The conventional fundraising playbook goes something like this: Build your list, craft your deck, start warming intros six months out, and prepare to spend the next year in a loop of coffee chats and follow-up emails that mostly go nowhere.

I did none of that. Not because I had some genius alternative strategy — but because I figured out, early and somewhat accidentally, that the best investors don’t want to be pitched. They want to discover you.

Every meaningful check in our $14 million round came from a personal encounter. A talk I gave. A dinner I attended. A conference I almost didn’t go to. If there’s a single lesson I’d want every first-time founder to take from my fundraising experience, it’s this: stop optimizing your outreach and start engineering the rooms you’re in.

Full disclosure: I went to . I know what you’re thinking: “Of course she raised $14 million — she had the network handed to her.” And look, I won’t pretend the alumni connections didn’t open certain doors. They did. It’s a competitive advantage most founders don’t have, and I won’t insult your intelligence by pretending otherwise. Harvard’s own data shows that have gone on to found for-profit or nonprofit ventures, collectively launching over 146,000 companies globally. But even at Harvard, ’s — and the vast majority of those 146,000 companies never reach meaningful scale. If a diploma were enough, far more of my class would be on .

What a diploma doesn’t give you is a mission people instinctively care about. I’m a Gen Z neuroscientist building technology to solve something my generation knows firsthand: the mental health toll of a world where social media has displaced real human connection. That mission travels on its own.

Investors don’t need convincing that loneliness is a crisis or that the way teenagers relate to each other has fundamentally changed — they see it in their kids, their families, the culture around them. When your problem is self-evident to the people in the room, the pitch is halfway done before you open your mouth.

Reverse the power dynamic

The obvious problem with cold outreach is noise. A partner at a top-tier fund receives hundreds of cold pitches a week. Yours lands in a full inbox alongside dozens of others with equally compelling subject lines.

Even if your deck is exceptional, you’re asking someone to extend trust to a stranger, based on a document, before any human relationship exists. According to , at all. Among those that are read, the conversion rate — the share that leads to any meaningful next step — sits at , even for founders who do everything right.

But there’s a less obvious problem: cold outreach inverts the dynamic you actually want. When you cold pitch, you are the one seeking. You are, structurally, in a position of need.

The investor holds all the leverage.

What I learned — through experience I did not fully understand until I looked back on it — is that every great investor relationship I have started from a moment where they came to me. And the engine behind almost every one of those moments was a room where I was speaking, teaching or simply showing up as someone who had something worth saying. I was never chasing.

Find the right rooms

None of the relationships I’m about to describe started with an email. They started with a room, a talk and a reason to be there that had nothing to do with raising money.

Any room works — if you have something worth saying. You don’t necessarily need to find yourself in prestigious halls. Any room where the right people are present, and you’re there as a voice rather than a business card, will do. A panel at a tech and wellness summit. A founder dinner in Soho where I gave a 10-minute talk on the neuroscience of friendship — the host made three introductions the following week. None of these were “investor events.” They were rooms where people who cared about the mission happened to be. In New York and San Francisco, these happen every day — on , on , through your alumni network. You don’t need a big name to get a small stage. You just need to show up and ask.

The stage size doesn’t matter. Being on it does. The big conferences won’t invite you to speak until you already have traction. That’s fine — because the rooms that actually move the needle are often smaller anyway. , founder of , came through someone who heard me at the . approached me after a sports dinner in London — a room I was in because I’m a marathon runner, not because I was fundraising. The investment followed because we were aligned on something that did matter.

Speak about the problem. Not the product. The talks that generated the most meaningful investor relationships weren’t the ones where I pitched . They were the ones where I spoke about loneliness, neuroscience, and what technology can and cannot do for human connection. The subject matter attracted people who already cared about the mission. By the time anyone asked about the company, they were already bought in on me.

Trust compounds across encounters. One of our key shareholders — a major fund — started with a partner I first met at a conference in Dubai. We ran into each other again at a New York event. And again after that. Three encounters across three cities, each one building a little more context, a little more trust. By the time we were both ready, the relationship already existed. Was it luck? Maybe. But I keep showing up in rooms where the right people are. At some point that stops being luck.

The deck gets you a second meeting. The relationship gets you a yes. An investor isn’t just a wallet. They’re betting on the change you want to make in the world — and on you. The relationship that leads to a check often starts with something human: a shared interest, a run, a conversation that had nothing to do with fundraising. That’s not a bug in the system. That’s the system.

The real lesson: Cold outreach has its place. It works for some people in some contexts, and I won’t pretend otherwise. But if you have a mission that is genuinely worth talking about — and if you can speak about it with conviction — the most efficient thing you can do is engineer visibility in the rooms where your investors already are. Not as a founder seeking capital. As a voice worth listening to.

That’s the posture that builds the kind of investor relationships where someone approaches you after a dinner, or appears in your orbit three times across three cities until trust quietly accumulates. That’s the posture that turns a shared run or a sports dinner into a check from someone who genuinely believes in what you’re building.

The goal isn’t to get lucky. The goal is to make yourself impossible to miss — every time you have a mic, and every time you don’t.

is the founder and CEO of , a Gen Z platform reshaping how friendships begin and grow in person. A solo female founder and member of Gen Z herself, she holds degrees from and in computational neuroscience, neurobiology and behavior. Under her leadership, Clyx has raised $14 million in Series A funding backed by ‘s , co-founder , F1 World Champion , and , and facilitated more than 500,000 real-world friendships across six cities worldwide.

