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.
For the product my friend works on, it's definitely the latter. I definitely don't expect this party to last forever.
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.
> 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.
Sometimes things are actually just finished. They don't need to treadmill.
"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.
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.
* 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.
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.
https://portfoliocharts.com/2021/12/16/three-secret-ingredie...
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.
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.