It used to be that if you invested in SaaS, you slept well at night. Returns were predictable because the business model was subscription-based and incredibly scalable: build a horizontal cloud-based platform to target as wide a market as possible, charge per seat and grow by expanding the user base.

1, and their peers returned billions to investors on that model. But now, due to AI, where AI agents are replacing humans as the user (through what the industry calls ā€œheadlessā€ models) and upending the per-seat model, the SaaS market has lost its predictability. January’s $300 billion single-session wipeout is a leading indicator that the old SaaS model has passed its peak.

Investors are retrenching and trying to predict whatā€™s next as the three frontier AI companies vault into the public markets at multitrillion-dollar valuations. We would argue that these infrastructure platforms enable the next wave of software innovation: AI-native software that automates and enables the $2 trillion white-collar services market.

Generic, horizontal SaaS, as we know it, is a declining legacy model (like on-premise software before it), but investors still have reason to be optimistic about the software market. Thatā€™s because AI-native software is going after a much larger opportunity than SaaS ever claimed and the productivity gains and value creation opportunities are unprecedented. The target markets are vertical industry focused and highly specialized, priced differently and built on proprietary data moats that didn’t exist five years ago.

Death of per-seat pricing

SaaS has always been priced on a per-seat basis. That model evaporates the moment AI agents generate most of the usage. A company that once needed 100 CRM licenses for its sales operations team may soon need just 50.

Technology companies facing that reality have to choose a new path forward beyond connecting peopleā€™s workflow: perform and charge for the actual work done (usage) or based on outcomes (ROI). A legal AI platform charges per contract drafted, doing the work of a lawyer. Here the software charges for some fraction of the labor it replaces. A spend management AI-native software application can take a percentage of overages found or a chargeback software application could take a fee on the value of the chargebacks it successfully recovers.

The next era of AI-native software runs on automation and performing knowledge-worker actions, not connecting workers or workflows. These solutions reach beyond IT budgets to much larger labor budgets. The companies that adapt will build faster, deliver more value and command a premium for it.

Horizontal is a liability

Generic horizontal SaaS is the most vulnerable to this changing market. If an entire product is a wrapper around a workflow that an AI agent can now handle autonomously, the value proposition may be greatly reduced. Form builders, project management platforms, SMB-focused CRMs, off-the-shelf social schedulers: these categories are compressing fast and may not recover.

The defensible positions now belong to vertical niche specialists, companies that have built what we call the three ā€œDs.ā€ Distribution through a recurring and longstanding customer base.

Domain expertise specialized to operate in regulated or complex industries. Proprietary data that drives decision-making and is closely held by customers and inaccessible to frontier models.

When your product is built around the specific workflows, terminology and compliance requirements of one industry, ending a vendor relationship is less about migrating data and more about rebuilding a complex web of experiences, corner cases and historical knowledge. Customers stay not because they’re trapped, but because the cost of retraining, reconfiguring and finding a vendor who understands their world is too high.

The more deeply a company understands the regulatory environment, the operational constraints, and the institutional logic of a specific industry and a specific customer, the harder it becomes to displace.

Legal contract repositories, insurance underwriting criteria, bank loan performance data; once embedded in a model and a workflow, these assets create high switching costs that dwarf anything a generic SaaS contract ever produced. You can export a Salesforce contact list. You cannot export your underwriting logic.

People are part of the product

The model that will define the next decade of B2B software deliberately combines software and services, what practitioners call Human-in-the-Loop, or HITL: pairing agentic intelligence with human judgment at the points in a workflow where it matters most.

Legal, healthcare, cybersecurity, construction, financial services, defense; these verticals are defined by high stakes, regulatory complexity and contextual judgment. Routine and repetitive tasks may be mostly automated, but some portion of decisions will always require human judgement because the cost of errors or omissions is prohibitive.

This solutions-centric customer relationship changes what a software company fundamentally is. When a vendor is embedded in how a client operates, handling onboarding, workflow design, optimization and quality control, it accumulates something pure SaaS rarely achieved: proprietary data, domain expertise and institutional trust. Every client engagement makes the product smarter and each deployment deepens the moat.

This is why the most durable software businesses of the next decade will be built inside verticals, not across them. The companies that understand this will stop treating services as a cost of implementation and start treating them as a compounding asset.

