AI Everything Today

Overview: PitchBook numbers, VCs, and what changed in 2025

New data from PitchBook shows a stark shift in where venture capital goes. In 2025, AI companies are on track to receive more than half of all VC dollars invested. That marks the first time a single thematic area likely captures a majority of venture funding in a year.

This trend affects founders, investors, employees, and customers. Venture capital firms, large and small, are channeling capital into startups working on foundational models, AI infrastructure such as chips and cloud services, developer tooling, verticalized AI for industries like healthcare and finance, and data services. The result is heavier concentration of capital around AI, and less available cash for startups that do not include AI in their product offering.

Why this matters to ordinary readers

When VCs prioritize AI, everyday consequences follow. Some familiar apps and services may get faster development and more advanced features thanks to new investment. At the same time, fewer early-stage bets outside AI can slow innovation in other areas, including social services, green tech, and consumer products.

Investors say they prefer AI because of the size of the market opportunity, potential for defensible moats through proprietary models and data, and network effects that can scale value quickly. Those preferences shape which startups can attract the largest checks, and which ones need to look for alternative funding.

The headline numbers and what they mean

PitchBook reports that 2025 is on track to be the first year when AI receives more than 50 percent of total VC capital. That is not just a headline. It signals three structural effects.

• Concentration of capital. More funds are allocated to fewer themes, increasing competition for AI talent and infrastructure.
• Valuation pressure. AI companies can get higher valuations from the same or slightly better traction, creating valuation inflation in the category.
• Funding scarcity for non-AI ventures. Startups that lack an AI angle may find traditional VC fundraising slower or harder to secure.

Which sectors are capturing AI investment

VC dollars are moving across a small number of AI-related buckets. Those buckets are receiving the largest checks.

• Foundational models, meaning large language models and multimodal models used as building blocks.
• Infrastructure, including AI-optimized chips, cloud services, and data centers.
• Developer tooling, such as SDKs, model management, and observability for AI systems.
• Verticalized AI, where models are tailored to industries like healthcare, finance, agriculture, and legal services.
• Data services, including curated datasets, synthetic data, and data labeling platforms that feed model training.

Why VCs are prioritizing AI

Investors frame AI as a combination of a large addressable market and structural advantages. Key reasons include:

• Potential for rapid revenue scaling when products find product market fit.
• Defensibility through proprietary training data and model architectures.
• Network effects when platforms improve with more users and more data.
• Opportunities for follow-on capital and large exits when infrastructure and models consolidate.

These factors influence term sheets, with VCs often asking for stronger governance and control when they are investing significant capital into a hot sector.

What non-AI startups can do now

Not every startup can or should pretend to be an AI company. Still, there are practical strategies to navigate this funding environment.

1. Integrate AI features where they add real value

Consider incremental AI enhancements that improve core product metrics. Small, well-defined uses of AI can boost retention and unit economics, making the company more appealing to investors.

2. Emphasize unit economics and profitability

VC appetite shifts between growth and profitability. If your product shows strong margins, predictable customer acquisition costs, and clear path to profitability, that will attract funds that are more cautious about sector trends.

3. Pursue strategic or corporate investors

Corporate venture arms and strategic partners may value your capabilities for integration, distribution, or procurement benefits, even if you are not an AI company.

4. Target alternative capital

Explore revenue-based financing, angel syndicates, government grants, and non-dilutive sources. These options can sustain growth without chasing hot sector valuations.

5. Reframe the narrative without false claims

If your product benefits from data or automation, explain that clearly. Avoid claiming AI capabilities you do not have. Signal readiness with concrete metrics rather than buzzwords.

Practical fundraising advice for founders

VCs have changed what they look at when AI is the dominant theme. Founders should prepare for different questions and metrics.

• Lead with customer value and growth metrics, not just product roadmaps.
• Show clear unit economics, such as lifetime value to acquisition cost ratios.
• Document any data assets and explain how data improves your product over time.
• Explain team capabilities, especially experience with ML or data engineering if you plan to add AI features.
• Be transparent about technical limitations, deployment plans, and model governance.

Risks and caveats

Heavy concentration in AI brings its own set of risks that affect investors and the broader economy.

• Hype cycle risk. Elevated capital flows can lead to overinvestment and a market correction later.
• Regulatory uncertainty. New rules around AI safety, data privacy, and liability can change business models quickly.
• Talent competition. AI talent is scarce and expensive, increasing operating costs for both AI and non-AI companies that rely on data skills.
• Consolidation risk. Large platforms and well funded model providers can absorb smaller players, reducing diversity of innovation.

Examples and case notes

In this funding cycle, some companies building base models and AI infrastructure have raised very large rounds. Developer tooling firms have seen interest because they shorten time to market for AI products. Startups that successfully pivoted to deliver measurable AI enhancements have sometimes unlocked follow-on funding, while others have raised by highlighting strong revenue, margins, or strategic partnerships.

There are also examples of non-AI founders securing capital by focusing on fundamentals, such as strong unit economics, steady revenue growth, and committed customers. Those cases show that AI is not the only route to funding, but it is the one attracting the most attention this year.

Actionable checklist for founders

• Assess AI relevance. Determine whether AI adds real customer value, not just buzz.
• Audit data readiness. Can you collect, store, and clean data reliably for model use?
• Update GTM strategy. Show how any AI features will change conversion, retention, or pricing.
• Strengthen financial signals. Improve unit economics and extend runway through cost control or alternative financing.
• Explore strategic partners. Corporate investors can provide distribution or procurement benefits.
• Prepare realistic technical plans. Document model requirements, latency, costs, and governance processes.

Broader implications for the startup ecosystem and society

The shift of capital toward AI affects innovation diversity, employment, and long-term ecosystem health. More money into AI can accelerate useful products that affect health care, education, and productivity. At the same time, decreased funding for other areas could slow progress on problems that are not easily solved by models. Talent will flow toward AI, potentially creating gaps in other fields.

Policymakers, funders, and ecosystem builders may need to consider targeted grants, incubators, and public programs to preserve a range of innovation priorities. For consumers, the immediate effect is likely to be faster introduction of AI-powered features in everyday apps and services.

Key takeaways

• PitchBook data shows 2025 is on track to be the first year AI gets a majority of VC investment.
• That shift increases capital concentration, valuation pressure, and competition for talent and infrastructure.
• Non-AI startups should assess whether AI adds concrete customer value, and consider alternative funding if it does not.
• Investors prefer businesses with scaleable economics, data advantages, and defensible moats, all reasons they are favoring AI now.

FAQ

Q: Does this mean non-AI startups cannot raise money?

A: No. Strong fundamentals, clear unit economics, and strategic partnerships can still attract capital. The environment is harder, but options exist beyond traditional VC.

Q: Should every startup add AI features to attract investors?

A: Only if AI improves the core product in measurable ways. Adding AI for appearance alone can backfire with users and investors.

Q: Will this trend reverse?

A: Market cycles can change with time. Hype can contract if regulatory or economic factors shift. Founders should prepare for both continued investment and possible corrections.

Conclusion

PitchBook’s 2025 finding is a clear signal that venture capital is concentrating heavily on AI. For founders, the practical response is to be honest about where AI fits into product value, to strengthen financial signals, and to diversify funding strategies when necessary. For the broader ecosystem, the shift raises questions about the balance of innovation and the distribution of talent. Short term, consumers will likely see faster rollout of AI features. Long term, sustaining a diverse and healthy startup ecosystem may require intentional support beyond market-driven VC flows.