TechCrunch Here are the 33 US AI startups that have raised $100M or more in 2025 | TechCrunch - AI2Work Analysis

Executive Summary

• 33 U.S. AI firms raised $100 M+ in 2025—up from 49 in 2024, but with a sharper focus on healthcare, legal‑tech, and hybrid hardware‑software.

• Mega‑rounds are now the norm: seed rounds can exceed $1 B, and valuations have outpaced revenue growth.

• Founders face a double‑edged sword—rapid funding fuels speed but also heightens scrutiny over monetization, compliance, and talent retention.

• VCs should prioritize companies with clear revenue pathways, domain‑specific data assets, and strategic incumbent partnerships.

• Successful founders will adopt staged capital infusions tied to product milestones, embed regulatory frameworks early, and build vertically integrated hardware–software stacks where latency is critical.

This article decodes the 2025 funding landscape through a startup advisor lens—offering actionable insights for venture capitalists, angel investors, executives, and growth strategists looking to navigate an era of accelerated capital flow and shifting valuation logic.

The 33 companies that crossed the $100 M threshold in 2025 illustrate a market where

speed of capital deployment has overtaken traditional revenue metrics

. The sheer volume—33 versus 49 in 2024—signals an acceleration, but the qualitative shift is more important: early‑stage firms now routinely secure multi‑hundred million dollar rounds before showing measurable product traction.

Key drivers:

• Foundational Model Boom : GPT‑4o, Claude 3.5, and Gemini 1.5 have lowered the barrier to entry for high‑performance LLMs, encouraging founders to launch with a “foundation first” mindset.

• Regulatory Clarity in High‑Value Verticals : The FDA’s 2025 AI guidance and the EU AI Act’s data‑ownership provisions have made compliance a competitive moat, especially in healthcare and legal‑tech.

• Hardware–Software Convergence : Edge chips from EnCharge AI and silicon‑AI stacks from companies like OpenEvidence demonstrate that latency‑sensitive use cases (e.g., real‑time imaging) are now driving capital toward hybrid solutions.

The distribution of funding reveals a clear prioritization:

• Healthcare : Abridge’s $300 M Series E (valued at $5.3 B) shows investors betting on domain‑specific LLMs that can integrate into clinical workflows.

• Legal‑Tech : Harvey’s second $300 M round and its $5 B valuation underscore the commercial viability of contract review and e‑discovery tools.

• Hybrid Solutions : EnCharge AI’s $100 M Series B and OpenEvidence’s $210 M raise illustrate a growing appetite for integrated hardware–software offerings that solve latency bottlenecks.

For founders, this means that building a product around a regulated domain with a clear data pipeline can unlock both valuation upside and strategic partner interest.

The most striking trend is the widening gap between

valuation and disclosed revenue

. Thinking Machines Lab’s $12 B seed round with no public revenue figure exemplifies this phenomenon. Investors are betting on future monetization of foundational models—API licensing, embedded services, or data‑monetization platforms.

Implications for VCs:

• Due Diligence Must Include Monetization Traction : Ask founders to present a clear path from model training cost to recurring revenue streams. Look for early pilot contracts, pricing models, and churn projections.

• Risk of Overvaluation Is Real : If revenue remains elusive beyond the next 12–18 months, valuations could snap back. Monitor burn rates closely against realistic runway extensions.

Startups are experimenting with several monetization strategies:

• Subscription APIs : Classic SaaS model—developers pay per request or tiered usage. This works well for generalist LLMs but struggles in regulated domains without robust compliance layers.

• Embedded Licensing : Companies embed the model into their own products (e.g., a medical imaging platform). This creates higher barriers to entry for competitors and often leads to long‑term contracts.

• Data Monetization Platforms : OpenEvidence’s proprietary clinical datasets can be sold or licensed to third parties, creating an additional revenue layer beyond the model itself.

• Hybrid Bundles : Combine subscription APIs with hardware licensing (e.g., EnCharge AI’s edge chips). This is increasingly common in latency‑critical sectors like autonomous driving and real‑time diagnostics.

Founders should articulate which mix aligns best with their product, target customer, and regulatory constraints. VCs will favor clear, defensible revenue pathways that can scale without proportionally scaling cost.

The surge in healthcare and legal‑tech funding is matched by tightening regulatory scrutiny:

• FDA AI Guidance (2025) : Requires premarket submission for high‑risk diagnostic tools. Founders must embed clinical validation from day one.

• EU AI Act : Introduces data‑ownership requirements and risk categorization that can impact model retraining cycles.

