At one point my boss asked why my AI usage was lower than other team members. I instantly knew what to do. Every session is now run at ultracode effort. My automated PR review bot averages like $80 in usage per PR review.
Oh and my favorite: Use 5 independent subagents to review code change and summarize the findings, and for any finding determine if they are real concerns
-Claude, burning my company's money.
Eg by doing that I was able to develop non-essential features which increased our quality of life for devs last month without going through our PO who'd need to price it - because that does let's you create changes in an incredibly hands off manner with miniscule amount of time investment if you already know what you want to achieve, and how the end result should be...
Admittedly, that's a pretty narrow usecase which is rarely the case- but if it is...
It's interesting that LLM barely had any vetting period or experimentation phase. Suddenly everyone was supposed to test it in production, it seems.
There's so, so much mechanically simple but time consuming refactoring that should be done but nobody ever does that because there's never enough free time. Or even various utility scripts and at least finding out of date docs (or writing very basic ones where none exist, though it'd be hard to get them not to feel like slop writing). Or figuring out what additional custom linter rules would be useful, how to improve the CI pipelines and so on.
If I had the Anthropic Max 20x subscription, I could make a large part of the technical backlog disappear (relatively safely).
They can do what works, or they can fail. Large enough companies with enough inertia can do really dumb things for a while, but even giants fall.
Are you saying companies have to mandate AI everywhere?
Or are you saying the exact opposite, as your second sentence suggests?
I haven't heard of AI mandates in small companies, only in big ones.
That is essentially not an argument in any direction.
Quoting the host of the recurring Quiz Broadcast sketch from That Mitchell and Webb Look: "Books mention 'hope'. What was 'hope'?"
We still need the humans, there are no cases for novel useful work I can think of, or have seen, where humans are no longer required.
Some people have not learned that velocity at small scale without global synchronisation is just thermal agitation.
Unfortunately the only phone line was answered by an AI bot who stubbornly refused to move the booking, simply telling us there was no availability within an hour of our booking.
Fortunately my partner was passing so was able to go in and speak to someone is person who was happy to move our booking back 2 hours. Lunch and drinks for our party must have come to several hundred pounds.
I'd estimate our party was between a third or maybe half of all the customers there. Had we chosen to book elsewhere I bet someone would still be patting themselves on the back about how clever they were to save a few minutes a day on actually answering the phone to actual customers.
Value is measure in generalized labour, since that the universal measure of human effort. The genealized amount of time a human being must spend to produce something from its parts. Generalized labour is also what's bought from labourers. You don't pay them to do something specific, you pay them to labour in general.
This contrasts against specific labour, which is whats actually required in the moment. Generalized labour power must be the right kind of specific labour to actually produce anything of value.
The AI leaders have been told that AI is labour. To the extent that it currently is, which I believe is only the case because the market hasn't adjusted, it's not the right specific labour to male anything valuable.
You just have to get the input coefficient right. The least amount of acceptable quality with the least amount of costs is the sweet spot. /s
Ford has hired 350 engineers over the last 3 years which happened alongside short comings in using AI inspection tooling. This has nothing to do with LLMs and instead is almost certainly about their MAIVIS and AiTriz pilots, which use old school CNNs on custom IBM hardware to do visual inspections.
Dirt bag media will do anything for your clicks and leave you more uninformed at the other end.
After a quick search I found a publication actually mentioning about these tools:
Ford previously told Business Insider that it had developed two bespoke AI-enhanced scanning tools that helped validate that cars were properly assembled before rolling off the lot. The tools, called AiTriz and MAIVs, both debuted in 2024. https://autos.yahoo.com/policy-and-environment/articles/ford...
And after doing cursory research on these tools, it is clear they are rudimentary (as compared to SOTA LLMs), they were essentially smartphone mounted on stands and doing visual checks using the camera - so OP could be very right.
https://www.businessinsider.com/ford-uses-ai-cameras-in-fact...
Where does this article say otherwise?
The defining motto of the corporate world
(most of them are for fairly innocuous stuff...)
Few of the issues I've experienced with the car were clearly tied to quality issues: 1) Battery died a few times, but maybe that was user error 2) squirrels/rats nibbled the engine cable harness, a not-uncommon occurrence in our area. Only 3) auto-unlock on passenger side being unreliable is clearly a quality/design issue.
