I do find it to be true that with coding agents the famous quote from Jurassic Park goes through my head multiple time a day
"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.
This argument is funny because you could have said the same thing 4 years ago: Uber still picks you up just as it did years before that, so what did all those millions spent on developer salaries get them?
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.
There's probably tons of backend projects going on, expanding in countries, payments, complying with regulations, effeciency and reliability projects. They also do food delivery. There's a whole engineering team to support
It feels like maybe the wheels are starting to fall off the AI hype train. I expect complete collapse once people start figuring out that the numbers on all this don’t make sense. I’m looking for investment portfolios that will weather that storm. If you are reading this and have a similar curiosity, this is a great place to start.
A friend of mine added some pretty extensive iOS UI tests to a keystone feature hit by millions every month. They'd been kicking the can down the road for years, trying to fit it in their roadmap, and with Claude running overnight they were able to bang out the whole suite in a week.
I'm not sure how it would show up in quarterly results.
Even if Uber really did double developer productivity, would it translate to 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.
I never took tokenmaxxing to be about improving productivity directly; mundane feature work that comes out of it is just a side effect. I always saw it as a race between these big tech companies to get a generational advantage by being the one to discover the way of the future, with respect to harnessing AI to actually and truly automate software development.
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.
I am not sure how uber is operating internally around the use of tokens but if they actually shipped features faster than before then it is still a win. if they learn that users don't want these features or want a different version of it; they have learned this new knowledge faster than they would have if they manually coded those features, which means in principle you should be able to iterate faster. but this will collide with creative ceiling that humans exhibit in a span of time and on top of that uber is prioritizing spending money on tokens over humans which seems like a mistake. you need humans for creativity.
This is "Feature Factory" thinking, and it is usually not ideal. Feature count is a poor metric to optimize against. Instead, ROI and delivered customer value should be the focus of product development investments.
Anthropic's annualized run rate is >$40b according to outside reporting. AWS hit that by Q4 2019. There were still debates on public cloud vs on prem at that time, but by late 2019 public cloud had facilitated the creation or adoption of entire categories of software within SaaS and PaaS, not to mention consumer internet businesses like Uber and Airbnb. The net impact of AI coding tools is far more ambiguous in comparison.
The profitability comparison is fraught but worth noting that by then AWS was already extremely profitable.
Now feels like a very good time to be a small team of experienced developers who can largely work on stuff by themselves and not a corporation of hundreds of developers of varying abilities all now trying to show how much code they can generate and how many tokens they can burn.
I actually think Chinese models have already popped the bubble, we just dont see it yet. the only way to justify AI IPO market cap is basically if they get to hold most sw industry code hostage and then token-flate budgets to collect AI tax. short of that AI expense would very quickly mean revert to model + some margin. this means the moat for AI 'trillion club' is gone. In fact AI virtually guarantees that there is no execution-moat left anywhere, definitely not in code or that engineer with knowledge about that obscure mechanism. without the moat most of the sw ecossytem's margins would shrink (as they should).
Ironically enough the only moat left would be what you can buy from Washington.
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.
This doesn't account for the fact that review is still a bottleneck, engineers understand much less of the code they're shipping and there'll likely be tech debt they'll have to unwind in the future.
Didn't google say that AI had increased their company's productivity by 10%? if that's the case, then how can they justify spending 50% to 100% of wages on it?
I wish they would spend some of that on the help/support function within their apps. Whenever I have a problem on Uber, it feels like a never-ending maze to figure out how to get any support, and I consider myself versed in navigating unseemly UI, I can only imagine how much it might frustrate people who struggle navigating apps.
Any idea why their help function seems to impenetrable and if AI might help with it?
I wonder how hard would be spend maybe 50% of that budget and build a farm of GPUs to run the models locally. I guess it would be a lot of work, but it would be interesting to see how it compares in terms of performance and cost.
Outside of tech, most companies have already begun pulling back and cancelling their AI spend. (The trend actually started last year.)
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.
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[ 3.4 ms ] story [ 55.1 ms ] thread"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.
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.
https://www.currentmarketvaluation.com/models/s&p500-mean-re...
Where does it show up in quarterly results?
I can’t see how it’s sustainable just based on “this feels more productive”
Standard answer is "companies that are not seeing significant gains from AI just aren't AI-ing hard enough, trust me bro".
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.
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.
The profitability comparison is fraught but worth noting that by then AWS was already extremely profitable.
Ironically enough the only moat left would be what you can buy from Washington.
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.
Nobody's going to jail.
Any idea why their help function seems to impenetrable and if AI might help with it?
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.