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The global innovation economy remains largely defined by agglomeration dynamics. Worldwide, 19 ecosystems dominate the innovation landscape, increasingly concentrating innovation demand (corporates) and supply (scaleups) — attracting further growth capital (investors).

Meanwhile, other ecosystems struggle to achieve a meaningful presence on the global innovation map and are at serious risk of technological disruption and economic downfall.

Yet something is happening below the surface. Over the past decade, the composition of the Global Innovation Ecosystems Life Cycle Curve changed dramatically, as the number of scaleup ecosystems worldwide has more than doubled.

The trend is not stopping just here: we expect these figures to even triple in the coming years.

In this new scenario, emerging innovation economies hold the potential for disrupting the agglomeration paradigm, toward a new scheme of interconnected networks of specialized local innovation hot spots.

Among them, there is also Africa. While the continent still lacks ecosystems at the most advanced stages of maturity, it now counts four ecosystems at the startup stage and 40 at the standup stage, compared with respectively 25 of those 10 years ago, according to by my organization, , in collaboration with and .

Africa: the awakening giant of the coming decade?

As of today, Africa’s innovation economy includes 883 tech scaleups that have raised a combined $24.7 billion. Despite this progress, the continent still represents only about 1% of global figures.

The African innovation landscape remains highly concentrated around four main hubs: South Africa, Egypt (North-East), Nigeria (West Africa) and Kenya (East Africa). The North-Western corner of the continent still lacks a dominant hub, although Tunisia, Morocco and Algeria remain the leading candidates.

A testbed for clean technologies?

Emerging innovation economies that thrive on the global innovation map typically build on top of highly specialized, unique local strengths.

Our recent analysis has identified clear evidence that Africa holds significant potential over the development of clean energy systems and technologies.

The relative prominence of the cleantech sector in Africa is evident from the data:

• Africa is home to 95 cleantech scaleups, representing roughly 11% of the total scaleup base.
• Collectively, they have attracted approximately one-fifth of all capital deployed to African ventures.
• Cleantech has also generated a disproportionate share of high-growth leaders, accounting for around 20% of both scalers (scaleups that raised more than $100 million) and super scalers ($1 billion-plus).

Within cleantech, a highly specialized vertical is also emerging, what we might call “gridtech”:

• It comprises 16 scaleups (17% of the cleantech total) and two scalers (25% of total).
• It has attracted around 30% of total cleantech funding.
• Africa’s sole cleantech tech giant, Kenya-based , operates within this gridtech vertical.

That said, the numbers still point to a gap.

The elephant in the room

The main challenge is the grid infrastructure deficit, which remains the primary bottleneck to scaling energy system technologies. As shown in the map below, Africa’s grid infrastructure is highly fragmented: High-voltage networks are concentrated in a few densely populated areas, while large parts of the continent remain largely disconnected.

As a result, grid infrastructure development and electrification are key to unlocking Africa’s growth — consider that Africa still accounts for only about 5% of global energy supply — and its innovation potential.

At the same time, the continent holds world-class renewable resources, including approximately 13% of global technical hydropower potential and around 60% of the world’s best solar resources.

Africa’s energy system is expanding, but fully unlocking its economic and innovation potential will depend on accelerating electrification and strengthening grid infrastructure.

Blended finance will be critical to enable this growth. Both private and public capital are required: private capital drives innovation, while public finance enables foundational infrastructure such as grid expansion.

In particular, private capital needs to be complemented by structured public finance initiatives to address the inherent limitations of a relatively small domestic VC market, which remains heavily focused on early-stage investments.

Public capital will be essential for infrastructure development. In gridtech especially, public investors are expected to account for up to about 80% of total investments by 2030, reflecting the capital intensity and risk profile of grid infrastructure.

International capital still dominates the market, with approximately 69% of active investors originating outside Africa, underscoring continued reliance on foreign capital despite growing local participation.

Get the full story in our report:

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 — venture 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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Defense, energy, robotics and government have historically been classic no-go areas for VC investment. These “hard” industries have slow procurement cycles, tight regulatory oversight and high-friction customer migration in common. Legacy software vendors serving them have benefited from a barrier of complexity to innovate slowly without facing the risk of customer churn.

This made the victims of this year’s AI anxiety-driven sell-off all the more dramatic. Software juggernauts serving heavy industries — , , , — have gone from safe bets to being the subject of investor scrutiny.

While headlines have attributed that sell-off to quick-fire launches of tools for vertical industries, there’s more at play. The macro trend is a newfound founder enthusiasm to build AI-native entrants in legacy industries, and the backing they’re enjoying from VCs that can see the once-in-a-generation opportunity to disrupt entire industries.

Why investor perceptions are changing

Context is important. Geopolitical instability, supply chain pressure and energy security concerns have placed industrial resilience at the center of national policy.

Be it the U.S. or across Europe, policymakers are prioritizing investment in grid upgrades, transportation networks and public sector infrastructure, while also re-examining procurement and compliance systems that have slowed the adoption of emerging technologies that could bring said industrial resilience about quicker.

At the same time, quick advances in AI and agentic systems make it possible to build a new class of AI-native software tailored to “hard” industries through deep integration with verticalized tooling and specialist automation of critical workflows.

