Siegel is licensed to practice in both California and New York.

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If your pitch still leads with “we use AI,” you’re describing infrastructure, not a business. Consider that 97% of the products nominated for this year’s Product Awards are deeply integrated with AI, highlighting the extent to which the days of AI as a differentiator are officially gone.

The real moat investors are seeking today is something many founders cannot identify: What will survive a well-funded competitor who could launch tomorrow with a better model.

We analyzed şÚÁĎłÔąĎ data on 576 venture-backed, AI B2B companies that raised $50 million-plus rounds since the start of 2025 using Hamilton Helmer’s framework and layered in insights from Products That Count’s 600,000-plus product leader community.

The data gives a clear answer: When building is nearly free, the only moats that hold are those that a model cannot generate.

Two of them work without requiring you to out-raise . Two of them are traps. And one isn’t a game most founders are playing.

Counter-positioning: The moat that costs nothing to defend

Counter-positioning is what happens when a newcomer builds a business model so structurally different that the incumbent can’t copy it without destroying their own economics. versus is the canonical example. Blockbuster could have matched the subscription model, but doing so would have gutted late-fee revenue, which kept their stores alive. So they didn’t, until it was too late.

In the AI era, this power is rare and underutilized. Only 5% of companies in our dataset leverage counter-positioning. Investors price that scarcity at a median enterprise value of 5.3x per dollar raised — the highest multiple of any power in the analysis.

The pattern shows up in vertically integrated AI insurers that sell directly to employers, a model traditional brokers can’t replicate without cannibalizing their own relationships and underwriting margins. It shows up in AI-native revenue management systems that would gut the high-margin consulting revenue legacy vendors depend on if those vendors tried to match them.

The incumbent sees the threat. They choose not to respond. That rational inaction is the moat.

For founders, the diagnostic question is this: Could a well-resourced incumbent copy your model if they wanted to? The right signal is answering “technically yes, but it would cost them more than it would cost us to build.”

Network economies: The moat that builds itself

Network economies arise when a product becomes more valuable to each user as more users join. At scale, this tends toward winner-take-all outcomes within the network’s boundaries, whether in geography, professional context or industry vertical. is the textbook case: More recruiters attract more candidates, which in turn attract more professionals, which in turn attract more recruiters.

In our dataset, network economies appear in only 5% of companies, but command a 4.2x multiple. That makes it the most capital-efficient path to a strong valuation premium available to founders today.

The B2B variant here is particularly underappreciated. Rather than individual users as nodes, the network connects companies: brands to factories, advertisers to audiences, platforms to partners. Every new participant makes the network more valuable for every existing participant.

The data that accumulates across those interactions — costing, production cycles, audience behavior — compounds in ways that become progressively harder to replicate.

Getting both sides of a two-sided market to commit simultaneously is hard. But founders who solve the cold-start problem own something that a well-funded competitor with a better model still cannot buy.

What looks like a moat but isn’t?

At 44%, cornered resources like proprietary data, unique IP and exclusive access command the second-highest prevalence in our dataset. But they also have the worst multiple: 2.6x. Because investors have watched too many proprietary datasets get eroded by foundation models and synthetic data, if a data advantage doesn’t compound in ways that get harder to replicate over time, it can’t be considered a moat.

Switching costs are the most crowded power at 37%, and look like a moat because customers really don’t leave.

But the cost of building them creates a problem, because the requirement for deep enterprise entanglement — baked-in instrumentation, institutional memory, rearchitecture risk — means expensive sales cycles before the stickiness kicks in. The multiple is comparable to network economies (4x), but the capital required to reach it is roughly 10x higher. It’s a viable path for founders who build a product-led growth motion to reduce that cost, or who engineer a reason for users to collaborate on the platform, converting switching costs into network economies over time.

Scale economies are not the game most founders are playing. The median multiple, excluding OpenAI and , collapses from 6.1x to 3.2x, and 88% of the category’s capital belongs to those two companies. Believing your unit economics improve with growth does not equate to building a scale moat. The gap between the two is measured in billions of dollars that most startups will never raise.

The only question that matters

Every company in this dataset has AI in its product. The ones commanding premium multiples have built something underneath the AI that a model cannot generate on its own.

That something is structural. It lies in the business model design or the network architecture, rather than model quality, feature set or data volume. It answers the question every founder should be able to state in one sentence: What about my business would survive a competitor who starts today with more capital and a better model?