A bigger market than SaaS ever was

Even capturing a small fraction of what projects is a $6 trillion annual productivity opportunity from AI transformation dwarfs the traditional enterprise software market. AI-native vertical platforms no longer just compete for the technology budget, they also compete for the labor budget, the compliance budget and the risk budget. That’s a much bigger pie and a more strategic partnership conversation than any per-seat SaaS vendor ever got to have.

The winners won’t be companies that bolt AI onto existing SaaS products, or that add a services layer as an afterthought. They will be the firms with true subject matter expertise that happen to run on AI-native software. They will collapse the boundary between software and services entirely, building businesses whose value compounds with every customer relationship and every data asset they accumulate.

The AI-native software company is a fundamentally different kind of company than the SaaS era ever produced. And it’s worth considerably more.

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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I sold my AI research company while I was qualifying as a lawyer in the U.K. I built with researchers from , , and who believed in the mission enough to trust a 21-year-old law student to lead the ship.

, when acquired us, it was the first time in its 170-year history it bought a company pre-revenue. Thomson Reuters acquired us for the science.

Getting there was painful, though. Our published papers put the model among the best in the world at legal reasoning, and we trained it for a fraction of what the large labs were spending. We had been a quieter version of ā€œthe story,ā€ developing very capable models using novel algorithms with huge capital efficiency.

None of that counted for much in the rooms I walked into. Investors always asked about the product and the traction. U.K. investors passed, and I ended up raising most of our funding in the United States.

I back founders now, and the things I weigh have stayed consistent. As a founder, I was told again and again that science meant little until it was bolted onto a product. That test was wrong then and I believe it is fatal now.

Backing founders in the foundational layer

In the first quarter of 2026, foundational AI startups raised around $178 billion. The market is realizing that foundational AI is where the long-term value sits, and this is the year we may see the exits and IPOs that prove the bet right.

However, the capital and the conviction have also pooled around a few names that were already incumbent. , and took roughly 97% of it, and every other foundational AI company in the world shared what was left.

For a deep-tech founder starting out now, that might push them toward a tempting but dangerous read of the market: that the race is over, and that the sensible move would be to build on top of one of these giants.

Iā€™m looking for founders who move the other way.

Most application-layer companies, built on a model they do not own, adapt to the pricing and access decided for them by the firms upstream, and compete in categories that the same firm can absorb whenever it chooses.

The more durable place to build is the layer underneath. The cost, speed, reliability, interpretability and safety of AI systems remain unsolved and genuine scientific challenges, and they decide what everything that sits above them can do.

A real advance in training efficiency, model architecture and inference cost is the work that will still matter in five years, long after most of todayā€™s wrappers have been priced out or absorbed.

Asking the right questions

So the questions I ask AI founders are:

• Is your technical team, scientist-for-scientist, equal to or better than the team at DeepMind?
• Does the problem sit at the level of the model and the system, or is it one more thing stacked on someone elseā€™s?
• Will the ā€œproductā€ get harder to live without over the next five years, or is it looking to reach for early revenue like every other startup?

Some of the companies that ended up mattering most in the AI era are those that survived this line of thought. DeepMind and OpenAI began as research efforts with no obvious product, and both would have looked uncomfortable to a conventional early-stage software investor. Their importance is obvious in hindsight, but the foundational problem-solvers tend to look unfundable right up until it looks inevitable.

Do not build to look fundable this quarter. Build something that the whole stack will depend on in the future. Hire the best team you can find to do it, and solve the hard, foundational problem while it is still unfashionable.

The deep-tech capital market is slow, and it will keep chasing familiar names for a while yet. The work still comes first, and the founders who dare to do it early are the ones the market eventually has to come and find.

The future lies in deep tech, not in the surface wrappers that pass for most products nowadays.

was the founder and CEO of , which was acquired by in 2024. He is an angel investor and senior director at Thomson Reuters Labs.

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For decades, the SaaS playbook was clear: predictable revenue streams, very high gross margins, efficient customer acquisition and strong net revenue retention made a startup very attractive to investors. These metrics built unicorns and defined how investors valued SaaS investments.

But today, with the launch of LLMs, and in the shadow of the ā€œSaaSpocalypse,ā€ 30 years of relative SaaS stability has been shattered and the playbook is being rewritten with disappearing ink.

If youā€™re a SaaS founder ā€” especially one raising capital ā€”Ģżthis may lead to uncertainty and confusion. You may lose sleep because the whole market trajectory is uncertain. Investors themselves are trying to anticipate how the SaaS business model will change and, ultimately, what your company should be. To add further confusion, the model many VCs are championing (SaaS and services, anyone?) doesnā€™t look anything like traditional SaaS. So, what should a founder do?

