EDIT: whoa, I used "way of the future" as a reference to Howard Hughes in "The Aviator", not this Way of the Future religious organization thing I just stumbled on; no intended reference there.
This is my understanding too. The underlying assumption is that action leads to information, iterations lead to enlightenment. So from an org's point of view, tokenmaxxing means encouraging everyone to explore as much as they can. Of course, token volume should not be the only metric - tokenmaxxing is just a catchy phrase.
The unusual thing is perhaps how global and cross industry it seems.
Genuinely asking: for which fads was it actually beneficial to jump in during the hype phase? Was there ever anything so critical that there was some huge disadvantage if you didn't adopt it right away?
ETA: I suppose the complicating factor, at least for B2B, is "customers demanding $fad", particularly when the purchasing decision makers don't actually understand what $fad is (e.g., "cloud", "blockchain", "ai", ...). If you don't become "$fad native" right away, you lose the Dunning-Kruger segment of the market.
The AI equivalent of the PC revolution isn’t quite here yet, but it’s the only way forward.
In many cases it really didn’t/doesn’t matter if the AI automation actually works, just that people think it could - and hence leave money on the table.
Not sure if you mean this in a good or bad way.
Generating a feature that is 90% correct in a tenth of the time is a reasonable tradeoff if you're trying to gain traction.
Generating a feature that is 90% correct in a tenth of the time, risking a multi-billion-dollar business, is a terrible tradeoff.
Small teams building continuously get to write features that are 90% correct in a tenth of the time.
Big enterprises get to write features that are 90% correct barely twice as fast, because all of the bottleneck lies elsewhere. They also spend more on AI per user because of the internal dynamics pushing people to adopt AI irresponsibly. They can correct the 10% of errors slower than small teams because of bureaucracy, increasing the cost of errors that show up in the product. Furthermore, they have less to gain from a given amount of speedup because they had plenty of engineering velocity anyway compared to small teams.
I don't think big enterprises will start winning from AI technology until AI truly can automate almost everything in a company and let said company outproduce competitors by burning tokens alone. That's nowhere near possible right now.
Now there is demands to justify not using AI like this, but people don't care about details. Which AI tool I use apparently doesn't matter at all, even if there are presumably productivity differences between them.
Edit: typo
Here’s a concrete example of conservative AI usage: I use Claude to vibe code my nvim config. Now, who cares if my nvim config is AI slop? What’s the worst that can happen? Nvim works for me now way better than it ever did when I was limited by the time I was willing to spend configuring it manually.
The profitability comparison is fraught but worth noting that by then AWS was already extremely profitable.
Where does it show up in quarterly results?
I can’t see how it’s sustainable just based on “this feels more productive”
I'm not sure how it would show up in quarterly results.
Is it like the stereotypical dad who rents a power washer, powerwashes every exposed surface on his property, and then doesn't need to do any powerwashing for a few years; his neighbor who gets an Instant Pot and uses it for every meal for a month, then sees it gathering dust when the family gets tired of pressure-cooked stews; or like their neighbor who gets a microwave oven and uses it multiple times a day for decades?
I guess only time will tell.
A few mundane things got automated, but these were just back office admin type work. Nothing that's going to show on the P&L. Yeah those people now have a little more time for other things, but those other things are also not revenue generating. No FTE got replaced by it so in the end they just paid for a bunch of administrative positions to be a little less busy. Great for the workers who are now less stressed, but almost no impact on the business financials except there's now yet another subscription.
Your employer is doing it wrong. You need usage surveillance with sanctions for low/declining use, then people won't stop using it.
If there's anything I've learned as a software engineer, it's that agreeing with and defending the ideas of business leaders and Silicon Valley VC influencers proves I'm very intelligent.
when I quote this comment later, with appropriate attribution, please know that I will be shaking my head and frowning while doing so
That’s the explanation how you can have both the anecdotes of amazing AI productivity and rigorous studies showing anything from actual loss of productivity to single-digit gains.
It's like building a super tall Jenga tower very quickly but laying the bricks much worse than a careful player.
The code AI produces is not created equally, not even close.
When you try to replace your entire brain with AI things are going to go wrong.
For the product my friend works on, it's definitely the latter. I definitely don't expect this party to last forever.
> I'm not sure how it would show up in quarterly results.
Ultimately they make money selling rides, not selling software. The Uber app is mature and adding new features is unlikely to significantly increase sales.
Writing 2x more code doesn't translate to 2x more revenue unless it results in 2x more rides.
It would if it meant they then fired half their software engineers, which is the ultimate goal.
Standard answer is "companies that are not seeing significant gains from AI just aren't AI-ing hard enough, trust me bro".
Sometimes things are actually just finished. They don't need to treadmill.
Depends on the cost
So I wonder what the heck were all those billions of AI tokens burnt on that they extinguished it in just 4 months into the year?
Uber’s business is relentlessly confusing for people who think it’s a simple app to send an alert to a nearby driver to pick you up.
Uber operates at a scale where there are no trivial problems because even small changes can impact hundred of thousands of customers. They can also justify spending time and money on new features that only 0.1% of customers might use because 0.1% of their customers is a very large number.
> so what did all those millions spent on developer salaries get them?
There was no doubt about what these developer salaries got them. It was to keep Uber stable and running in thousands of jurisdictions with varying rules/regulations.
The idea of using AI was (I hope) not just to replace developers for this purpose but to also ship features/products beyond what was already being offered. It has however not panned out as these CEOs/execs thought it would.