If you can’t answer that question, you’re building a product. The founders commanding 4x to 5x multiples are building a power.

Ěý is the founding managing partner of and board chair at . As a venture capitalist honored on the Kauffman Top 30 Index and Power100, she invested in pioneering companies including , and . She earned a reputation for developing products that people love during the cloud and mobile era, when she built products that billions of people use at and , won industry awards and nominations from and the , and wrote an award-winning bestseller on what makes a great product. She holds a master’s in electrical engineering and a MBA, and is a Kauffman Fellow and member of .

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At , we are regularly asked to put numbers on something that is, by nature, hard to pin down: the health of an innovation ecosystem. Governments, corporates and investors all want the same thing: A benchmark that tells them how their country or region stacks up against the rest of the world.

The problem is that no two ecosystems look alike, and any methodology rigid enough to allow cross-country comparison risks flattening exactly the traits that make a place distinctive.

This is the tension we have been working on for years, refining a model that is standard enough to compare Boston with SĂŁo Paulo, Tel Aviv with Turin, yet flexible enough to capture what is genuinely different about each. This week in Seoul, we presented the latest application of that work: The Innovation Economy of South Korea. It’s a good occasion to lay out, briefly, how we think the exercise should be done.

Start from the Pyramid

Every innovation ecosystem can be represented as a pyramid. At the base sit startups: venture-backed technology companies still in the process of building and proving their business.

Move up and the base narrows into scaleups, or companies that have shown enough traction to raise more than $1 million in venture capital. Scaleups are the most structured, most visible output of an ecosystem, and for that reason they are among the best proxies for its maturity: You can plot their number, growth rate and sector mix against the Innovation Ecosystems Life Cycle Curve and get a reasonably honest read of where an ecosystem stands.

At the very top of the pyramid are the outliers that matter disproportionately: scalers and super-scalers, companies that have broken out of their home market and achieved real international scale. A handful of these can do more for a country’s innovation profile than thousands of early-stage startups —Ěýwhich is why counting them separately, rather than burying them in an aggregate “startup” number, is essential to any serious methodology.

Alongside this VC-backed pyramid runs a second, often under-measured population: innovative SMEs, or established, revenue-generating small and mid-sized companies that compete on technology and innovation rather than venture funding.

Ignore them and you miss a large part of the real economy’s innovation capacity, particularly in ecosystems — Korea among them — where corporate-led and government-backed innovation has historically mattered as much as the VC route. Many emerge as bootstrapped companies. Some remain independent, while others may eventually raise external funding and move upward into the startup and scaleup layers.

All these tech companies stem from the knowledge base generated by local universities and research centers. Together, these layers represent the technology supply of an ecosystem.

Why the supply side alone isn’t enough

Counting tech companies gives you the supply side of the equation. But supply only turns into economic impact when it meets demand, and demand is largely, though not exclusively, represented by corporates, both local and international.

Corporates benefit from the solutions developed by startups, scaleups and innovative tech companies, while also potentially supporting their industrialization and growth through acceleration programs, venture client models, CVC and M&A. This interaction between technology supply and corporate demand is what turns innovation into economic impact and company growth — which is why measuring it, not just the supply side, is essential.

The supply side also interacts with local, regional and global B2C markets: Consumer demand is the other half of the demand equation, particularly for companies whose growth path runs through the market rather than through a corporate relationship.

The evolution of an ecosystem is everywhere, fueled by capital and public support. Private capital comes from angels, VCs and CVCs, providing companies with the resources to develop, commercialize and scale. Public support comes through subsidies, grants and government programs, delivered either directly or indirectly through innovation agencies, ecosystem builders and other innovation brokers.

The stronger the connection between these different sources of capital and the companies sitting in the pyramid, the faster companies can move from one layer to the next. The less advanced an ecosystem is, the more public capital needs to be fueled into the ecosystem to bridge the gap.

When an ecosystem reaches critical mass

As a local ecosystem reaches a certain threshold in volume, density and quality of companies — typically the Star stage of the Innovation Ecosystems Life Cycle Curve — it starts attracting increasing interest from external players. Investors, multinational corporations and government agencies begin establishing a local presence, because proximity provides better access to talent, technology, deal flow, partnerships and market opportunities.