Ignore the SaaS du jour

In a recent , partner argued that the next trillion-dollar company will be a software business disguised as a services firm, one that sells both tools and outcomes.

His logic is straightforward: For every dollar spent on software, six are spent on services. Meanwhile, LLMs are commoditizing many AI-native SaaS products before they even have a chance to scale. In this world, Bek argues, judgment ā€” not software ā€” is the scarce asset and customers will eventually pay for outcomes, not seats.

For founders, advice like this can be seductive to take. If software margins are compressing and AI is eroding moats, why not follow the trend, add services and open new revenue streams?

Founders need to be careful about taking this fashionable advice because it is greatly driven by investor anxiety and not as much by market reality. What VCs are really responding to are two separate concerns: how to reduce the risk that a portfolio company is disrupted by foundation models, and how to adapt to a new SaaS economy where software alone may no longer command the margins, defensibility or growth premiums it once did.

Founders should instead be prepared to answer this practical question: which parts of their business still matter, which parts have changed, and how do they need to adjust in that context. If the offering is not core to the operations of the enterprise, a pivot will likely be necessary.

The market reset is real, and yes it affects your pitch

The growth-at-all-costs mindset is gone. In its place, investors are laser-focused on capital and sales efficiency, gross and net retention, as well as Rule of 40, gross retention, CAC payback and burn multiple.

What this means for your pitch: A strong SaaS founder today must be able to demonstrate a sharp wedge, a clear buyer, strong usage, measurable ROI and a product roadmap that expands from point solution into platform.

The bar has moved from ā€œCan this company grow?ā€ to ā€œCan this company grow efficiently and organically, retain customers through budget scrutiny, and compound value as it scales?ā€

AI startups can grow at unprecedented rates, but early hypergrowth can be misleading when switching costs are low and retention is unproven. Investors are excited by AI growth, but increasingly skeptical of AI novelty.

A demo is not enough. You need to prove AI creates durable workflow ownership, not temporary experimentation. Remember, if it takes less than a year to create a company using the current tools, without a sufficient moat, it will take even less time to create an even better company to compete with this one in 12 months.

Focus must be on creating a system of intelligence or a vertical operating system for an enterprise. Understanding workflows is critical. Features and functionalities are no longer sufficient.

Your pricing model is going to change

Seat-based pricing is no longer always the right answer. If your AI performs work independently, customers don’t need more seats to get more value. This is pushing the market toward usage-, consumption- and outcome-based models. notes that long-term pricing is shifting toward value-based and outcome pricing, and that continued cost-of-intelligence improvements could eventually help margins expand.

In the old SaaS model, value was tied to access: seats, users, departments. In the AI era, value is tied to outcomes. Software isn’t just helping employees do tasks anymore. It’s beginning to execute them directly: writing code, reviewing contracts, resolving support tickets, analyzing financial data, automating back-office workflows.

Have a big moat

Promising AI categories are attracting 2x to 3x more competitors than in prior years, while large SaaS incumbents are aggressively launching AI products, acquiring startups and hiring AI talent. Investors will ask you directly: what’s your moat? Is this a real defensible position, or a feature that 1, or can ship in a quarter?

AI expands the addressable market for software significantly. Traditional SaaS captured software budgets. AI-enabled SaaS can capture services spend, labor spend and outsourced process spend. Battery frames this as a major expansion from cloud software into services automation and human labor displacement ā€” a much larger opportunity than prior SaaS waves.

Rules for the road

The market is open for exceptional SaaS companies. But the bar is higher, and investors have seen enough AI pitches to be skeptical of the theme. What they want to hear from you: A specific customer pain point with evidence of urgent demand; proof of retention, not just initial adoption; efficiency metrics that hold up under scrutiny; and a clear, concrete explanation of how AI improves your product, your business model and your customer’s ROI.

The founders who get funded in this environment will be domain experts who understand their customer’s workflow deeply, where AI can safely replace, augment or accelerate human work, and disciplined operators who understand the economic tradeoffs: when to use frontier models, when to use smaller specialized models, when to fine-tune, and when to preserve human review.

is managing partner at . He has more than 41 years of C-level global experience in the tech sector as a seasoned investor, entrepreneur, board member and educator focused on helping teams prepare for rapid growth, scaling and liquidation events.