Age-old incumbent moats, like cumbersome migration cycles that put businesses off moving to new software providers, are also being challenged as embedded automation cuts migration processes down from weeks to days.

The creation of software in and of itself has become commoditised in the AI era, and more investors are spotting that operational depth, intuitive UI/UX, speed to market and seamless integration into complex real-world systems are traits of high-quality vertical software that startups are well-placed to build.

Investors are also realizing that most of the available value from horizontal SaaS has been extracted. In those early post-ChatGPT years, VCs widely backed AI companies building for non-regulated SMB adoption — exactly the audience that foundational model players like and Anthropic are now making inroads with as they push into enterprises. Foundational models are general in nature, and their verticalization can therefore only stretch so far. Given this, AI-native products built for heavy industries are compelling and competitive propositions for VCs.

Growing faith that incumbents are vulnerable

There’s always been lots of skepticism among investors and tech executives that AI startups can meaningfully challenge incumbents that have been on top for decades. But those companies are operating over sprawling product architecture and processes that were built in the pre-AI era.

Pivoting from that state of affairs to AI-native systems is a massive undertaking, whereas new companies are being launched with those systems in place from day one. Incumbents also have a low incentive to innovate at pace when customer churn is limited. But in the current context of breakneck speed improvements to AI models and agentic systems, waiting for churn to show up will be too late.

Scepticism also risks overlooking the profile of outstanding founders building AI-native challengers. Some of the fastest-growing startups in defense, energy, government and the public sector are led by people who came directly from the same industries they are transforming. Their understanding of sector constraints and operational realities gives them an advantage over general software providers that lack the same specialism and experience.

Picking up pace

Savvy entrepreneurship and VC investors are colliding to make a play for hard sectors. Once seen as off-limits due to procurement complexity or regulatory burden, these sectors represent huge, untapped potential in the new AI-native era.

The emerging companies offering solutions designed for these industries with deep, vertical-specific tooling integration and critical workflow automation are well placed to command a growing share of overall AI funding as they serve customer pain points that have gone unanswered for years.

We are talking about disruption within markets worth trillions. The scale of the opportunity for growing VC interest in sectors they’ve historically avoided is no mystery or miscalculation. The vision is an ambitious one. Rather than simply building better software, the foundational sectors of the world economy are about to be reimagined.

is a partner for the U.S. and Europe at early-stage venture capital firm . He currently oversees the deployment of the firm’s latest $1 billion fund, backing a range of AI-native startups building to disrupt legacy industries and business processes. In a personal capacity, Cuvelier wrote an angel check for at pre-seed.

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Every year for the past several years, the same prediction circulates: This is the year the IPO market comes back. We said it in 2025. We said it in 2026. We’ll probably say it again in 2027.

And every year, a handful of headline-grabbing offerings get held up as proof. This cycle it’s , and . The narrative writes itself: the window is open, the giants are listing, the market is back.

But here’s the catch: those aren’t IPOs for the rest of the market. They’re exceptions to a rule that has been hardening for 30 years.

The IPO market isn’t closed. It’s shrinking.

The instinct is to treat the IPO drought as cyclical, a consequence of rate hikes, market volatility or investor risk appetite. Fix the macro, the thinking goes, and the listings follow.

The data doesn’t support that story.

In 1996, more than 8,000 companies were listed on U.S. stock exchanges. Today, fewer than 4,000 are, even as the U.S. economy has tripled in size.

The bar to go public has moved in one direction.

In 1980, the median company went public with around $64 million in revenue in today’s dollars. Today, the typical IPO candidate has revenue that would have made it a mid-cap public company a generation ago.

The result: Companies are staying private far longer, and the liquidity that shareholders were counting on keeps getting pushed out.

Every time the IPO window “reopens,” it reopens at a higher threshold than before. Waiting for conditions to return to historical norms isn’t a strategy. It’s a bet against a structural trend that has outlasted every rate cycle, bull market and recovery in recent memory.

The companies left behind

When the bar rises high enough, it doesn’t just delay IPOs. It eliminates them.

There are thousands of private companies in the United States today with $50 million, $100 million, $200 million in annual revenue, with continued growth. Previously, companies at that scale formed the backbone of the public markets. Today they’re still private, and most will stay that way.

Not all of them are great businesses. Some raised at 2021 peak valuations and are quietly running out of runway. But a real subset has grown past the early venture stage. They have revenue, margins and years of operating history. The IPO was supposed to be the exit. For most of them, it won’t be.

Who’s actually suffering

Employees at these companies made a bet: below-market salaries, equity instead of cash, years of building. Their equity was supposed to be liquid by now. It isn’t. Meanwhile, life has continued: mortgages, children, aging parents, career crossroads.

I lived this at . When I left, exercising my options triggered a tax bill I couldn’t afford without finding liquidity for shares I didn’t know how to sell. The market for these shares exists in theory. In practice it’s opaque, fragmented and slow. A transaction that should take weeks can take months, if it closes at all.

Venture general partners are in a different bind. Their funds are locked in companies with no exit path. Distributed to Paid-In capital is near historic lows. Limited partners who expected returns from prior vintage funds are still waiting, either holding back re-commitments or concentrating capital into the megafunds that can generate deal flow regardless of exit conditions. The mid-tier manager without DPI is struggling to raise.

A small number of the most prominent companies can run tender offers, giving employees a company-sponsored, structured opportunity to sell their shares.

