A past employer thought it was a good idea to put up a leaderboard of who sent the most Slack messages. They celebrated the people at the top for being so active.

Predictably, everyone started talking in Slack like their jobs depended on it. Everyone was responding to everything. Instead of writing out a complete message and pressing enter, they'd send each fragment of the sentence as a new line.

The Slack leaderboard was never shown again. Unfortunately the habit remained because people were afraid they were going to be secretly judged by how much Slack activity they generated.

I expect the same thing is going to happen at companies who had token leaderboards. Once you've instilled that fear in people, they internalize the expectation.

It’s funny how many times the same thing happens at each large company. I think people’s thought process is this:

> Oh wow! If I paid for this myself I would have spent a lot of money! Are other people spending as much as me? I’m going to create a leaderboard!

> Oh no, my misinformed manager is using the leaderboard as a slight of hand for work. I need to game this now.

Then the leaderboard is banned… I can’t see how this ever really goes up the chain beyond director.

It wasn't leadership doing this though. Any meta IC can generate internal apps and dashboards. This was unofficial and unsupported. Some random IC just made it for fun. Management is usually pretty lax with stuff like this (plenty of games and joke internal apps) so they left it up until it became a problem.

> Who could possibly have predicted that happening?

Charles Goodhart :-)

I still don't understand how Mark Zuckerberg has any serious investors, he went on this AI tangent and has absolutely nothing to show for it, despite FB / Meta having built some key tech in the space. He needs to stop trying to do something "different" and literally try and build a serious coding agent he can sell, he could have probably had something worthwhile in that space by now.

He started being drastically more serious into AI in 2022, and 2023 and he has nothing to show for it.

Heck, he could have rented GPUs the way Elon did at this point and either mended the bleeding or stopped it, not sure how many he has, but it beats losing this badly.

If he doesn't wake up and learn how to business, I suspect he will lose his empire he's built up for himself.

> Who could possibly have predicted that happening?

Everyone except the executives who get paid millions to predict exactly that.

I know right? What did the leadership think would happen when they give some of the worlds greatest software engineers (supportably), a easily quantifiable metric to target?

Given that Meta has run 5ish layoffs at this point, and everyone is in survival mode, what did they expect? Everyone wants to juice whatever numbers possible to keep their jobs.

Now come on, there was a recent post where the author argued that infallible management knew this would happen, but was part of the double-secret-probation strategy to get the cogs to finally start using AI.

unfortunately at big tech, this shit will keep happening.

people who make it to managers tend to have bozo tendencies & are yes men.

before it was lines of code, Jira tickets closed. Now it's tokens spent.

How dare you question the most effective allocators of capital.

For real. I've used 8B tokens in the past month and haven't hit my limits even once. In fact, I can't even get close except for the day I used Fable. I've barely stopped. Claude keeps reminding me to sleep.

They can't.

The subscriptions are for personal use not enterprise.

i.e. [1] "This article is about paid Max plans for individual consumers. If you're part of an organization looking to use Claude with your team, refer to Team and Enterprise Plans."

[1]: https://support.claude.com/en/articles/11049741-what-is-the-...

Enterprise customers don’t get those plans, at the enterprise level you have to pay by the API rate… so people don’t have limited use, but you’re also not getting the heavily discounted rate the “normal” plans are at.

Big enterprises don't get to have those subscriptions. OpenAI or Anthropic simply won't sell them to you if you need a couple thousand of those.

The whole phenomenon of metric based Eng evaluations is because leadership does not trust line managers to evaluate individual engineers.

What outcomes though? The ones I’ve seen posted are still nonsensical metrics that a publicly traded firm absolutely doesn’t care about.

It wants to see faster R&D, higher revenues from existing assets, greater operating margins, higher sales to invested capital ratio and so on…

The best way to measure that for a software firm is up-time of services, usage and project completion duration

> measure outcomes (impact)

This is also not easy. In particular proactively preventing bugs is not rewarded

It's not flashy.

When shit just works for months or years no one is going to come and praise you for stuff you did a while back.

You are better off breaking stuff and then fixing them to show how useful you are.

A million tokens is like $15 with SOTA models… that’s their allocation?

I can’t tell if you’re complaining that Meta isn’t saving the whales or that their products aren’t good. If it’s the latter, you should double check their financials.

I guess maybe they can crank out more ads in their dystopian ad space of a social network site.

I love how confidently you say this, with no evidence provided (and I doubt you have any.)

Just a pristine comment section yap.

Because Muse isn't good enough and why use Muse if they'll let you use Opus for free?

that is a fair point. The contrast between Meta and Apple could not be bigger here. Apple has billions of devices and yet they decided to use 3rd party models from OpenAI and later Google to build their AI features rather than building foundational models in house. Yet Meta did opposite: they built models (spending billions of $$$ and firing 10% of the company) for billions of users who rather would not use Meta AI features.

As many companies do with all their budgets, down to the trivial and clearly positive EV cost of free coffee. So it goes, cost controls are hard and necessarily imprecise.

Tokens are less valuable than the eyeball metric of the Dotcom era. At least the eyeballs were real then.

I'd argue most of the AI value is related to how 'Dead' the internet is.

Don't worry, once we achieve post-scarcity they will have more tokens than they could ever dream for spending.

How soon until this becomes part of the "no one wants to work anymore" argument

Because PDFs are a nightmare of a format and the only thing that’s is reasonably guaranteed about them is they will render to an image that people can read, the parsing of which will be much less token efficient than the equivalent text

Amen. Normal office work is wildly different from what we read about on HN. If you were a CEO, determined to lay off all your people, you would want to really zero in on having your AI solve these very unsexy problems: extract data from Office and PDF. Grab data from some part of the screen of a webapp and parse it. drive a line of business app via keyboard or mouse simulation. I know there are companies out there that try, eg Appian and (here in YC) Skyvern, but its a hard problem and yet I feel this is where the true money is.

I hope someday we can get out of this local maxima of PDF documents. The format is terrible, but was right place, right time and might be impossible to dislodge.

You don't need to use an online service to do this; you get to avoid spending money on tokens doing it offline.

Gemma 4 works perfectly well offline on limited hardware (I have an 8GB video card) and can handle extracting text from image-based PDFs just fine.

Take a PDF -> run it through MarkItDown [1], using the OCR plugin if you need (point it to Gemma 4) -> now you can ask Gemma 4 questions about the (markdown) document.

I am sure Gemma 4 could even create a GUI to make this process very simple for a non technical user.

[1] https://github.com/microsoft/markitdown

The best way to parse pdfs is to convert them to images and feed them into the llm.

This workflow is highly optimized.

You are missing that the product is the hype cycle around AI and that's worth Trillions of $ (Trillions with a T). Why build a PDF parser that generate text when you can BS in a podcast and get paid.

This discussion was about measures, goals and incentives. Follow the incentives.