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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.
Feels like it would be better to spend just enough so that you have the capacity to scale up IFF LLM's end up being a big deal. You spent less than your rivals (who were competing for a supremacy that never came), but you have saved more "dry powder" to compete against them for the more likely future. The only future you exclude with this strategy is the "LLM Supremacy" future, which you only had a 1/(number_of_players) chance of winning anyway. :]
I think the real reason for the spending is that the scent of LLMs in the air causes stock values to go up. And even if everyone knows it does not make sense, they still want "NUMBER GO UP", and so they will spend more money to excite the amateur and professional investor class.
It’s not that they should “scale back” their use as much as the metric should be improvement/tokens. Tokens used is a denominator in any worth calculation.
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.
So doing something (action) creates something new (more information), and iterating on that new information leads to the realization there is nothing new left to be learned with that information (enlightenment). Is how I'm interpreting that.
The AI equivalent of the PC revolution isn’t quite here yet, but it’s the only way forward.
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.
There are privacy and general de-centralization reasons to prefer this outcome, even though most AI and cloud-first tech companies don't want this.
How long it will take us to get this point is a different matter.
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.
For under-specified tasks, it's not really accurate to talk about "correctness," because the machine isn't psychic. I would suggest that given a high-level feature request like "add streaming support" it's more about acceptance probability. In a well-structured and well-documented codebase, and a reasonably sized feature request, there might be an 80% chance it will generate something which is 100% acceptable. But there's about a 99% chance it will generate something which is acceptable after 1-2 revisions.
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.
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.
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.
Using AI requires skills to know how to use it, particularly agents then it requires the time to build an improved way of doing things.
I think of it like giving someone excel in the beginning and expecting they know how to use it, when the rest of their team doesn't, they don't have the skills to know how to use it and how it can benefit them.
For the product my friend works on, it's definitely the latter. I definitely don't expect this party to last forever.
When you try to replace your entire brain with AI things are going to go wrong.
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.
Topline growth matters more than costs.
Mathematically - There is no limit to topline growth But cost cutting has a ceiling, which is the current costs.
You can only make(save) so much money with cost cutting
Topline growth is upper bound by total energy in the universe though.
Standard answer is "companies that are not seeing significant gains from AI just aren't AI-ing hard enough, trust me bro".
The profitability comparison is fraught but worth noting that by then AWS was already extremely profitable.
It wasn't. The retail business took years to move to AWS. They could not even be described as early adopters of AWS.
— CEO of Anthropic, the employer of over 2000 developers, over 6 months ago
AWS has so many analogs. It’s not as novel. Renting vs buying a home/car/anything is essentially what AWS brought.
If Uber is tracking the second number, I'd love to know what they're seeing. If they're not, the spend will obviously feel unjustifiable, every line item does until you connect it to a unit of work.
Any idea why their help function seems to impenetrable and if AI might help with it?
If you can't tell, it frustrates me so much. I wonder how the internal culture of Uber changed when it went from almost zero interest rates to now trying to make lots of profit.
My friend said he realized Uber can just rely on a steady stream of people either growing up or getting laid off and trying to make a quick buck, so they can treat their drivers poorly as well.
I'm not sure what's happening, just know support may be a lot simpler and cheaper to address than nothing, or at least in the medium to long term, but maybe not?
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.
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.
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.
We are seeing shoe companies pivot to AI. They didn’t do that with Hadoop or NoSQL.
[1] Even some homelab folks sometimes go straight to kubernetes even though it’s technically overkill.
"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.
Ironically enough the only moat left would be what you can buy from Washington.
We have to wait a whole year (sigh) since firms generally wait a whole financial year to do critical reviews
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.
Most of the time, the tasks that AI would take over aren't the bottleneck in the business process, so having AI do something faster isn't very useful. It's definitely not useful enough to justify spending more than two digits a month on a recurring subscription, but the price point at which AI is a viable product is far below the price point necessary to sustain even a single AI company.
Nobody's going to jail.
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.