For everyone else, there are brokered secondary marketplaces that work, slowly and imperfectly, for a narrow slice of the most in-demand names. According to , 90% of all venture secondary volume was concentrated in just 15 companies last quarter. For the rest, the market barely functions.

We’ve been here before

This situation has a historical parallel most people in finance have forgotten.

In the late 1800s, the was the only legitimate listing venue, and it was selective. Hundreds of real companies couldn’t meet the requirements, so brokers took matters into their own hands. They gathered on Broad Street, outside the NYSE, and began trading unlisted stocks on the curb. Literally on the sidewalk. It was chaotic, informal, fragmented. No centralized pricing. No standardized process. No real infrastructure.

But the companies were real. And the demand was real.

Over time, the curb traders organized. They moved indoors. They built rules and infrastructure. The Curb Market became the . The companies that traded there weren’t defective, the system was.

The private secondary market today looks a lot like that sidewalk. Fragmented brokers. Inconsistent pricing. Transactions that depend on who you know. The companies being traded are real. The demand is real. The infrastructure doesn’t exist yet, but it’s coming. Markets that serve real economic needs don’t stay informal forever.

The original Curb Market didn’t fail. It grew up. What’s happening in private secondaries today will do the same. The only variable is timing, and the shareholders waiting on liquidity are the ones absorbing the cost of that delay.

�� is the founder of and managing partner of Earlyasset Capital, where he is building infrastructure for and investing in the venture secondary market. Earlier in his career, he was part of the original founding team at .

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• Forecast: 2024 Was Slow For Tech IPOs, But 2025 Could Be Different
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Starting a company has never cost less. A founder with the right AI tools can ship a working product in a weekend, stand up a website in an afternoon, and fill out an accelerator application before lunch. But that speed hasn’t made it easier to get funded.��

Fewer seed-funded startups are graduating to Series A than just a few years ago, and startup funding has been in a downturn so far in 2026. Investors are concentrating capital in fewer, stronger bets. The question is what “stronger” means now.

Every generation of technology resets what investors should expect from founders. Twenty years ago, a founder who wasn’t internet-native was at a structural disadvantage. Forty years ago, it was computer literacy. Today, AI-native fluency is the baseline — the ability to build, test, and iterate using AI copilots, APIs, and low-code tools at a speed that would have required a full engineering team just a few years ago.��

Founders who haven’t embraced these tools in their daily operations aren’t even at the table. They’re new-aged dinosaurs. Technical expertise still matters, but when everyone can build, thanks to AI, it no longer differentiates. And that forces a harder question for investors: If the product isn’t the moat, what is?

Finding the fit

The answer is founder-market fit. Investors are shifting their attention from what a team can build to whether the founder has domain expertise that predates the startup, has done real customer discovery, and can articulate a path to market that competitors can’t easily replicate.

AI can help a founder build anything, but it’s what customers have a need for that tells them what’s worth building. That judgment is steeped in industry knowledge, customer relationships, and a clear-eyed view of what people will actually pay for. That is the scarce resource these days.

That’s not to say AI can’t help build a company the right way. It has implications for how early teams should be composed., the average seed-stage company last year had just over six employees, down from more than 10 in 2021.��

With teams that lean, every hire has to pull disproportionate weight. The highest-leverage early additions are a product-minded builder who can ship fast with AI tools, someone who owns the customer relationship and drives early revenue, and someone who can position the product and generate demand. A bench of engineers no longer tops the list.

The investor’s harder job

Knowing what to look for is one thing. Finding it is another, because AI has made it easier to fake the signals investors rely on.��

There’s an entrepreneurial equivalent to the that so many people are talking about. Instead of a tidal wave of empty marketing copy about “ever-evolving landscapes,” there’s startup slop that creates a serious evaluation problem for investors. Dealflow volume has become a vanity metric. There’s a surge of submissions that are pure noise, especially from software startups that can fabricate credibility in a single afternoon.

Deep tech is harder to fake. Building a therapeutics company still requires real science, real key opinion leaders, and real partnerships. The same is true for hardware and advanced manufacturing. There’s an actual moat in those sectors, which may help explain why investor interest in deep tech has been growing steadily.��

Investors can weed out the startup slop by asking more specific questions. For instance, our accelerator, AlphaLab, is based in Pittsburgh, and we always ask founders why this city is the right place for them to grow their businesses. You can sense how genuine someone is based on their answer. Same goes for asking about the customer discovery process. Even more telling is why someone started their company in the first place, whether the answer reflects real conviction or a market opportunity they read about.��

AI can’t manufacture what investors are really looking for. The signals that matter most at the early stage are coachability, hustle, and genuine conviction. There are details in an application that suggest someone has actually lived the problem they’re solving. Investors don’t want to write a check to someone who has vibe-coded a company they aren’t passionate about, and the tells are easier to spot than founders think.��

However, AI has reallocated where founders should spend their energy. Because it can help with some of the technical aspects of creating a company, founders should devote more effort to refining their strategy through higher-order skills like judgment, creativity, storytelling, and relationship-building. Speed of communication has become a revealing signal. There is no longer any excuse for taking four days to respond to an email, skipping a weekly investor update, or failing to follow up after a meeting. AI has eliminated the friction in all of those tasks. A founder who is still slow is telling investors something about how they’ll run a company, and investors are paying attention to those soft interactions more than ever.

