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The AI Productivity Gap

bjorg.bjornroche.com

61–70 of 127 posts

Re: The AI Productivity Gap

#61
post #49

Earlier quoted context omitted.

Neither of these points feel true anymore. Models are very much predictable these days (except anthropic models). The real issue stems from letting them work on their own for far too long. Also we are not controlled by 2 companies anymore as kimi k3, deepseek flash (and soon pro) as the ultra-cheap variants, glm 5.2 especially is a direct replacement for opus 4.8. Models will only get better and cheaper I wouldn't fe…

How exactly will models get cheaper?

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Re: The AI Productivity Gap

#62

What I have noticed in my own work that a lot of the time that used to be for coding is now just waiting. I have three agents working on three different features in parallel, and I'll go back and forth with all of them, correcting things and steering etc, but then I find myself with three busy agents and nothing to myself except stare at the screen while they code away. There is a mental budget for me where I can't h…

It reminds me of multitabling at online poker. More tables translates into more revenue even if the ROI per table is lower as you don't dedicate as much attention, until it collapses from not being unable to do the right decisions on time and keep tabs on each player. The main difference is, with agents you might be creating (technical) debt.

Re: The AI Productivity Gap

#63
The outwardly measurable productivity increase will come but over a time scale of years, and most of that will be in barely measurable increments. Nobody wants to hear that but it's just how the world works.

Re: The AI Productivity Gap

#64
post #14
post #6

Earlier quoted context omitted.

I stopped using coding agents after more than one and a half year of active use, it really started to become way too boring, and I’m t a point where I just hate having to babysit them and for the 200th time make it understand what the actual goal is… and to be honest, going back to writing code by hand without assistance is really hard at first you continuously have that little voice telling you how simple that would…

I'd be really interested to see all the software that is written by agents. Whenever I touch agents or ai I can't get much use out of them. My understanding is the value when I think aloud with them/treat them as a better google search, but thats about it. Except one off web stuff, that is a pretty neat use case. But lets be real, anything moderately complex that is out of the domain of publicly available sample code…

Idk, ymmv. I’ve had very good outcomes writing embedded firmware for complex systems, drivers, etc. it’s really, really good at not bodging the kinds of things that always catch meaty developers unawares.

OTOH, we run extensive harness optimization, where everything is specified in advance, then a plan is made, then a naive review of the plan vs the specification vs the blast radius, then implementation, tests, then a naive review of test coverage, a naive review of the code vis a vis our code guidelines, a review for smells, a review for silo violation and architecture compliance, a reconciliation of the documentation, then planning the next subfeature, etc. probably 10 percent code generation, 40 percent documentation and planning and adversarial review, 50 percent automated adversarial code review.

We use a Claude for planning and generation, sol for adversarial review. Our metrics say we are about 2x. Productive, at a cost of about $300 per dev per month.

We are also shipping less bugs and better, more clearly written documentation (we use technical writing English style guides implemented by Claude)

I think a big part is the constant adversarial review by a different model with no prior context except the coding standards. Also important is context management, we do an onboarding and wrap-up for each session where we have a batch of continuity documents- Learnings, musings, and roadtrips where we let the most successful high-context sessions research and then write about something that they “ became curious about” during the session. That actually brings in a lot of insight to the team and occasionally is brilliant.

Also critical is crossing compaction barriers (standard re-onboarding protocols, writing transition documents prior to compaction, etc)

Also critical is being able to smell when a session is going off the rails. What we do there is sideline the session, wait for master to advance a bit, then have it do a “4c’s” review of the committed work, blast radius analysis, and remediation. If there’s still useful context left we put it into a project unrelated to its failure context. That salvages the valuable session context without staying in the failure trench.

It’s a lot like herding cats.

Re: The AI Productivity Gap

#65
post #37
post #12

Earlier quoted context omitted.

This has been my observation too. Because I'm chatting it feels like I'm not working, so any output can be "productive" in that context but I'm hyper aware of all the negative time here. Correcting, pushing it back to the prompt, reminding it that it doesn't have full context so do what I told you not what you think, and then verifying it and correcting it (always) seems to take longer than just doing the work myself

You should spend time as much as 50% on harness engineering for you and team to stop that and make the AI steer better.

I find this is the difference between people that 2-4x their productivity and improve their shipping quality vs those that don’t find AI very useful. It’s all about the process, the adversarial tension, and the documentation driving the process. The good code squeezes out of that pressure like juice being pressed from fruit.

Re: The AI Productivity Gap

#66

What I have noticed in my own work that a lot of the time that used to be for coding is now just waiting. I have three agents working on three different features in parallel, and I'll go back and forth with all of them, correcting things and steering etc, but then I find myself with three busy agents and nothing to myself except stare at the screen while they code away. There is a mental budget for me where I can't h…

I find myself in the same situation, baby sitting AI agent, monitoring them. It's like l've become a coordinator.

It’s like herding cats, if you’re doing it right. There should be enough adversarial tension in your process that the majority stays on track, but the parts where you are managing and have strong observability are the points where AI needs supervision to not go wildly off the rails, and where you can cull or redirect pathological sessions.

Re: The AI Productivity Gap

#67

> Reading and Debugging 1.5 1.0 > Code Reviews 0.75 0.75 Since these numbers are made up, I may as well throw my personal anecdote in the ring. I find reading and reviewing far harder with coworkers who are using AI. Tickets contain about 5x as much meaningless junk as they used to, and testing notes - while far more thorough - are often now multiple pages in length. Reviews also contain much more code, people try to…

Your process engineering is lacking. Just throwing AI at existing workflows seldom produces good results. Processes have to be reengineered to benefit from the strengths and cover for the weaknesses of AI systems, with observability at the right inflection points being fundamental to success.

Re: The AI Productivity Gap

#69

Writing code is a small part of everyday's job of a software engineer. The article's table reflects this fairly well. AI compresses implementation time for an individual engineer, but architecture decisions, design reviews, integration, testing, deployment, and production validation remain largely serial activities. If code generation speeds up by 5x while those bottlenecks don't, you've mostly increased the team's w…

Very well said.

Programming has always felt like converting thought-stuff into something that the computer can understand. That's still the same, it's just a different language, and much faster.

My hot take is: if people are saying AI code is bad, yet they're using cutting-edge models like Opus 5, then those same people are writing bad code even without AI.

I'm finding that AI today can write excellent code, as long as you plan, review, and help it along with a little guidance.

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