We are beset on all sides with companies declaring agentic coding a failure and here you are stating as a matter of fact some teams “thrive” with this probabilistic expensive approach to approximating working code?
All the while concluding with “I have no evidence for any of this”.
Agentic coding is absolutely not a failure. It's just not the 10x that CEOs really wanted it to be.
We learned that some tasks don't really benefit from AI while others do. My team went from 7 people to 2 (went to new teams, no layoffs), and we're doing the same amount if not more work than we used to.
Is it more draining and lacking of focused work? Yes.
Is it more money for the business? Yes.
Friend, you are commenting on a thread where one of the most prominent figures in tech (rightly or wrongly) is saying something did not meet expectations. As far as we can tell this is a man with every incentive to exaggerate and boost these products.
In my world, when something is expensive and doesn’t meet expectations it called a failure. Especially when something has been as hyped, scrutinized, defended and attacked as vibe coding.
Honestly, if you are the director of robotics at a firm I think it’s time you took a cold shower.
I’m sorry, what’s the question? Genuinely not sure.
I’ll rephrase, Zuckerberg would certainly enjoy agentic coding to be a wild success because it means less staff and more products he could fail to create.
Again, Zuck's success criteria don't necessarily align with that of others. In fact, I'd expect his success criteria are substantially different to most.
Consider: Someone who "expects" their bank account to have $100M in it before they turn 30 and "only" gets it to $10M.
From the point of view of a normal sane person, they are experiencing "wild success", and yet at the same time they are definitely "going slower than [they] expected".
Why would Zuckerberg want to boost their AI, they have none to speak of after Llama really? Zuckerberg actually has every expectation to downplay AI so as to save face that Llama failed compared to frontier models.
Who said the layoffs were a success? It's a short term fix (or correction from pandemic hiring) that may still nevertheless have long term consequences.
On the other side of this you have two companies growing revenue literally the fastest ever in any market segment, just for said tech. Somebody is spending this money and thinks it's worth it. Let's check back in six months.
From my experience with daily stand-ups, I think they can be a significant boost to that, too. Though you will absolutely have to wear a manager hat with their estimates and breakdowns, not just fire-and-forget, as they're often as not wildly over-optimistic about task complexity.
Directionally, that's why companies are getting rid of token leaderboards and imposing limits on LLM costs. There's a diminishing marginal return to tokens
There is increase in (not only perceived) value _somewhere_ - IMO depends on the organizational culture. At my work I found going over 100$ on OpenAI/Anthropic's API pricing does not produce any meaningful additional output. It might be much different in different companies.
I am also leading a small team of myself and 2 others and we're getting a lot done. The short lines of communication of being a small group + the power of agents has been great for us.
> Is it more draining and lacking of focused work?
To be completely honest, I’m living life right now. I love programming with my bare hands, but man I’m living just building a gajillion things a mile a minute with LLMs. I then come home and spend hours building stuff for myself using local models. I’ve never been so excited about just building shit, that I sometimes want to pull all nighters because I’ve been in the zone (a for work and at home).
Draining? Sorry… inject that LLM serum right into my veins
Now we just need to find those tasks. I want to believe.
> and we're doing the same amount if not more work than we used to
Zero evidence for this. It's programmers self-reporting their own productivity. (Have we not learned this lesson after 50 years of programming practice?)
Because this companies defined the goal to replace humans with AI what didn't happen. What happened is that the humans can work faster and have more coffee breaks while the AI iterates for the next review and iteration round by that human.
why would companies who build tools for developers say they'd want to replace humans with AI? it was never the goal, it was never stated like that. they said that by the end of 2026 most of the code being output would be generated by LLMs, and is pretty much true.
are we tho? i don't really see anyone going back to planning and coding by hand, that ship has sailed and it's not coming back. people ditching agentic workflows altogether would be failure, people figuring out it's not a miracle tool and has uses for which it's not as productive is correction.
The bit I don't have evidence for is whether or not teaching ICs to be managers would improve how they use agentic AI. I have plenty of evidence for the efficacy and effectiveness of AI itself (although not qualitative or obviously causal unfortunately.)
> plenty of evidence … although not qualitative or obviously causal
Those two things are the opposite of each other (evidence, but only anecdotally; you cant be both).
Anyway.
More tangible to your argument; what is your argument that this will be more effective than just prompt engineering?
Ive long believed that prompt engineering is a losers game; if there is a trivial set of tricks that improve the output, they will simply be automatically applied.
We see this playing out with the system prompts in coding agents and image gen.
The value of learning “photo realistic studio lighting…” was non existent. The nano banana api is capable of taking a naive prompt and expanding it with these tricks.
People who devoted themselves to learning these “magical incantations” wasted their time and effort; and it was obvious, from the beginning this would be true.
Now.
With managing agents; if a trivial set of management tricks can drastically improve the results, why are you better off learning them now, rather than waiting for them to be baked into cursor/codex/claude in easy mode?
What makes you believe this is a valuable investment in time and effort?
Even if we accept that right now assigning personas to agents and managing them as a manager yields good results, the horizon for change right now is so short, it seems extraordinary to suggest mass management and leadership training for engineers.
We should just wait and see.
All in investments like this would just be tokenmaxing in a funny hat.
We are beset on all sides with companies declaring agentic coding a failure and here you are stating as a matter of fact some teams “thrive” with this probabilistic expensive approach to approximating working code?
All the while concluding with “I have no evidence for any of this”.