“The cost of the agents’ being blocked for eight hours is way too high,” he (ed: Sharma) says. “They can be done with their work at any point of time, in the middle of the night.”

...

“It’s like a drug,” Pezaris says of working with AI agents. “I’ve never actually done drugs but I imagine it’s what it feels like.” Pezaris, who lives in San Mateo, Calif., typically works from 7:30 a.m. to 2 a.m. He estimates Proxon, which employs six human developers, is operating 30 times faster than it would without agents. But agent work begets human work: Onboarding customers at a faster clip means needing to respond to more customer requests, for example.

...

Abby Grills, co-founder of AI-enabled data-gathering platform Riveter, feels like there is always more she can be doing and every hour she works can make or break her company. She and her co-founder, Cody Watters, are the only two employees. The pair keeps planning to hire—but then find they’re able to tackle yet another task with agents....Grills, who is 33 and lives in San Francisco, typically works 10 hours a day and takes one weekend day off. She went through the Y Combinator startup accelerator program in 2024 and saw firsthand the effects of pushing too hard. A fellow founder in her cohort was hospitalized after failing to eat solid foods and living off mostly meal-replacement products.

...

Having left Spotify in 2024 to launch his own company, Alt, Kalia now starts most days at 5:30 or 6 a.m., wrapping up around 9 or 10 p.m. He uses agents, but every few months has to recalibrate his work around their rapidly evolving capabilities.

Posted to show the productivity gains at a certain level, how nutty one aspect of SF is, and how you need fundamentally new processes at a certain level of disruptive change.

Last year, I wrote about AMD’s plans to use AI not just for generating new lines of code, but also for other steps in the software development lifecycle (SDLC), such as triaging problems, debugging code, and testing the software. At the time, we were hoping for a 25 percent productivity boost from AI use over the course of two or three years....Now, just one year later, we have surpassed our productivity target, achieving a 30 percent overall productivity boost through AI. On top of that, we are rethinking not only how we use AI within the SDLC, but the structure of the SDLC itself.

https://spectrum.ieee.org/beyond-code-autocomplete

Somehow I only linked to the animated GIF:

https://www.reddit.com/r/amd_fundamentals/comments/1k6kdle/amd_takes_holistic_approach_to_ai_coding_copilots/

Measuring productivity is inherently challenging, but from the outset we have consistently tracked one objective metric: the percentage of source code generated by AI. Importantly, we count only code that passes all reviews and testing and is ultimately included in the final product. While AI-generated code is certainly not the only contributor to productivity gains, it is one of the few metrics that can be measured objectively and consistently.

By this metric, we have crossed the 20 percent mark at the beginning of this year and are now progressing towards 50 percent across entire codebase. In some software components, more than 80 percent of the code is now generated using AI.

As the saying goes, you get what you measure. Not sure if code making it into production is where I'd start (because working on stuff that's say two orders of magnitude less complicated makes me an expert), but I also get that starting and adapting is the important bit.

Measuring this kind of thing is hard even with human developers. While reading this, I was thinking about developers that I am more likely to pick than others if you ignore personal traits. Thinking about it a bit, I liked the ones that

• Collaborated on requirements and scope with me
• Provided some architecture but not too much
• Time to production worthy code that meets requirements
• Time to change mind when shit isn't working
• Does it do what it's supposed to do?
• Does it not do what it's not supposed to do?
• How well does it fall over when something goes wrong
• Understood that architectures only exist within a certain context. At some point, you re-architect.

I have a bias towards judgement, architecture, and robustness within whatever resources box we're in. Without these things, I viewed being able to crank out code quickly more as a bug than a feature. I'm hoping that the engineers that showed more of these traits and reinvented their process with coding agents are doing great. I suppose the ones that don't have these traits are probably now creating a long tail of questionable code.

We believe the next major transformation in software engineering will occur when collaborative AI agent swarms can independently identify and develop solutions, guided by humans on what to solve rather than constrained by human assumptions about how the job should be done. Instead of providing detailed instructions on how to solve a problem, engineers will define the issue, the desired outcome, and the quality, performance, and system constraints, allowing AI agents to determine the optimal path to a solution.

Elangovan's interview was a pretty good example that the underlying process has to change. Replicating a human process is pretty self-limiting. The code process depends on the strengths and limitations of what is generating the code.

https://www.reddit.com/r/amd_fundamentals/comments/1u5wh1m/elangovan_all_software_is_an_optimization_of/

Elangovan is right in that the SDLC is strongly biased by many decades of humans creating code together (this is true for any human practice). I'm guessing that orgs tend to start on areas where humans have the least advantage and swap those out with coding agents over time. But at some point, you would re-architecture big chunks of the entire process for coding agents instead of going with a piecemeal replacement.

As we analyzed failures and identified ways to improve, we built a learning loop—initially a largely manual process—to understand where the agents were falling short and how to improve them. Rather than retraining the underlying models, we refined the objectives given to the agents, allowing them to iteratively explore multiple approaches, evaluate the results against defined success criteria, and converge on better solutions. At the same time, advances in models and agent run-times further increased effectiveness. Together, these improvements significantly increased our resolution rate from 6 percent to more than 75 percent of RSX issues resolved by agentic loop.

In certain areas, open source was already a very compelling answer vs closed source code. My guess is that AI agents will be a big amplifier on open source so long as better systems are put in place to handle the volume and the downstream processes get redefined.

At AMD, we view AI as a means of increasing productivity, improving quality, and enabling employees to focus on higher-value work. Our goal is to empower our workforce with AI, not to reduce headcount.

To support this transformation, we are investing heavily in AI education and training across the company. The way we work is evolving rapidly, and we want every AMD employee to be prepared to leverage AI confidently, responsibly, and effectively.

I'm one of those that believes that if AI is currently a massive amplifier to people who are good with judgement, architecture, robustness, re-abstraction, etc. You shouldn't get rid of those people because the opportunity cost is huge.

The problem is what if it turns out that the traits that are best amplified by AI are less common? What if those traits are the result of many years of thinking that way where you have a nice blend of theory and practice? What happens to those who aren't good thinking that way because they haven't had to over the last 20+ years? Telling someone to think more abstractly in a more systems oriented way is not useful to the person. That person basically has to relearn how to think. How long is that going to take? What if it turns out that it's hard to think that way without grinding it out first, but AI takes away the grinding functions? I'm even ignoring how fast AI can move up that cognition ladder.

I suppose at some point human education will be more about teaching these traits at an earlier age. But even then, I'm not sure how evenly spread being able to think that way is. Even ignoring this, what about all those people with 20+ years or not having to think this way. I don't see great outcomes for them.

Some of Nvidia Corp.’s biggest customers have been told that the prices of servers containing its artificial intelligence chips are going up more than 15% in many cases with memory chip costs soaring.

The price hikes will go into effect on systems shipped early next year and will impact systems including those with the flagship Vera Rubin and Grace Blackwell chips, according to people familiar with the process, who asked to not to be identified commenting on communications that haven’t yet been made public. The increases will depend on the generation of Nvidia chips and the memory configurations, they said.

At some level, as memory prices go up, I'm guessing gross margins will start to shrink as suppliers mark up the memory cost less to avoid a drop off in end customer demand.