Investors Predict New Rules For Startups And VCs

VCs are advising founders to brace for a tougher, smarter market. Another cycle will favor companies that can demonstrate distribution advantages, actual unit economics, and fast paths to customer ROI—rather than a slick demo or large total addressable market. AI is still the accelerant, but the pitch has pivoted from “what the model can do” to “what business you can sell over and over.”

Climate and Value Discipline in Venture Funding

Early-stage capital is still available for the very best teams, but the bar has been pushed higher for closers.

No moat as a service

American and European VCs have been flooded with “software-powered companies” who charge customers a monthly fee to replace manual labor, but they are also investing in the front lines of Automation 2.0, where no user is left behind. Investors tell me “the moat has gone”—that assets used to protect margins—like capital, scale, or patents—are over; repeatable sales engines, proprietary workflows built by their teams, and founders who use the product in depth for years before they create it are what take products, policies, and safety to new civilization-like next levels. Therefore, a SaaS relationship with data alone won’t define buyer dominance. Even on Wall Street there’s less information arbitrage than some young firms remember (go watch Margin Call). According to PitchBook and NVCA numbers, deal values remain well off their 2021 peak, and late-stage valuations are down roughly 30–50% across many categories in the wake of a reset that has brought focus back on efficiency and revenue quality.

Expect fewer mega-rounds at pre-seed and seed in crowded AI categories with thin moats. Series A and B will focus on those companies demonstrating unmistakable momentum—net revenue retention over 120%, rapid payback periods, disciplined burn. Private credit has temporarily provided some runway to growth-stage companies, but funders warn that debt is not a substitute for product–market fit or a long-lasting business model.

IPO Window and the New Liquidity Mix for Tech

Most investors expect a slow reopening of the public market as there is a backlog of mature software, fintech, and AI infrastructure companies waiting to list. Catalytic listings from category leaders might drag others through the window, but who else goes where and when in terms of liquidity will be multi-threaded. And look for greater roles for secondaries and targeted M&A to join IPOs, a toolbox approach many companies leaned on as public exits cooled.

The takeaway for both founders and LPs: plan on optionality. If a business can’t grow due to current market conditions, it should focus on keeping its balance sheet strong early, and operating until profitable or near breakeven (or running a methodical secondary process) will decrease exit-timing risk during stabilization.

Where Capital Will Flow in the Next Venture Cycle

Money is pouring into “sleepy” or hard-to-understand sectors where AI can fundamentally change the game for ROI and moats. Think field service operations, logistics, insurance claims, compliance-heavy healthcare, and industrial maintenance—areas in which proprietary data, workflows, and distribution are difficult to reproduce. In healthcare, platforms and systems of record dominate over point solutions because they own and influence integration and data gravity.

There are also exciting opportunities on the fringes around infrastructure and tooling for model training and deployment, as well as embodied AI, simulation, world models for robotics and autonomy. At the software–hardware edge, there are competitive disciplines like energy management (bringing efficiency and control), precision manufacturing, and computer vision for operations in meatspace—where a very big part of global GDP still lives.

AI Beyond Hype: Practical Architecture and Moats

AI-first is ceding to AI-native. Enterprises value explainability, cost, and reliability, which is forcing startups to:

• use the right-size model for the job
• combine small models with some deterministic logic
• orchestrate across multiple providers

The era of the “one model to rule them all” is over; the moat now lies in proprietary data, workflow integration, governance, and distribution.

That translates into less spending on random LLM calls, and more focus on guardrails, caching, and training fine-tuned small models. It also means we need to start thinking about model choice as infrastructure—it should be interchangeable, measurable, and continuously optimized for latency, accuracy, and margin.

Globalization of Venture and New Tech Market Centers

And the center of gravity keeps getting broader. CB Insights and other trackers report that about half the world’s unicorns (companies valued at $1 billion or more) are now domiciled outside the U.S., and new company formation is accelerating in much of Central and Eastern Europe, the Middle East, and Latin America. Public-market precedents like MercadoLibre and Nubank have bolstered investor confidence; meanwhile, regional exchanges such as Tadawul are becoming more credible options for tech listings.

And cross-border founders are building to global standards from day one, often with cost advantages and direct access to under-digitized markets. The message for VCs is straightforward: ignore these geographies to your own detriment.

What Founders Must Demonstrate Now to Raise Capital

Investors are looking for high-context founders who can translate domain expertise into compounding advantages. The winning pitch demonstrates: a buyer and distribution wedge, proprietary data or relationships, specific measurable ROI in one or two quarters and how the company gets there, and a believable path to achieving gross margin and payback targets without burning too much. Due to modern tooling, most teams can become profitable with lean headcount; the great ones will also do that while continuing to grow efficiently.

“Security, compliance, and governance are no longer ‘later’.” SOC 2 readiness, data residency, model audit trails, and AI safety guardrails are all landing in diligence checklists at seed and Series A, especially for regulated industries.

Risks and Wild Cards for Venture and AI Markets

Two macro determinants are the 800-pound gorillas: energy and rates. A rate spike in power would ripple through the AI training and inference economics, compressing margins and slowing adoption. Rate volatility could also dampen risk appetite, crimping new fund formation, and driving LPs toward pacing discipline. Preqin finds the most private credit AUM on record, but over-leveraging to fill equity gaps brings future refi risk if growth slows.

Yet even with those caveats, the investment vortex agrees: the market is pivoting from storytelling to scorekeeping. Founders that bring distribution, defensible tech, and clear customer value will find capital—and, internationally, increasingly more than one path to liquidity.