While the cost of building companies has dropped, the burden of earning investment has risen. And for investors, the evaluation itself has gotten harder, with more noise, more polish, and fewer of the old signals to rely on. The founders worth funding will stand out the same way they always have: by knowing something the rest of the market doesn’t.

��brings 20 years of experience in venture capital, accelerator leadership and strategic operations to his role as director of accelerator programs at����in Pittsburgh. He oversees��, AlphaLab Gear, AlphaLab Health and Robotics Factory Accelerate, programs that support early-stage startups with mentorship, resources and capital. His leadership has helped create a connected AlphaLab ecosystem that empowers founders across industries and stages of growth. Earlier in his career, Tainter held roles at����and����where he led cross-functional initiatives and evaluated early-stage investments. He also teaches at��, where his work focuses on funding entrepreneurial ventures.

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When I was a child growing up in Japan, Dragon Ball was “contraband.” My parents were unhappy about me reading manga for hours every day. Teachers confiscated manga magazines at school. But I was fascinated by a universe created from the pure imagination of a single person that went on to shape the aesthetic consciousness of more humans than almost any artist of the twentieth century.

Japan didn’t build Dragon Ball. Akira Toriyama did.

From One Piece, Slam Dunk and Hello Kitty characters to , and , each traces back to a singular, obsessive individual who looked, by Japanese social standards, like a weird outcast.

The country globally perceived as the ultimate collectivist society made its greatest contributions to the world through lone visionaries building what no committee would have approved.

What makes this pattern remarkable is what accumulates underneath it. Each obsessive builder, over decades, pulled behind them layers of precision craft, knowledge and discipline that no bureaucracy could have planned.

Japan’s extraordinary concentration of underleveraged assets, from precision manufacturing expertise, materials science technology, longevity and gastronomical research to a generational cultural content library, is the sediment left by people society once called misfits.

The vault is opening

The global anime market was only 30 years ago and to hit around $88.5 billion by 2033, growing annually at more than 9%. Overseas anime revenue and accounting for 56% of total sales — confirming that international markets now outweigh Japan’s domestic earnings.

Sources: AJA Industry Reports, Grand View Research, Fortune Business Insights.

has disclosed that more than 50% of its 300 million global members watch anime. Viewership on the platform has tripled over five years, with anime content watched more than 1 billion times in 2024 alone. Naruto, a manga serialization that began in 1999, logged 330 million hours watched on Netflix in the second half of 2024 alone. Out of the top 10 global franchises, five are Japan-originated.

That is critical social infrastructure.

Note: Some other rankings instead have Mario, Harry Potter and/or Shōnen Jump, but generally Japan-originated IP accounts for half.

The convergence nobody is pricing

At the same time, there is a louder conversation happening in Japan.

The nation is rearming. Its defense budget has nearly doubled in three years, exceeding for the first time the symbolic 2% of GDP threshold. Under a five-year Defense Buildup Program through 2027, Japan has committed ¥43 trillion (~$275 billion) to defense-related spending.

Globally, VC investment in defense-related startups totaled $7.7 billion in 2025, Crunchbase data shows, a record high.

Most observers treat this as a separate story. To me, it is not.

Japan’s manufacturing edge in silicon wafers, photoresists, specialty ceramics, industrial robots, optics and sensors is the same precision culture that made watches accurate to the second and frames hand-painted with obsessive fidelity. The outcast engineers who spent careers perfecting micron-level tolerances for consumer electronics built capabilities that now happen to matter enormously in a world consuming autonomous, high-precision munitions at industrial scale.

The creative and the industrial share the same genealogy: a Japanese individual, largely ignored, building something to an extreme that no one asked for.

This convergence of Japan’s technological prowess and cultural impact is what makes the country’s opportunity genuinely unusual. IP that a teenager in Jakarta, Riyadh, Paris or Lagos carries emotionally,�� and precision hardware that only a handful of countries on earth can actually produce, originate from the same national psychology.

One crosses geopolitical lines. The other determines them. Japan’s soft infrastructure and hard capability are rooted in the same stubborn, misfit tradition.

Manufacturing advantage is learnable. The history of industrial development is a history of production methods moving across geographies, in the past over decades, increasingly over months. Competitors can close the gap.

What is harder to replicate is the cultural depth. A franchise relationship formed in childhood does not transfer by policy or investment. , built by a man who spent years mapping insects on foot and wanted to share that obsession with other children, now lives inside the emotional architecture of an entire global generation.

The window is real, and it will not stay open for long

Wars are hard and exhausting. People do not stop wanting to be moved, amused and alive. If anything, that appetite sharpens during geopolitical turmoil. The world increasingly demands the safety that precision manufacturing enables and the meaning that great storytelling provides.

Japan offers both, not by strategic design, but because its most consequential builders were, for a long time, left alone to be strange.

The assets exist. The global demand is accelerating. What Japan is missing is the cross-border fluency — legal, cultural and financial — needed to connect them at the speed the moment requires in the age of AI.

