Hudson questions companies on self-improving AI and data center costs

Thank you so much, Chair.

We heard earlier from a panel of whistleblowers who raised a number of flags, and one issue raised was that of self-improvement among AI agents.

What is your company's core belief around the role of self-improving AI in your overall strategy?

And to be more specific, is it core to your AI strategy or a less significant part of the strategy?

Could you continue your race to the top or the bottom, I suppose, depending on one's perspective, without self-improving AI agents or with significant guardrails on the way agents self-improve?

And my second question is, when a new AI data center strains a local grid or water supply, the costs can end up in every...

ones utility bills while the benefits go mostly to the company.

Who should bear those costs?

The company that creates the demand, the utility that builds the capacity, or the public that shares the system but played no role in building it?

Thank you.

Thank you for your question.

I'll start off by addressing the question about RSI.

To say that we believe that RSI may be one of the most consequential safety and security issues of the coming decade.

And that's why OpenAI has advocated for federal legislation specifically focused on RSI

to include independent technical evaluators of internal lab practices, as well as the pace towards RSI development.

We've made that work public in our Frontier Safety Blueprint.

It's available online, and I'm happy to share it with you as follow-up.

On the case of electricity costs, look, OpenAI is committed to paying our own way on energy.

But we're also committed to building with and for communities.

All of our Stargate sites, which are our infrastructure sites, have what we call a community compact.

That's developed with the community and addresses how we're going to work together.

For example, we recently announced $80 million of community-based...

benefits investment at our site in Georgia.

Thank you.

Council member, in relation to your first question on recursive self-improvement, if I may, I might just go back to a point that I mentioned earlier in the day in relation to recursive self-improvement,

that we believe that committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure that this technology is developed safely.

In relation to your second question, in terms of electricity and data centers, we believe that customers should not bear costs.

And we pay the full cost for electricity used in our data centers.

I also might just mention as well our Futures for Everyone Fund, which is a $1 billion investment in relation to communities that we partner with and work with with our data centers.

So we also take a very prudent approach to self-learning inside our model training.

We use AI models to help us evaluate, benchmark, and refine other models.

But our use of this type of iterative model development is confined to narrow supervised processes, that's human supervised processes, to keep progress verifiable and within defined safety limits.

We also manage the risks of fully autonomous model self-improvement through our frontier safety framework.

So we're very mindful about RSI and are quite controlled in the way that we think about self-learning internally.

On data centers, similar to the other companies represented today, we believe in paying our own way and have pledged to do so, particularly when it comes to the energy demands of data centers.

We've also invested in a wide range of energy sources to ensure that our data centers

have an independent energy source that does not strain the grid and, in fact, adds back to the grid so that in the long term it will benefit wider energy customers.

Thank you.

Likewise, on recursive self-improvement, we take that extremely seriously and have from the start.

I think from my

I'd like to review that there are three really important things here.

The first is just measurement and understanding.

I'm glad you raised this question because we think this is something people need to talk about.

In late spring, we published what I think is a first of its kind assessment of how it is happening or signs of it happening at Anthropic.

Happens in a lot of ways and it's nuanced.

In late summer, we published a follow-up that went into further detail of how we should understand and measure self-improvement.

But that's not enough, and that's why you saw us and our CEO publicly call for pacing the frontier, because candidly, the technology is moving very fast, and we think we would benefit from having more time.

And this is the second component.

And then obviously the third thing that you need to do here is figure out what the right guardrails are, but additionally what level of technology development we should be going for, as well as what safety processes we need along the way.

And I think this is just where we're at now, and I'm glad that we're having this discussion.

On data centers, this is outside of my expertise.

My understanding is that we pay our own way, and we're committed to contributing and working with the communities around them.

But I'll have to defer to my excellent colleagues who work on it.

Thank you all and thank you, Chair.

I do want to just make my one little note that it seems like the self-improving aspect is absolutely core

to everyone's AI strategy and that's certainly concerning given the testimony we heard earlier this morning.

So thank you, and I look forward to any follow-up.

Thank you so much.