Lawmakers are largely supportive of helping AI startups and challengers grow and thrive. They understand the need for the United States to compete and win in AI and generally support small businesses and entrepreneurship. Yet, numerous state AI proposals—while intended to put safeguards in place for the biggest players—still risk sweeping in the startups at the forefront of AI innovation. The tools lawmakers reach for to carve out Little Tech, including compute and training-cost thresholds, aren’t built for the realities of how AI is made today.

In this conversation, Guido Appenzeller, investing partner, and Matt Perault, head of AI policy at a16z, discuss why thresholds based on either compute power and training costs fail to separate Little Tech from larger developers, and why revenue may be a more effective criteria for establishing what counts as an AI startup.

Topics covered:

01:33: Realities of startup teams building AI models

03:57: Challenges of defining frontier models by compute

06:46: Why competition at the frontier is key to US success

10:45: Practicalities of building and training AI models today

13:24: Why training-cost thresholds fail

16:47: When startups hit $100M in training spend

24:16: Revenue as an alternative metric to focus on use and market impact

28:09: Revenue as a clearer metric

31:48: Implications for startups

33:17: Loopholes to game thresholds

34:56: Closing thoughts

Resources:

Follow Matt Perault: https://x.com/MattPerault

Follow Guido Appenzeller: https://x.com/appenz

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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