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For more than a decade, customers spent their software budget procuring vertical SaaS products. ACVs, or annual contract values, were modest, customer acquisition cost had to stay below a ceiling, and the resulting go-to-market playbook was product-led growth, SDR-led and content-driven.

With AI, many products are no longer SaaS but usage and outcomes based. They are replacing labor, not software. At my investment firm, , we call this new category of companies vertical AI. Vertical AI spend doesn’t just come from a customer’s software budget. It often comes out of headcount as well, a much larger line item. As a result, ACVs have jumped meaningfully to 6- and 7-figure deals.

I’ve written before about how AI for vertical SaaS, and how the value framing shifted from subscription pricing to. As ACVs have grown in vertical AI, the go-to-market motion is changing too. We’ve explored tactics to drive a more efficient sales process.

Here, Iā€™ll explore how the channels are changing as well.

Why direct sales is back

Direct sales has historically only worked at true enterprise scale. The cost of an AE’s time wasn’t warranted for smaller ACVs. Below a certain deal size, the math didn’t work for high-touch sales. That’s why SaaS GTM became PLG and SDR-led.

With vertical AI ACVs frequently landing in the 6- or 7-figure range, founders now have room to invest meaningfully in winning each logo. Weā€™re also seeing these smaller businesses spending relatively more with quicker sales cycles which is enabling higher volume.

AEs, in-person sales motion, and other tactics that didn’t pencil at scale under old SaaS economics now do. Direct sales now works further down market where prior SaaS economics didn’t allow it.

Two channels in particular have driven a lot of distribution and success for vertical AI companies recently. They are distinct from each other but we’ve seen companies have success with both.

No. 1: Private equity and heads of AI

Many PE firms are actively pushing their portfolio companies to drive efficiency with AI. Some have even created a new role internally to spearhead these initiatives. These AI partners are often tasked with collecting and disseminating learnings, finding good AI tools, and connecting them into the portfolio if there’s a fit.

The motivation is sometimes EBITDA driven, but can also be softer than that. Many of these execs are focused on adding value across the portfolio, helping companies build AI competency, and coming up with an execution plan.

The decision making structure also varies. Sometimes the and push adoption down to the portfolio. More often, the firm will forward information to relevant company executives and leave the decision making to them. If executed well, this can be a very efficient channel for vertical AI companies. One introduction to the PE firm surfaces many qualified leads across their portfolio companies.

Usually, companies will land one customer initially. Positive feedback then travels in two directions. Laterally to peer companies within the portfolio, and back up to the PE investor, who introduces the vendor to others in the portfolio. We’ve seen this be particularly successful in industries where rollup strategies are popular like healthcare services, dental, MSP, accounting, legal, financial advisory, insurance brokerage, home services and industrial.

No. 2: Conferences

We’ve also seen sector and function specific conferences be incredibly valuable in driving distribution for vertical AI companies. The advantage is concentrated attention and self selection by the right buyer. Buyers are captive and open to learning.

They come to these events curious to hear what’s new in their sector. Attendance allows companies to meet the right buyer, showcase the product live, and collect leads at scale. Sponsoring and attending dinners is another opportunity to meet prospects.

Iā€™d argue that scalability of lead generation and brand awareness matters more now than ever. That requires getting the word out about your own company but also cutting through the noise of others in the market. Buyers are actively building out their AI strategies so vertical AI companies should be sprinting on GTM. Companies need to be top of mind when potential buyers are open to evaluating new tools.

Whether that becomes a sole source decision or an RFP, the prerequisite is being part of the consideration set. In order to do that, your buyer needs to know you exist, and this is a great way to spread the word efficiently.

What this means

The GTM playbook for vertical AI now looks meaningfully different from the SaaS playbook it grew out of. Distribution, pricing and sales motion have all shifted in tandem, with each piece reinforcing the others. Buyer pull justified larger ACVs, larger ACVs justified deeper investment in the sales motion, and the new economics opened up channels that didn’t work under the old model.

The companies pulling away are the ones pairing a great product with the right GTM motion. They have recognized that bigger ACVs demand a different playbook, and they have adapted before their peers.

When the gates of distribution opened, everyone walked through. The companies winning now have figured out what to do once they were inside.

If you’re a founder building vertical AI and rethinking GTM, I’d love to hear from you.

