I came to AI safety from markets, not AI.
My background is in quantitative trading and portfolio construction.
Markets teach you pretty quickly that being right is not enough.
You can have the correct thesis and still lose because you run out of capital before the thesis pays off.
A lot of finance is really about surviving the path: sizing, leverage, drawdowns, liquidity, financing.
That habit has affected how I read AI strategy.
One assumption kept standing out to me. A lot of discussion about frontier AI implicitly assumes that the capital keeps coming.
That may be reasonable. But it is still an assumption.
Frontier AI is extremely capital intensive, so I became interested in what changes during a serious funding contraction.
My main concern is that everything probably does not slow equally.
Capability work, product work, and safety work sit in different economic positions inside a company. Some are easier to connect to near term revenue than others.
During a contraction, that matters.
I would not claim that safety automatically gets cut first. But I do think its funding structure deserves more attention.
There is another asymmetry I find interesting.
Capital can disappear much faster than infrastructure does.
The GPUs remain. The trained models remain. The people and strategic incentives remain.
So a downturn can change who owns or controls frontier AI assets without making those assets disappear.
That could mean acquisitions, consolidation, government support, asset sales, or some combination of them.
The state involvement case is especially interesting to me.
We usually discuss centralized AI development as a deliberate policy choice. Financial distress could create concentration for much more mundane reasons.
You could end up with a more centralized system without having designed the governance around that concentration in advance.
This feels very familiar from finance.
During good times, funding structure looks boring. During stress, it suddenly becomes the whole story.
Institutions that looked independent turn out to share lenders, counterparties, infrastructure, or incentives.
I suspect AI has similar hidden dependencies.
This is probably the part of AI safety where my markets background is most useful.
I am interested in the assumptions underneath a strategy and what happens when one of them breaks.
Capital is one of those assumptions.
I do not think we understand the failure modes around it very well yet.