The world is finally ready to pay for what remarkable, overlooked individuals in Japan have quietly been building for decades. The question is whether Japan will be ready to let them and if so, how it can capitalize on its valuable assets quickly enough.

is a seasoned investor, serial entrepreneur and attorney with 25 years of experience bridging law and global business. He currently serves as the inaugural managing director of Japan, where he leads one of the world’s most active startup accelerator programs. He also serves as a senior adviser at , a U.S. and Canada-based hardtech venture capital firm, and as a venture partner at , an innovation advisory firm. An active angel investor, he has backed more than 50 startups, including several unicorns, and founded , an international angel network connecting investors across Japan, the United States, Europe, Asia and the Middle East. His track record also includes co-founding three venture-backed startups. Previously, Shirato spent a decade at global law firms across New York, Toronto, Abu Dhabi/Dubai, Singapore and Tokyo, and before that, held strategic roles as a management consultant at and as a trade negotiator at . He holds a law degree from the , an MBA from the and , and a bachelor’s degree in international law and economics from the .

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In October 2024, my co-founders and I set out to make our Ph.D. research useful in the real world. We had built AI models that could interpret medical images such as X-rays and CT scans across tens of thousands of potential diagnoses, generating comprehensive radiology reports that mirror how radiologists reason in clinical practice. At a time when AI in radiology was limited to flagging a handful of specific conditions, this marked a fundamental shift.

Less than a year later, we faced a critical fork in the road: raise venture capital and continue independently, or accept an acquisition offer from , the world’s largest radiology practice.

The conventional wisdom in tech is that real ambition means staying independent. But in asking ourselves what it would truly take to transform healthcare, the answer was different.

Clinical AI is highly regulated with long sales cycles and complex stakeholder dynamics, where structural advantages tend to harden market positions and compound over time. We decided that joining forces — carefully structured to protect our velocity — would dramatically improve the odds that we realize our mission of significantly increasing the world’s access to healthcare.

Research success is not clinical readiness

During my Ph.D., I trained radiology AI foundation models on what, at the time, felt like massive research-scale datasets; tens to hundreds of thousands of studies. These models make for strong academic demonstrations, prototyping new capabilities across a range of tasks. In real clinical settings, however, they would not yet have met the standards required for production-level safety and consistency in patient care.

Despite the persistent narrative that AI will make radiology obsolete, the reality is that the problem is extraordinarily difficult. A single CT study, for example, can contain 10 high-resolution volumetric series, effectively 3D videos. Add prior studies for the same patient, and you can have a billion pixels of data.

Those billion pixels encode entire medical textbooks worth of information. On top of this, real-world radiology is defined by edge cases where rare but critical pathologies are encountered regularly. We learned a hard truth early on: Models that work in controlled research environments often fall apart when exposed to real-world complexity.

Think about self-driving cars. A decade ago, progress looked impressive. But the real world kept introducing new failure modes. After more than a decade of significant capital investment, only a handful of companies have approached true reliability.

Components required to build reliable models

Key patterns emerged. The companies that made the most progress controlled the entire system and achieved scale early. They owned the vehicles, the sensor stack, the data collection pipeline, the simulation environments, and the deployment infrastructure. That integration, operating at scale, allowed them to continuously collect rare edge cases, retrain models, validate improvements and redeploy safely.

Radiology is no different. Success in the real world requires massive, diverse historical datasets and live data feeds that continuously surface rare edge cases and distributional shifts. It requires vast clinical resources and operational infrastructure to redesign clinical workflows around AI, engineer systems that perform reliably at scale, conduct large-scale research studies, secure regulatory clearance, refine models safely, and continuously monitor performance post-deployment.

Additionally, frontier language models have clearly demonstrated that continuous, high-quality and extensive human feedback is the secret sauce in making models useful. This is no different in radiology. In a world where radiology reports are drafted by AI, every draft must be reviewed, edited and signed off by a human radiologist.

Those edits become high-quality signals that can be leveraged for improving the AI models. Better models elevate radiologists’ accuracy and capacity. Improved radiologist accuracy increases the quality of future training data. Increased capacity allows radiologists to take on additional contracts.

That, in turn, generates more data and high-quality corrections, setting a powerful flywheel in motion. Access to this correction data is rare in AI and can only work meaningfully at a massive scale. These capabilities would be incredibly difficult to achieve as a standalone AI startup.

In healthcare, growth follows evidence

In healthcare, trust is hard earned. It rests on demonstrated clinical efficacy, reliability, security and regulatory rigor. For a health system or radiology group to adopt technology from a new startup, particularly in workflows that directly affect patient care, requires rigorous, real-world evidence.

Evidence in healthcare is not generated in small pilots. It is built through sustained performance across diverse sites, patient populations, modalities and edge cases. If a system proves itself within the world’s largest radiology practice, it establishes credibility across multiple dimensions at once — efficacy, reliability, security and scalability.

In sectors where lives are at stake and the goal is to build something that endures, the way to build it is from within the system you’re trying to improve. Selling early didn’t shorten our journey, it accelerated it. It gave us the foundation required to deliver on our mission of significantly increasing the world’s access to healthcare.

is the CEO and co-founder of , the AI business unit of at . During his undergraduate studies in physics and electrical engineering, he became driven by a singular mission: increasing the world’s access to healthcare through technology. Convinced that AI was the most promising technology to make this happen, but not yet good enough for real-world clinical use, he pursued a Ph.D. in AI at where he focused on foundation models for radiology. His doctoral work produced Merlin, a 3D vision-language model for CT interpretation published in “Nature” in 2026 and recognized as one of the most important papers in the field.

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Founders spend a lot of time thinking about capital. They model burn carefully. They negotiate valuation. They weigh hiring plans against runway.

