Posted by TheEdonian 21 hours ago
>Process blocked on human inputs
Have AI check chat, email, issue tracker and see who it's blocked on and what latest status is. It may not save a huge amount of time but it can dig through the info pretty quick.
>Exploration
Once again, have it scour issue tracker, chat, customer suggestions, product documentation and summarize history and current status. Much quicker than setting up new meetings to try to rediscover and organize existing info.
Another use case, have agent build prototype, hand to people, have AI summarize and integrate feedback.
Claude or ChatGPT + Slack MCP + Jira MCP + Google Docs MCP + internal knowledgebase MCP + gh (GitHub) CLI + Datadog MCP--really 1 MCP per process in the Gantt chart--has been a huge boost at work just digging through context scattered all over the place and summarizing.
That said, it definitely still needs supervision and hand holding along the way
...but yeah most organizational processes & people aren't set up for leveraging it and roll out will be slow (same on learning where it does / doesn't work).
I’m currently working on a data migration for an enormous dataset. I’m writing the tooling in go, which is a language I used to be very familiar with, but that I hadn’t touched in about 12 years when I started this. It definitely helped me get back into go faster.
But after the initial speed up, I found myself in the last 10% takes the other 90% of the time phase. And it definitely took longer for me to wrap my head around the code than it would have if I’d skipped the AI. I might have some overall speed up, but if so it’s on the order of 10-20%. Nothing revolutionary.
I have been able to vibe code a few little one off tools that have made my life a little easier. And I have vibe coded a few iPad games for my kids for car trips, but for work I still have to understand the code and reading code is still harder than writing it.
This is also not from lack of trying , I spent $1000 last week during a company wide “AI week”. Mostly on trying to get AI to replicate my migration tooling, complete with verification agents, testing agents, quality gates, elaborate test harnesses etc…
I’d let Claude (opus 4.7 max effort) crank away overnight only to immediately find that had added some horrible new bug or managed to convince the verification agent that it wasn’t really cheating to pass my quality tests.
What I learned from last week is that we are so far away from not needing to understand the code that everyone who says otherwise is probably full of shit. Other people who I trust who have been running the same experiments have told me the same thing.
Until and unless we get to that point, it’s always going to be a 10-50% speed up (if that).
For many businesses that is revolutionary.
Not sure that's enough magic to make the math work for the trillions being invested, but on a ground level within companies even small wins stack up. You may have burned through $1000 without getting much done, but from a company perspective they've probably got an employee with better instincts as to what does or doesn't work
Where I have a problem is with the FOMO, panic, and mania that has come down from up top. There are people in my company saying that we should be spending 3x our salaries in tokens.
But if you’re in a business where a 20% speed up is revolutionary, there are so many things that have been on the table for years that you could have been focusing on. I’ve seen at least 5 advances over that have happened over the last 20 years with that kind of boost.
That’s probably about you’d get from spending time really learning vim or eMacs.
- People need to be trained to use AI in ways that we don’t call slop, meaning half is made up by the LLM
- To this effect, LLMs should be trained to ask for more input before offering any kind of final output
I am finding that lately I do not allow LLMs to write any code I am interested in maintaining. Or if they do, I have to micromanage them and it usually takes longer. They produce mediocre solutions, and often add redundant state ("Why did you add that state?" "Because we might need it in the future")
That said, they are extremely good at:
- Dev tools: creating debug tooling, debug screens, scripts that get the job done - Auxiliary development: landing pages, "what's new" screens, tedious boilerplate, gathering strings for localization - Prototyping: building full implementations quickly so you can see all the problems rather than having to anticipate them - Pure transformation: porting from one language or paradigm to another
So while I agree with the article that the actual spec of the feature you are building needs just as much human thought, regardless of AI, the speed-ups around that are worth exploring
An example I have from a recent feature development is adding CarPlay support to an existing app. We could have talked about it and designed it for weeks, but with an LLM I was able to get it running in my car in an hour, go for a drive, and feel it to understand whether it was a valuable direction.
The code was a mess, most of it had to be thrown away, and the LLM couldn't even get the initial build functional (not much CarPlay training data, I expect). But it was an accelerator to answer the question "is it worth investing more time in this?"
My hypothesis is that AI greatly accelerates only a small portion of development. It's pretty effing great at prototypes. But the net acceleration factor is just not that large currently.
Opus has been out for ~6 months. If AI were a net 100x multiplier then we'd be seeing what previously took 5 years ship in just 2.6 weeks. This is objectively not happening.
No. AI is used all the way from the very start to the very end and after.
The cost of a subscription is somewhat offset by being guaranteed income regardless of usage, following the financial models of gyms. Whilst api costs represent both the convenience of on-demand pricing and the scale for applications with many users.
Further, the costs of api and subscriptions need to cover the operating costs of the business, the massive SOTA training costs as well as the costs of inference.
The true cost of serving tokens is buried in all of that in these enormous, opaque companies.
Careful who you share this information with- better to roll with the kool-aid drinkers when they're holding the cards.