Ģż is a general partner at , where she invests in and partners with early-stage founders from inception through Series A across sectors including AI, fintech, healthcare and enterprise software. Prior to joining Defy, Agarwal spent seven years at and began her investing career at . A former founder and operator, she previously co-founded two startups and started her career at

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Legal tech funding is booming, but the money isnā€™t spreading evenly across the market.

Last year, 91¾«Ę· News reported that legal tech startup investment was riding high as investor enthusiasm for AI reshaped legal software funding, citing a report estimating that 44% of legal work could eventually be automated. That concentration has helped create one of the clearer success stories in legal AI ā€” and may also be obscuring an adjacent market that remains far less developed.

Using disclosed funding totals for a selected group of plaintiff-side legal AI companies, the imbalance is hard to miss.

EvenUp has raised $370 million, $164 million, $85 million, and Darrow $63 million, for a combined total of roughly $682 million. Plaintiff-focused companies account for about 71% of disclosed capital for legal AI, suggesting investors have found a part of the sector where adoption, workflow clarity and venture-scale narratives already line up.

That investor interest is not difficult to understand. Plaintiff firms tend to share more standardized workflows around client intake, case evaluation, medical review and demand generation ā€” all areas where AI can automate repetitive work and improve throughput. As those firms have adopted software, the category has become easier to understand, distribute and fund.

The underserved side: legal defense

The defense side, by contrast, remains underdeveloped and may present the next big opportunity.

Corporate legal departments and the law firms managing high-volume defense work still rely heavily on fragmented systems, spreadsheets, email-based coordination and outside counsel processes that were not designed to produce portfolio-wide visibility. For companies facing hundreds or thousands of active matters, litigation is often still run more as a services function than a software-enabled one.

That creates a sizable but harder-to-package opportunity. Retailers, insurers, healthcare systems and financial services companies can each manage large litigation portfolios, yet many still lack a unified view of case risk, settlement patterns, legal spend and outside counsel performance. The need is not new. What has been less clear is whether a venture-backable software category could be built around it.

Part of the reason defense-side legal AI has lagged is structural. Workflows vary widely by industry, matter type and regulatory context, making the market less standardized than plaintiff-side practices. Buying decisions also tend to run through general counsels, legal operations teams and outside counsel relationships, which can lengthen sales cycles and make the category look less immediately viable to investors.

But a shift is underway. Last fall, 91¾«Ę· News reported that legal tech funding reached record highs in 2025, reinforcing how quickly investor attention has shifted toward AI-enabled legal workflows. As plaintiff-side firms get faster at sourcing, valuing and prosecuting claims with software, the operational pressure on defense teams mounts. At the same time, AI is making it more feasible to turn messy litigation workflows into systems that can surface comparable matters, flag risk earlier and benchmark outcomes across portfolios.

From an investor perspective, that makes defense-side litigation AI look less like a niche and more like an underbuilt segment of a broader legal software market. If plaintiff-side investment reflects where legal AI has already become easy to fund, defense-side infrastructure may represent where the next category still has room to form.

Investors, take notice

For venture capitalists, this is the kind of asymmetry worth watching: a large enterprise market with measurable pain points, improving technical feasibility, and no entrenched category leader yet. What investors should watch is whether startups in the category can pair proprietary outcome data with repeatable enterprise adoption ā€” the combination most likely to produce a durable category leader.

One emerging approach on the defense side is exposure and settlement benchmarking: using historical resolution data to estimate settlement ranges, legal spend and case risk across similar matters. In practice, that can mean comparing claims by jurisdiction, plaintiff firm, claim type or other operating variables to help in-house teams make faster and more consistent decisions.

If the category scales, one potential moat may come from proprietary outcome data. Defense-side settlement details, matter economics and resolution patterns are often difficult to reconstruct from public records alone.

A platform that aggregates and normalizes those signals across customers could build a data asset that becomes more useful with scale ā€” a familiar dynamic in vertical software, and a potential early signal for investors of durable advantage in defense-side legal AI.

There is still no clear, scaled, venture-backed winner built specifically around defense-side litigation intelligence. For startup and growth investors, that makes the segment less a settled market than an open question: whether one of legal AIā€™s next durable companies will emerge not from the workflows that have already attracted the most capital, but from a large enterprise category whose software stack is still taking shape.

Ģż is CEO and co-founder of , which builds AI-powered litigation intelligence for corporate defense teams and law firms.

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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 ā€” but because I figured out, early and somewhat accidentally, that the best investors donā€™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 didnā€™t go to. If thereā€™s a single lesson Iā€™d want every first-time founder to take from my fundraising experience, itā€™s this: stop optimizing your outreach and start engineering the rooms youā€™re in.