• State-Level Data Privacy Laws (e.g., California Consumer Privacy Act extensions) : Increase the cost of data acquisition and storage, especially for patient or client data.

Strategic recommendation: Build a compliance team early—legal counsel, data privacy officers, and clinical advisors should be part of the founding squad. A compliant product reduces the risk of regulatory delays that can derail funding milestones.

While the Bay Area remains the dominant talent pool, Austin, Seattle, and Toronto are gaining traction:

• Austin : Strong venture ecosystem, lower cost of living, and a growing AI research community.

• Seattle : Deep ties to Microsoft’s Azure AI platform provide an infrastructure advantage.

• Toronto : Canadian tax incentives and proximity to European markets make it attractive for data‑centric startups.

VCs should consider the geographic footprint when assessing a startup’s ability to scale talent without inflating costs. Founders can leverage remote work models and local partnerships (e.g., universities, incubators) to mitigate talent scarcity.

The rapid funding cycle raises questions about the realistic timeline for exits:

• IPOs : The market remains cautious; companies need a proven revenue track record and robust governance before going public.

• Strategic Acquisitions : Incumbents such as Salesforce, Google AI Futures Fund, and Shopify Ventures are actively co‑funding startups, positioning themselves to acquire or integrate them later.

Founders should align their product roadmap with the acquisition interests of incumbents in their vertical. VCs should evaluate whether a startup’s technology aligns with an incumbent’s strategic roadmap—this can accelerate exit timelines and increase valuation multiples.

• Adopt Staged Funding Milestones : Instead of a single mega‑raise, structure capital infusions around clear product development milestones (e.g., MVP launch, first paying customer, regulatory approval). This aligns investor expectations with tangible progress.

• Prioritize Domain‑Specific Data Assets : Secure high‑quality, proprietary datasets early. A robust data pipeline can be a defensible moat that justifies higher valuations and attracts strategic partners.

• Embed Compliance from Day One : Build a compliance framework into the product architecture—data encryption, audit trails, and risk scoring should be native features rather than add‑ons.

• Leverage Hybrid Hardware–Software Offerings Where Latency Matters : Edge chips and silicon‑AI stacks are becoming essential in healthcare imaging, autonomous vehicles, and real‑time analytics. Founders should partner with hardware firms or develop in‑house solutions to capture this market.

• Target Strategic Incumbent Partnerships Early : Co‑funding from Salesforce Ventures, Google AI Futures Fund, or Shopify Ventures not only brings capital but also a pathway into enterprise ecosystems. Align your go‑to‑market strategy with these partners’ APIs and integration roadmaps.

• Maintain Transparent Revenue Projections : Investors are increasingly skeptical of valuation models that rely solely on future potential. Provide realistic revenue forecasts, cost structures, and churn assumptions to build credibility.

• Focus on Talent Retention in Emerging Hubs : Offer competitive equity packages, remote flexibility, and clear career progression paths to attract top talent outside the Bay Area, reducing burn while scaling.

• Plan for Exit Early : Whether aiming for an IPO or acquisition, map out the exit criteria—market share thresholds, regulatory milestones, and revenue targets—to align with investor expectations.

Looking ahead, the funding engine will likely continue to accelerate but with tighter scrutiny on monetization:

• AI-as-a-Service Platforms : Expect more enterprises to adopt modular AI services (e.g., fine‑tuning LLMs for specific use cases) rather than building from scratch.

• Regulatory Sandbox Expansion : Governments may introduce sandbox programs that allow startups to test regulated AI solutions with reduced compliance overhead, accelerating product-market fit.

• Vertical Integration Trends : Companies like OpenEvidence and EnCharge AI will lead a wave of vertically integrated solutions that combine proprietary data, hardware acceleration, and SaaS pricing models.

• Capital Allocation Shifts : VCs may shift focus toward later‑stage companies with proven revenue streams, while early‑stage investors will demand clearer paths to monetization.

The 33 U.S. AI startups that raised $100 M+ in 2025 illustrate a market where capital velocity is high, valuations are aggressive, and regulatory compliance is increasingly pivotal. Founders who can articulate clear revenue pathways, secure domain‑specific data assets, and embed compliance early will stand out to investors and strategic partners alike.

Venture capitalists should look beyond headline valuations—demand concrete monetization plans, staged funding milestones, and evidence of regulatory readiness. By aligning investment theses with these criteria, both founders and investors can harness the 2025 funding frenzy to build sustainable, high‑impact AI businesses that are ready for the next wave of growth.