Gotcha.

I thought McDonalds is the supplier and the franchisee is the one paying. Either way, that money is transfer-billed out of the country.

“This random part of the code is slow, I used an LLM to generate a PR that speeds it up.”

Okay, you optimized the part that’s not a bottleneck, sped up nothing and cost the company $100 in tokens. Good job?

Rumors are worth squat when they’re most likely put in motion by the people with a vested interest in this industry.

Let’s talk about profits when there’s real data from the IPO documentation.

> And you are vastly overstating the effect of S&P500 fast tracking, the plan was to reduce it from 12 months to 6 months; which is more than enough time for the market to find a price.

They might never reach 6 months of profitability, let alone 12 months.

In both cases enforcement is reactive and happens after an accident has already happened.

That’s an insane level of detail and I can understand why it makes sense to use an LLM to remove that busywork, otherwise you’d be spending half of your time on this bureaucracy.

For the company I would imagine it could be more cost efficient to stop being so onerous instead of burning tokens.

Isn’t that just more work than logging it yourself?

Doesn’t seem to be widely adopted.

Wouldn’t expect anything less from someone working as a manager at OpenAI. I don’t think their org culture is survivable if you’re not 200% all-in on LLMs eating the universe.

Yet startups keep trying it and failing. Turns out users actually want exclusive access to that hardware to have a smooth experience. The tradeoff has always been between faster exclusive hardware or slower but cheaper shared hardware.

If local hardware can’t beat shared hardware on performance then something’s wrong? Either it’s because the providers are charging wildly below cost or because local hardware just hasn’t needed to catch up. Maybe it’s both.