Full disclosure: I went to . I know what youā€™re thinking: ā€œOf course she raised $14 million ā€” she had the network handed to her.ā€ And look, I wonā€™t pretend the alumni connections didnā€™t open certain doors. They did. Itā€™s a competitive advantage most founders donā€™t have, and I wonā€™t insult your intelligence by pretending otherwise. Harvardā€™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, ā€™s ā€” 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 doesnā€™t give you is a mission people instinctively care about. Iā€™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 donā€™t need convincing that loneliness is a crisis or that the way teenagers relate to each other has fundamentally changed ā€” 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, youā€™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 ā€” the share that leads to any meaningful next step ā€” sits at , even for founders who do everything right.

But thereā€™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 ā€” through experience I did not fully understand until I looked back on it ā€” 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 ā€” if you have something worth saying. You donā€™t necessarily need to find yourself in prestigious halls. Any room where the right people are present, and youā€™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 ā€” 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 ā€” 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 ā€” 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 ā€” 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 ā€” a major fund ā€” 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 ā€” 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 wonā€™t pretend otherwise. But if you have a mission that is genuinely worth talking about ā€” and if you can speak about it with conviction ā€” 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.

Thatā€™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. Thatā€™s the posture that turns a shared run or a sports dinner into a check from someone who genuinely believes in what youā€™re building.

The goal isnā€™t to get lucky. The goal is to make yourself impossible to miss ā€” every time you have a mic, and every time you donā€™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) ā€” 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, Africaā€™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 ā€œgridtechā€:

• It comprises 16 scaleups (17% of the cleantech total) and two scalers (25% of total).
• It has attracted around 30% of total cleantech funding.
• Africaā€™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, Africaā€™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 Africaā€™s growth ā€” consider that Africa still accounts for only about 5% of global energy supply ā€” 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 worldā€™s best solar resources.

Africaā€™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.

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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 ā€” venture clients, venture builders, intrapreneurship, CVCs ā€” 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 ā€œhardā€ 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 yearā€™s AI anxiety-driven sell-off all the more dramatic. Software juggernauts serving heavy industries ā€” , , , ā€” 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, thereā€™s more at play. The macro trend is a newfound founder enthusiasm to build AI-native entrants in legacy industries, and the backing theyā€™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 ā€œhardā€ 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 ā€” 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

Thereā€™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 theyā€™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 firmā€™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 ā€œreopens,ā€ 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.

Ģż 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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• The IPO Window Wonā€™t Stay Open On Its Own
• Forecast: 2024 Was Slow For Tech IPOs, But 2025 Could Be Different
• 91¾«Ę· Predicts: IPOs Picked Up In 2025 And The Outlook For 2026 Is Even More Optimistic

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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 hasnā€™t made it easier to get funded.Ģż

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 wasnā€™t internet-native was at a structural disadvantage. Forty years ago, it was computer literacy. Today, AI-native fluency is the baseline ā€” 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.Ģż

Founders who havenā€™t embraced these tools in their daily operations arenā€™t even at the table. Theyā€™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 isnā€™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 canā€™t easily replicate.

AI can help a founder build anything, but itā€™s what customers have a need for that tells them whatā€™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.

Thatā€™s not to say AI canā€™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.Ģż

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 investorā€™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.Ģż

Thereā€™s an entrepreneurial equivalent to the that so many people are talking about. Instead of a tidal wave of empty marketing copy about ā€œever-evolving landscapes,ā€ thereā€™s startup slop that creates a serious evaluation problem for investors. Dealflow volume has become a vanity metric. Thereā€™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. Thereā€™s an actual moat in those sectors, which may help explain why investor interest in deep tech has been growing steadily.Ģż

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.Ģż

AI canā€™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 theyā€™re solving. Investors donā€™t want to write a check to someone who has vibe-coded a company they arenā€™t passionate about, and the tells are easier to spot than founders think.Ģż

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 theyā€™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 doesnā€™t.

Ģżbrings 20 years of experience in venture capital, accelerator leadership and strategic operations to his role as director of accelerator programs atĢżĢżin Pittsburgh. He overseesĢż, 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 atĢżĢżandĢżĢżwhere he led cross-functional initiatives and evaluated early-stage investments. He also teaches atĢż, where his work focuses on funding entrepreneurial ventures.

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