But many startups overlook a source of capital that doesn’t require dilution at all: tax credits. And to be clear, this isn’t typically because a business doesn’t qualify. It’s because no one builds a process to identify and capture these credits consistently.

Most startups are aware of at least one major opportunity, and that’s the Research & Development tax credit. But fewer founders take a broader look at business decisions throughout the year and how many of those may lead to tax credit opportunities. Hiring decisions, benefit structures, accessibility upgrades, facility investments and certain energy projects all can carry incentives.

So, the issue isn’t eligibility. It’s ownership, timing and consistency.

In early-stage companies, finance teams are lean. Credits often get discussed once a year during tax preparation. However, by that point, it can be too late. The required elections may have been missed, documentation may not support a claim, or deadlines may have passed.

When that happens, the opportunity is gone. We see this pattern frequently in examples such as:

• A company hires several employees who may have qualified for a hiring credit, but no screening process was in place at onboarding.
• A retirement plan is launched without evaluating available startup or employer contribution credits.
• Paid leave policies are expanded without reviewing whether a federal credit applies.
• A facility upgrade is completed without considering whether accessibility- or energy-related incentives were available before the project was placed in service.

None of the above decisions are inherently wrong, but they are incomplete.

Coordinating credits

Tax credits don’t appear automatically because money was spent. Taking advantage requires planning, including specific documentation, elections and coordination between departments. Without that coordination, even well-managed startups leave savings unclaimed.

More-mature companies approach this differently.

Instead of waiting until year-end to ask, “Did we qualify for anything?” established organizations build periodic reviews into their operating cadence.

• Hiring processes include the necessary steps to preserve potential credits.
• Engineering teams track qualifying activities as projects progress.
• Finance evaluates larger operational investments before contracts are finalized.

This doesn’t mean turning every department into tax specialists. It requires clarity around who’s responsible for asking the question early enough, and it ideally includes expert guidance and support to get it right.

It’s helpful to think about this as an evolution.

At a reactive stage — which is most startups — credits are evaluated only when the tax return is being prepared. At a more structured stage, the company reviews credit opportunities quarterly and aligns documentation throughout the year. And in a strategic stage, leadership fully understands how certain business decisions may create incentives and ensures the right processes are in place before those decisions are implemented.

Multiple credits add up

The accumulated financial impact can be meaningful. While a single credit isn’t likely to transform a business, multiple credits across hiring, development and benefits can offset real costs. For companies focused on extending runway without raising additional capital, those offsets matter.

There’s also a governance component.

Investors and buyers increasingly review operational controls during diligence. A startup that has evaluated available credits and maintained documentation signals discipline. A company that hasn’t considered them at all may invite additional questions (especially if elections were missed or filings need to be amended).

None of this is to suggest credits should drive a founder’s core strategy. Product development, revenue growth and customer demand remain the priority. But when companies are already investing in innovation, hiring and infrastructure, it makes sense to evaluate whether part of that investment can be recovered.

The first step is simple: Get the full picture before making any decisions. In many cases, that includes working with an adviser who understands how credits apply to growing businesses.

Then, assign ownership. Determine who is responsible for reviewing credit opportunities throughout the year. Coordinate among departments like finance, HR and operations before major decisions are finalized. Make documentation part of the process rather than a reconstruction exercise at the end of the year.

Being proactive

Again, tax credits are not automatic. They’re for those who plan the entire year.

Startups looking to be more proactive should keep credits like the R&D in mind for its potentially meaningful offsets when investing in product or technical improvements. But don’t stop there.

If considering structured paid leave, review the Paid Family and Medical Leave Credit, which can apply when policies meet specific requirements. Businesses reviewing facility improvements may qualify for the Disabled Access Credit. While credits such as these don’t apply to every company, they’re common enough to demand attention before decisions are finalized — even seemingly unrelated ones.

Startups focused on capital efficiency will see this planning make a measurable difference over time.

is a tax supervisor specializing in research and development tax credits at . He holds a master of science in accounting from and has experience across both public and private sectors. Garba has spent several years advising companies on R&D tax credits, helping startups and growth-stage businesses navigate complex tax regulations and maximize available incentives.

Related reading:

• Sales And Use Tax: What Every High-Growth Startup Should Know About Compliance
• How Your Startup Can Take Advantage Of The R&D Tax Credit

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If you’re the CEO of or and reached $100 million in ARR in under a year, this article isn’t for you — enjoy being a unicorn as thousands of investors beg to fund you.

But if you’re not, then let’s be honest: raising in 2026 is tough. Although global venture funding is growing, raising capital isn’t any easier for the average startup. According to Crunchbase , more than a third of global funding in 2025 went to just 629 companies, compared to 24% of funding in 2024.

This highlights a growing concentration of capital, making most of that funding effectively inaccessible to early-stage startups. So what can founders do to fix that?

We don’t invite strangers to our houses, and we don’t hire them for important jobs either. For thousands of years, trust and credibility were the most important factors in forming relationships, both business and personal.

In 2025, Silicon Valley companies attracted nearly 50% of the entire U.S. . Silicon Valley is also home to , over half of all U.S.-based unicorns.

It’s not because San Francisco Bay Area founders are inherently smarter — it’s largely about being close to capital and networks. When you’re in constant proximity of MAG7 companies and hundreds of VCs, connections happen organically, through social gatherings, meetups and referrals. This is how credibility is formed: through connections and exposure.

