awb

> Create a realistic eval suite for your use case using your actual data that you manually label, with easier, more complex, rarer samples, and run each model you are considering against it and decide based on the usual confusion matrix, cost, latency, or whatever metrics matter to you.

I’d imagine there’s only a tiny percentage of users that can or want to do this.

But what’s a reasonable win rate? 50%? Then you need 200 bets that could realistically capture $20B each (probably more to cover your losses).

$20B is a massively successful company.

And who’s managing the strategy behind a company that’s competing in hundreds of diversified verticals?

At that point, just take a % of an industry leader in exchange for giving them your frontier AI capabilities, some compute and maybe a little AI-native training/expertise.

Or just pick 10-20 $200B opportunities to go after.

If they’re targeting a $2T valuation, then spinning up a $20B company would be a 1% increase in value.

They’re chasing massive markets strategically, not trying to brute force small ones.

That might be the ultimate legal guidance, but is it settled though?

Good point. Not sure the average user would consider this though.

> What if I specifically tell it to not do anything nefarious or illegal?

Sure, but a reasonable user would expect that condition to be implicit in using a commercial model.

It’s what the safety/alignment discussion is about: how we can get models to actually comply with laws & ethics.

But the same problem exists in the real world. If you tell someone to “Try everything possible to get it done, I don’t care what it takes”, I’d imagine that people would be far more likely to bend/break rules than if you said “could you please help me with… ?”

Cool experiment Zach!

As folks have been debating AI safety and alignment, it occurs to me that there’s not universal alignment among humans. Per the stat you surfaced: 81% of humans pulled the lever, 19% didn’t.

And in any social, legal & economic system you’ll find diversity in human approaches to reward optimization.

Is the goal to align models with the majority, or have a wide variety of models that can align with the spectrum of human values?

No audio on Safari mobile either

> I don't think any new languages will easily beat the existing ones, simply because of the mass of training data that is available.

Seems like a solvable problem though by generating synthetic data that’s guaranteed to be accurate through linters, compilers and tests.

If it’s ~9 figures to train a frontier model, it seems like training on a new language could be a rounding error if it was a priority.

I could see the appeal of a new agentic-friendly language that’s focused on minimizing tokens. The standard library could be massive with no concern of making the language easily readable or learnable.

If it was a poison pill then why make the severance $8M instead of the majority (or all) of the value of the company?

The demand curve for speed and intelligence seems pretty steep to me.

If you look at the hiring marketplace, being just marginally better than your peers can be very lucrative.

If you’re competing on speed or capability as a company (or as an employee), you’re probably going to be willing to pay for the frontier.

Employees can quit too.

In both scenarios, it really depends on how many alternative options for income you have available.

When you’re paying someone’s bill/salary, it changes the dynamic.

Just like someone in customer service might act differently with coworkers vs customers.

Exactly.

It’s the concept of a management chart as an inverted pyramid with each layer holding up and supporting the layer above them. If you imagine a promotion as working your way down the corporate pyramid, then it’s easier to see how the managers at the bottom are carrying more weight and deserving of higher pay.

As opposed to a pyramid where it’s visually represented as the broader management layers supporting the layers above them.

In a pyramid, it looks like the CEO has a cushy, overpaid job. In an inverted pyramid it looks like they have the weight and responsibility of the company on their shoulders.