Safety and innovation trade off against one another: what makes a model safer makes it less capable or less widely available, so pushing on one goal costs ground on the other.

4 source(s) assert this claim, 7 deny it.

Who asserts it

So on the one hand, access to AI data is a good thing for research, but on the other hand the same open models can create risks just because they are open. Senator Hawley and I as an example of our cooperation road to meta about an AI model that they released to the public, you are familiar with it, I'm sure Lama they put the first version of LAMA out there with not much consideration of risk. And it was leaked, or it was somehow made known. The second version had more documentation of its safety work. But it seems like meta or Facebook's business decisions may have been driving its agenda.

Oversight of A.I.: Principles for Regulation — Full Hearing Transcript — 2023-07-25

Third, we should recognize the science of testing and auditing for AI systems is in its infancy. It is not currently easy to detect all the bad behaviors in the AI system is capable of without first broadly deploying it to users, which is what create the risk. Thus, it is important to fund both measurement and research on measurement to ensure a testing and auditing regime is actually effective funding. NIST and the National AI Research Resource are two examples of ways to ensure America leads here.

Oversight of A.I.: Principles for Regulation — Full Hearing Transcript — 2023-07-25

Along with my co-founders and employees, I have grappled with this duality of risk and benefit since the beginning of Anthropic. Not building the technology deprives humanity of benefits or simply places AI in the hands of authoritarian powers, while building it too fast is reckless. We have sought a middle way: to show that it’s possible to build carefully and succeed commercially, and to make safety something on which AI companies compete. In other words, to create a _race to the top_.

We Must Pace the Frontier — 2026-09-12

Who denies it

Now, regulation is often said to stifle innovation, but there is no real trade-off between safety and innovation. An AI system that harms human beings is simply not good ai. And I believe analytic predictability is as essential for safe AI as it is for the autopilot on an airplane. This committee has discussed ideas such as third party testing, licensing, national agency and international coordinating body, all of which I support. Here are some more ways to, as it said, move fast and fix things. First, an absolute right to know if one is interacting with a person or a machine.

Oversight of A.I.: Principles for Regulation — Full Hearing Transcript — 2023-07-25

I don't think, I mean, there are many areas where there's important trade offs. I don't think this is one of them. I think such requirements make sense. I mean, to give a little of our experience in, you know, red teaming for these biological harms, you know, we've had to work on, you know, piloting a responsible disclosure process. I think that's less about reporting to the public, more about making the other companies aware. But, you know, the two things are similar to each other.

Oversight of A.I.: Principles for Regulation — Full Hearing Transcript — 2023-07-25

**In fact, openness may be one of the most important paths to AI safety and security.** Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk. It results in a small number of single points of failure, weakens competition, and leaves critical technology in the hands of a few providers.

Open Weights and American AI Leadership — 2026-07-24

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