Definitely not unique. Thats said, given how "easy" it is to scale LLM output, compared to human output, this pattern could be messier in the LLM era? The whole tokenmaxxing motion assumed more output equals more outcomes. What do you think?
> So this is inherent to the technology. No amount of tokenmaxxing is going to change it. LLM development even breaks common and well accepted quality norms in software development - like backwards compatibility. You literally can’t (and wouldn’t want!) an LLM to do the same thing in the same way twice. But this means, LLMs - on their own - are not a solid foundation to build a revolution on. They never can be.
I thought this was the weakest argument in the article. Yes, non-determinism is inherent to LLMs. It's also inherent to human brains, yet somehow we still manage to call (some) people reliable.
The bit about backwards compatibility doesn't make sense either. LLMs, just like humans, can take backwards compatibility into consideration — or not. Sometimes they'll forget, and sometimes they'll fixate on it even in a system that hasn't been deployed yet. It has nothing to do with LLMs never doing "the same thing the same way twice".
LLMs may or may not become as reliable as other productivity-improving infrastructure, but it's not the nature of their technology that will decide this.
Yeah I have a different take. We will end up scaling human output by hiring more devs as wages for entry to mid level continue to stagnate.
I think LLMs make finding information and learning way more accessible. Even with realistic expectations it already is a revolution for literacy, education, and search. LLMs are a massive achievement that would be celebrated appropriately if only the public wasn't introduced to them during global political/economic crises encouraging grifters and authoritarians muddying the waters.
To be clear, hiring at scale is a sorely needed step. Software is just like any other form of writing. It carries weight and needs cultural and community context to work. We have needed way more technical literacy for decades to make this digital always-on world work. I think most would at least agree that LLMs bridge knowledge gaps. They're a net good thing despite the current abuse by bad actors.
Looking forward to that hiring. So far, just layoffs. That said, there reports that consulting firms are hiring to fuel the demand around AI adoption itself. LLMs are a breakthrough like machines were. We will create new business models around them, and perhaps drives more humans. Wrt software, LLMs can make learning very easy. But with all the AI generated code, who is the "architect"?
Given the last few years, I don't see why traditional software and app dev jobs would be impacted. The current architect is still the architect.
New jobs are new jobs. The catch is these new jobs that lean heavily on AI will be a race to the bottom regarding pay. There are so many people with their own special interests that thus far never had a chance to contribute without coding skills. I don't mean that in a sinister way, but it makes total sense that we'd continue to see the rest of society find their way into the decision making. Right now we have a ton of lower priority stuff that never gets implemented because it's not part of the MVP. We also have codebases not as modular and robust as they should be behaving as platforms.
It also makes total sense this is a nightmare for those people who were most loudly proclaiming we'd replace white collar jobs. They showed their cards too early and will have the mob coming after them.
Code is meant to be shared and collaborated on. That was always where the value came from. There's a lot of untapped potential when truly anyone can write their plugins and build communities. This is really is like the early internet era repeating itself, but reaching way deeper this time.
On September 8, 2025, NPM reported that several libraries were compromised in a user-side man-in-the-middle attack. This research dives into the details about how the attack works.
Data streaming emerged as a new software category. It complements traditional middleware, data warehouse, and data lakes. Apache Kafka became the de facto standard. New players enter the market because of Kafka’s success. One of those is Redpanda, a lightweight Kafka-compatible C++ implementation. This blog post explores the differences between Apache Kafka and Redpanda, when to choose which framework, and how the Kafka ecosystem, licensing, and community adoption impact a proper evaluation.
is your thesis available to read? Jepsen and these concepts for databases are certainly very different from those for kafa, as we heard from Kyle during the Jepsen testing for Redpanda. Someone needs to write about those perceptions!
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