> They can also justify spending time and money on new features that only 0.1% of customers might use because 0.1% of their customers is a very large number.
And what are those features exactly? Because even the President of Uber doesn't seem to know:
"“That link is not there yet, right? I think maybe implicitly there is more that is getting shipped, but it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25 percent more useful consumer features,’” said Macdonald."
The budget allocated to AI for the year has been wiped out in 4 months.
* In App Hotel bookings in partnership with Expedia.
* Travel Mode with suggestions on where to eat and visit when travelling.
* Eats for the way - your driver picks up a takeaway for you to eat while they drive you to your destination.
* Voice bookings using AI and speech to text.
How did we ever live without them!
This seems like the kind of terrible idea that an LLM might have come up with. I'm pretty sure most drivers do not want people eating (especially a whole meal) in their car, and I can't imagine a lot of instances where you're calling an Uber and don't have time to get yourself food, but don't mind waiting an extra 10 minutes for the driver to detour, find parking, and wait for your food.
Recently I got a car to take me to the train station and picked up food on the way. Seems pretty common to me. Of course, I didn't need or want it charged as a premium feature in the app.
Are they profitable yet lol
In a few years, what do you end up with? The modern version of every single fucking app we use today.
If it's easy enough to add to the app and sticks around for a while, it may well be profitable even if only a small percentage of customers use it or even realize it's available.
https://www.theverge.com/podcast/922909/dara-khosrowshahi-ub...
Can't say I am convinced.
Nobody's going to jail.
Ironically enough the only moat left would be what you can buy from Washington.
I can understand it from the side of the companies selling tokens and AI hardware. I don’t understand the race to spend more on internal tools.
I’ve been sitting around waiting for my company to buy a number of necessary bits of tools. They cheap out on every solution imaginable. Datadog is too expensive, let’s buy a cheap solution that costs us months of setup time. Configuration management is too expensive, let’s use the free version with no audit trail or dashboard.
But everyone…in the entire company…gets multiple AI tool subscriptions.
I don’t remember investors being this stupid at any other point. I don’t recall investors pressuring my company to use blockchain or NFTs.
As a more obvious example consider that cars were just invented and the post office management thinks that they could improve performance of letter carriers. But right now cars are slow, break down a lot and there isn't much infrastructure for them. Lots of letter carriers will (rightly) think that it is a waste of time because they need to get in, stop, park between every house and they break down so often it isn't worth it and half of their route is unsuitable for a car anyways. But if cars are forced for a while they will find out what routes work well for cars and which don't, improve the cars and related infrastructure to make cars more effective and other improvements to unlock more productivity.
So yes, right now management is wasting money on cars and gas for no increased productivity. And yes, measuring how much gas each employee uses and encouraging to use more is obviously stupid in isolation. But the idea is to force adoption to iron out the kinks and find out where it can improve productivity. It is basically funding a research project.
Despite decades of the industry telling itself that we "pay for performance" or whatever, that has never been the case because we can't really measure performance very well. Where I have seen it done ok (not great, just ok), it was massively labor intensive and did not last, and was only done fully when considering promotion.
So, as you observe, now we have some new technique that managers are sure will increase performance by 50+%, if only people would use it. They can't just raise their expectations of performance by 50%, because they can't measure performance to within 50%! So, they measure the thing they can: token consumption.
I’m all for a trial run, but it needs to be done like any research experiment. With a goal and measurements along the way. Not by going blind and hurting your workers/customers.
The number of times I have been told "oh I talked to so and so and they are having SUCH a good time using X" and then three years later "oh I talked to so and so and they got rid of X as soon as they could, we should switch!"
Not with the same pressure as everyone in the company (literally everyone, regardless of the job role) has to burn AI tokens, and attend forced AI workshops, still it is always running after the next new shinny.
"our scientists were so preoccupied with whether they could, they never stopped to ask if they should.
I've now come to the realization that if I'm having an llm work constantly all day writing code for me i'm probably doing something wrong as I'm no longer focusing on the core issue itself.
I may be in a minority here in that I write code to augment my self and not to ship to others so I can tell very quickly if I'm just gold platting something or if i'm actually delivering real value to my trading or risk management.
https://portfoliocharts.com/2021/12/16/three-secret-ingredie...
The government and everyone with any money/power are fully invested in keeping the market going regardless of any kind of reality.
"Every American child under 18 with a Social Security number can have a federally recognized "Trump Account," a one-time $1,000 IRA seed deposit"
By doing this every citizen will personally have skin in the game and want markets to continue to rise.
1) workforce reduction
2) AI spend (reduce tokenmaxing)
They'll expect fewer people to do more with even less, while "more" is continuously increasing.
When I say "more", I mean that the deluge that engineering teams deal with comes from two sources:
1) the business side of companies - marketing, sales, solutions teams, etc.
2) outside actors, mainly security threats
The first source can now move to generate work for engineering faster than ever. They expect the nerds to do what they're told and get the features out now. The more features, the better the product, right? The saving grace here is that they're bound by the same management concerns that engineering has. There's only so much money that they themselves can throw at generating more work for engineering teams, and that might also come under scrutiny from management, so that acts as a brake.
The second source has no such brake, especially not with security threats. Either there's good money to be made by holding company data hostage, or there's an endless supply of resources (read: nation-state resources) dedicated to the effort to attack the company's digital assets. And of course, they're using AI to enable this, just without the "but what about the shareholders!?" handwringing.
If you aren't very, very careful with your token cutting, you're going to put yourself at a disadvantage against that second group.