We know it’s totally stupid, but unfortunately tokenmaxxing is real. I know our management line isn’t that dumb, but this is what you get when the business is selling it.

He was. When it comes to marketing. This is was most people don't understand. Peter is a great marketing guy who got hired because of a hype vision, not because he is an outstanding engineer. Think of it like OpenAI hiring MrBeast of the coding world.

For me it's not even a "what the hell are you working on" so much as complete inability to understand how you can keep so many different processes working on distinct tasks. It simply doesn't map on to how I use these tools.

I spend most of my day writing extremely detailed prompts and that's how I'm able to get the sort of excellent results that confound skeptics. But I have to be honest with you: I don't think I can write (or think) fast enough to do two of these at a time, much less 15.

I definitely could not review what they are generating with any degree of confidence.

I'm really hoping you can explain what the heck your usage pattern actually looks like, because reading this makes me feel like I'm missing something.

Lets say I was at the casino and was spending a lot on casino chips but I also happen to work at the casino. I'm not really losing money whether if I win / lose since I'm using the houses money and there's little risk involved on every press of the button. The risk is far higher if I don't have that level of access and continue to spend the same amount of money on lots of tokens.

The same is true here with these agents. Some companies will realize that they can no longer afford to spend millions a month on tokens or even startups spending $5k - $6k per person per month on tokens.

I can only see local efficient models making sense as recovering from this unnecessary spending or even light gambling on tokens.

What I mean by this:

1. Intern, analyst, junior, or offshore level coding is cheaper when done by the machine.

// Side note: There is good reason the industry invests in suboptimal output from this set which moves to the "cost" column when using an LLM, but nobody's accounting for that.

2. For the interns, analysts, junior, or offshoring to do the right thing costs a multiple of the coding effort: the PdM/PjM stuff of course, but also the Stakeholder, Product Owner, Architect, Principal Engineer, QA, and SRE stuff.

3. If you are not a principal or staff engineer level engineer, you are likely unqualified to catch and fix the errors LLMs make across engineering, much less these other PDLC (product development lifecycle, which includes SDLC and SRE) loop.

4. For LLM output to be useful, your 'harness' has to incorporate all of that as well, which because it's so much harder than transliterating spec-to-code, balloons tokens exponentially.

5. Today it is faster, more efficient, and costs less, to work with LLMs "XP" (eXtreme Programming) style, pairing with the LLM actively co-creating and co-reviewing, steering for more effective turns.

So, your options are:

- ship garbage while costing less than a median first world SWE

- pair with the LLM actively for the benefits of XP

- add enough harness and steering the LLM costs more than SWEs, and still needs a human loop “move fast and break things to find out what's broken” style

I would expect that within a couple years, these other disciplines can be baked in enough the machine costs less for everything but surprises.

He has a different opinion of what it means to be lean than almost everyone else. That's fine, he's allowed to, but it's something you have to understand to make sense of any of his comments on things. He has a radically different set of values to most people.