AI policy impacts startup competitiveness
What startups are looking out for in 2026
Why startups care:
In every corner of the economy, there is a flourishing ecosystem of startups building AI, building with AI, and using AI to better everyday tasks. Some startups are building their own machine learning models to perform specific tasks. Most startups, though, are leveraging foundation models—either by licensing from market leaders or accessing open source—to fine-tune and build unique products. Startups are building tools that sit at all places in the tech stack—an architect is using an AI startup’s tool to create floorplans, that startup is fine-tuning an industry-specific model developed by a startup in the construction space, and that model developer is using an infrastructure-layer startup to store and tag that floorplan training data. The AI policies that have been enacted or proposed at the state-level will touch every part of this AI value chain, shaping how startups can build with the technology and at what cost.
“...a lot of amazing solutions that society is coming to rely on that wouldn’t be possible without AI. It’s important for policymakers to be careful when thinking about how to handle technologies that are still evolving.”
- Laura Truncellito, Founder, Enployable, Tysons, Virginia
Enployable is AI-powered platform designed to unlock hidden talent and address labor shortages in the construction, energy, transportation, and tech sectors through two-way matching between employers’ missions and cultures and job candidates’ values, beliefs, and soft skills.
AI proposals startups are watching:
State lawmakers introduced over 1200 bills related to AI in 2025. Of course, like most bills, many of those went nowhere, but it’s hard—especially for resource-strapped startups—to predict which ideas will have traction and which states will get new rules across the finish line. However, there are several common frameworks likely to appear across states in 2026.
⚠️ Rules for AI and “consequential decisions” in “high risk” areas
What it is: Many AI proposals, including those passed into law in Colorado and through agency rulemaking in California, regulate the development and deployment of AI systems involved in “consequential decisions” across categories of “high-risk” areas—such as education, employment, finance, health, or housing.
Overbroad definitions: Policymakers often paint with too broad a brush, unintentionally sweeping in low-risk applications of AI where AI is merely assisting humans making high-risk decisions or used to help make decisions outside of high-risk areas.
Liability for downstream misuse: The most egregious versions of these proposals would create liability regimes that hold AI developers liable for unlawful downstream use by customers—often by expanding the reach of consumer protection and civil rights laws beyond bad actors to widely used and benign technologies.
Why it matters for startups: When rules governing AI’s role in consequential decisions in high-risk areas include overly broad definitions, they scope in and place compliance burdens on startups building benign tools. New obligations—even when they are similar to existing rules elsewhere—such as notices, reporting, and impact assessments are estimated to require tens of thousands in initial spending and ongoing costs. And if AI startups are held liable for downstream use of their tools, they will be exposed to costly litigation over factors outside of their control, creating untenable legal risks that disincentivize innovation.
⚠️ Regulations for model development
What it is: State lawmakers advanced multiple frameworks for regulating the development for the largest foundation models. States have adopted legal frameworks for foundation model development, requiring publicizing safety plans, issuing transparency reports, reporting “critical incidents,” and providing whistleblower protections. Other proposed frameworks have included third-party audit requirements and imposed liability on developers for downstream uses of their AI models.
Why it matters for startups: Startups are building upon foundation models—both open and closed source—to create unique services. Many of these policy frameworks create new obligations that will increase the cost of model development, which could be passed along to startups. Additionally, third-party audit requirements and poorly-tailored liability regimes are most poised to negatively impact the cost and availability of the models startups rely on. Holding model developers liable for the actions of end users will disincent model development and undermine the availability of all tools and models, especially open-source models.
✅ Resources for AI R&D
What it is: A handful of states have set up or explored programs to encourage the growth of AI in their local research institutions and entrepreneurship hubs, by providing resources and partnerships to reduce barriers to developing AI, especially compute and data storage.
Why it matters for startups: AI R&D is expensive, especially access to cutting edge compute clusters. These programs help attract startups, build out local talent pools, and contribute to advances in AI technologies that are commercialized by startups.
How startups can get involved:
The perspective of startups developing and deploying AI should be front and center as policymakers consider these changes. To help startups stay informed on what’s happening in their state and identify engagement opportunities, Engine launched a free portal where startups can get updates on what’s happening in AI policy conversations in their states, access resources about the differing types of AI legislative proposals, and find information about contacting their representatives.