So, is networking the secret to raising capital? Partially, but it doesn’t scale. You can’t just meet the whole industry and invite them all to a 1-1.

So instead, you have to build your reputation. Here are my top four pieces of advice on how to build it right.

Be visible

Make your growth visible. Whenever you reach a significant milestone — raising a round, hitting a user target, or achieving revenue growth — the market should hear about it.

We’ve seen countless companies reach a huge target and then fail to spread the word about it.

Make sure to plan all media coverage in advance, keep exclusive news up your sleeve, and have an extensive media strategy. Once the word is out through your company’s social media, pitching to journalists becomes significantly harder. Everybody wants exclusives, and no one wants to write about old news. Global media outlets are all about relationships. Make sure to form a meaningful connection with journalists covering your particular niche.

Focus on customers

The second priority when raising funds is your company’s place in the overall market landscape. Be where your customers are. Many founders make the same mistake: chasing investors instead of customers.

Remember that investors will always find good investment opportunities. Your job is to make sure that your company is one of them. Investors have to see that your company has a sustainable customer acquisition approach and is able to continuously grow its user base.

Chasing investors can even damage your public picture. If VCs see you spending heavily to attract investors rather than customers, it may signal misaligned priorities.

Be a thought leader

Important thing No. 3: thought leadership. You have to prove your credibility through actively participating in conferences and meetups.

Speaking at industry events signals credibility at scale. Conferences are highly selective. Being on stage implies that organizers have already vetted your expertise. Getting on the stage and delivering your core message will help your credibility more than any degree or a title.

Raise symbolic capital

The fourth significant factor is symbolic capital — the way your company is perceived by the market. A great way to acquire symbolic capital is through various ratings and features. They’re usually put together by the larger media outlets and include programs such as Forbes’ 30 Under 30, TechCrunch Startup Battlefield and Slush100.

Similar to conferences, participating in different features shows potential investors that a credible player with a good reputation has already done a background check on you and is ready to endorse you. One well-known logo in your endorsements list can go a long way in securing the next round of funding for your startup.

A somewhat unexpected benefit of getting into the biggest ratings and roundups is your AI visibility. Your company being featured in one of these lists will significantly improve the odds that AI will highlight your company in relevant conversations. AI visibility is increasingly important for user acquisition, considering that according to , already use AI instead of traditional search engines for shopping.

Reputation: You can’t buy it

Reputation is one of the rare things in the business world that you can’t just buy.

One of our longstanding partners received an invitation to a dinner with the Royal Family of the United Kingdom, which is something that no amount of marketing budget will give you. It takes a lot of coordinated work and effort that won’t result in exact KPIs on day one, which is why many startups just don’t have the patience and strategy it takes to build credibility.

As development and compute costs fall, the number of startups continues to grow. In that environment, reputation becomes the key differentiator between companies that attract capital — and those that don’t.

is a communications strategist and serial entrepreneur with more than 10 years of experience. She co-founded , an AI adviser for smart manufacturing, and leads BeGlobe, a PR agency for tech startups and VCs. She is a graduate of ’s SkyDeck Accelerator.

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For much of the past year, the conversation around agentic AI was dominated by ambition. Founders and investors alike talked about autonomous systems that could reason, act and operate with minimal human involvement. As we move into 2026, that narrative is shifting away from what agents might do someday toward what they can reliably deliver today.

This shift is evident in findings from ’s report, which quotes from conversations with eight AI-focused VC investors who discuss what they see in the market today and in the year ahead. Their perspectives reflect a broader recalibration underway across the venture ecosystem. The experimentation era is giving way to one of more intentional adoption. AI is increasingly treated not as a standalone feature, but as an operating layer — embedded in workflows, governed by policy and evaluated on outcomes rather than ambition.

In practice, that means agents are finding traction in narrow, well-defined use cases. Fully autonomous agents remain rare in production, particularly for complex or high-risk workflows. What is working are agents deployed in data-rich domains like software development, customer support, sales operations and internal analytics. In these environments, human-in-the-loop designs are not a compromise; they are often the reason agents can be trusted and adopted at scale.

What investors look for

This shift has changed how startups are evaluated. As agentic tooling becomes easier to build, impressive demos have lost much of their signaling power. What matters now is evidence of usage: customers running agents in production, measurable productivity gains and early revenue momentum.

Founders need to clearly articulate how their agents improve existing workflows and why that value persists over time. Without that clarity, even technically strong products struggle to stand out.

Capital dynamics are also shaping the market. Investment continues to concentrate around a small group of foundational models and infrastructure providers. Rather than crowding out startups, many investors see this as an enabling layer. Well-capitalized platforms absorb the cost of training and inference, allowing startups to focus on application-level value.

Looking ahead, 2026 is shaping up to be less about sweeping claims of autonomy and more about execution. Enterprises want agentic solutions that fit into existing operating models, meet governance requirements and deliver quantifiable business impact.

For VCs, the hype cycle has done its job. The next phase will reward startups that turn agentic AI into focused, outcome-driven businesses and can prove it.

is the head of where he focuses on investments to drive innovation and unlock new value on top of the Snowflake platform. A seasoned product management leader, he originally joined Snowflake in 2017 as a senior product manager, a role in which he played a pivotal role in the company’s partner ecosystem expansion. Prior to Snowflake, he spent 18 years at with various roles across master data management and data platforms. Kapre earned his bachelor’s degree in electrical engineering and computer science from the .

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