Honestly, I actually love the Escape. The pedal feel is very responsive in all driving modes, compared in particular to the 2020 Hybrid Rav4, which felt like driving a boat (maybe I didn't find the drive mode?), or the 2020 VW Tiguan which had a shockingly slow automatic transmission for an ostensibly "sporty" vehicle. And I'm not even a car guy. I also love its actual buttons on the dashboard, instead of the idiotic "everything on a huge touchscreen" that too many cars do nowadays.
The fact that you find this acceptable is amazing to me.
Sounds like a complete failure of quality control.
They still don't have a solution to the problem. The shavings amount/size is supposedly common among all engine manufacturing processes, but the new engine design has such tight tolerances that it's now problematic.
None of the US automakers have good quality reputations. If you want something that works reliably, get a Toyota.
Be sure to have “updated your rate schedule” recently, which explains why you’re now twice as expensive as before.
They know how bad they screwed up and how bad they need you now. I’ve never had anybody refuse a giant rate bump now that we were all on the same page.
While it was significantly better than previous attempts, it still misses very basic things - sporadically. Eg. A clear design requirement was essentially adding clients, explained clearly and comprehensively. The ability to add clients was entirely missed in the build and iteration (there were multiple 'please check its all done' separate agent runs/checks).
I can imagine in a fully autonomous deployment, in even moderate complexity, even to this day would still occasionally mess up - badly enough to cause non-trivial business issues.
I haven't managed to really figure out what's the best way, but my latest thinking is really having boil down tasks to almost unit operations "add UI button, wire to Api call. End".
You could ask it to go through the spec point by point and then mark what is done and WHERE/WHY, then it'd point you towards exactly what might be missing.
The more interesting question, I think, is what proportion of businesses will choose the learn from Ford’s experience without first choosing to relive it?
Often people, and therefore also organisations, struggle to usefully learn from the experience of others without repeating the same mistakes, and experiencing the same pain.
With self driving, one imagines ai will scale the self-driving fleet up and down on demand, ordering new cars from 3rd parties by the hour during peak demand.
Cars are more and more becoming white goods appliances with the driving experience becoming less and less a priority. Even enthusiast cars now are about raw numbers and need electronics to reign them in to make useable for the average driver on the average road.
The average user probably doesn’t even want to drive and have AI do it for them.
Repairability is becoming less viable as mechanical parts replaced with screens and digital locks. Parts availability is already an issue, only going to get worse especially with the pace of new cars are being churned out from China.
The end will be car as a subscription. We already have it with leasing, and BMW having to pay to use your electric seats.
Pardon me?
We're living in the dystopian present, where most everyone has a car or several. Cities are crowded with cars -- both moving and parked -- and it's awful for humans who aren't cars.
I can't wait for the moment people switch to a subscription and the cars are shared and drive themselves. The streets will be just as full of moving cars, but at least the parked cars hopefully disappear, giving us more space for trees or sidewalks or anything but cars really.
* Their "not owning" means a swap to a subscription/license for the car, which could still be exclusive rather than shared.
* Your "not owning" assumes a reduction in the number of cars per capita.
In other words, the "dystopia" they are referring to is one that still has today's problems of gridlock, land use, urban planning, etc., with new kinds of problems layered on. Cars not being user-repairable, being nickel-and-dimed on features, a monopolistic used-parts market, and a general shift towards whatever boosts the car-manufacturer's profit margin.
I see no reason to assume that this would lead to the disappearance of parked cars or to more trees. Our corporate overlords will want to make use of that space for more cars or infrastructure to support the new car network, why would they ever just give it back willingly?
Just two days ago at work a call of 15+ people spent a non-trivial amount of time recounting the scars of colleagues being laid off, or they themselves having to sign severance papers, only to be saved in the final hours. These events happened 10-15 years ago and they still cost the company time a decade later, not to mention that trust that erodes with these events.
If companies want people to focus on work, those people need to feel secure in their jobs. Laying them off and hiring them back is not job security. It’s a signal that management has no idea what they’re doing. Why would these people follow the leadership of those who can’t even solve the issue of staffing without making a mess of it?
It’s also bad when seemingly competent employees are laid off while incompetent ones stick around. It sends a signal that it doesn’t matter what you do, so why try.
Our AI sucked but that doesn't mean less AI. We need better AI, not humans.
Reminds me of this disaster at Toyota,
https://www.wsj.com/business/autos/toyota-bet-technology-wov...