One concrete way to measure this external attractiveness is by counting corporate innovation outposts set up by multinationals, alongside government innovation outposts set up by foreign countries, regions or cities. It’s an indicator that tends to lag the ecosystem’s real progress by a couple of years, which makes it a useful confirmation metric rather than an early signal, but a valuable one nonetheless.

The Korean innovation pyramid

As for our latest count, 3,359 scaleups sit at the top of the South Korean innovation pyramid.

This makes South Korea the eighth-largest national innovation ecosystem in the world, with Seoul ranking as the 11th most developed ecosystem globally.

Below the scaleup layer sits a much larger base of approximately 10,000 startups, alongside an even broader base of approximately 25,000 technology companies (innovative SMEs) that are not venture-capital backed.

The enabling ecosystem includes approximately 700 investors and more than 550 innovation brokers, both public and private, powering more than 800 unique programs in support of entrepreneurship and innovation.

Beyond the number of scaleups, which is calculated analytically, all other figures represent our best-effort assessment of the different components of the ecosystem. The analysis starts from government data and consolidates available data sources. While this work is continuously refined, we believe that open-sourcing this data through the MTB Innovation Ecosystem Platform provides a further opportunity to build (innovation) bridges.

The demand side completes the picture:

• About 130 local companies with structured open innovation activities.
• Roughly 90 international companies with an identified innovation outpost in South Korea.

These figures are also subject to continuous monitoring and refinement as the ecosystem evolves and new players and activities emerge.

The point of measuring

None of this is an academic exercise. The output of this kind of methodology — as we discussed applying it to Korea this week — is meant to be actionable: It should tell a government where its ecosystem sits on the Innovation Ecosystems Life Cycle Curve, where the bottlenecks are, and how it compares to peer economies pursuing the same transition from startup nation to scaleup nation.

Get the methodology right, and the numbers stop being a scoreboard and start being a diagnosis.

A decade of divergence

This is exactly what happened in South Korea.

Just 10 years ago, Korea was about 40% smaller than Japan and Germany, comparable in size to Australia and Spain, and slightly ahead of Singapore and Italy. Fast-forward 10 years, and Korea had become the clear leader of the pack. With 3,233 scaleups in 2025, Korea:

• Started pulling away from Germany and Japan, building a positive gap that appears difficult to close;
• Grew to more than double the size of Spain — Korea’s most comparable European tech ecosystem in 2015; and
• Nearly doubled the size of Singapore, the other Far East tech haven.

Strategy, not luck

The extraordinarily rapid growth of the Korean innovation economy is not the result of chance, but rather of more than two decades of forward-looking strategic government direction.

After establishing the basic framework for a radical increase in R&D spending — from 2%-3% of GDP to a minimum of 5% in 2008 — Korea, in 2013, underscored the centrality of tech entrepreneurship as a strategic pillar of the economy, alongside the launch of TIPS, or Tech Incubator Program for Startup, to boost the early-stage segment. Subsequent policies expanded on this framework by supporting the scaling process of local tech companies. In particular, between 2014 and 2015, 17 regional centers of innovation (CCEI — Centers for Creative Economy and Innovation) were established, bringing together local large conglomerates (chaebols) and startup incubation activities.

New regulatory frameworks allowed greater freedom for experimentation by tech companies, opened the door to new forms of financing, and provided incentives for scaleup financing. More recently, starting in 2022, new dedicated strategies and instruments have contributed to a major shift, concentrating investments and tools on deep technology innovation.

The figure below highlights the impact of some flagship innovation-related policies enacted by the Korean government, juxtaposed with the historical growth of the overall scaleup ecosystem.

Get the full story in Mind The Bridge’s report, available for free download .

is chairman of and a professor at . He is a serial entrepreneur who has started three startups in his career, the last of which is , among the five Italian scaleups that have raised the largest amount of capital. He is recognized among the leading international experts in open innovation and has wide experience in setting up and managing open innovation projects —Ěýventure clients, venture builders, intrapreneurship, CVCs — with large multinational companies, as well as advising and training on this subject. Onetti has a column on () and several other tech blogs.

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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.Ěý

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.Ěý

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.Ěý

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.Ěý

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

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.Ěý

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.Ěý

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.Ěý

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.Ěý

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.Ěý

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.Ěý

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 1Ěýagents 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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