> a near-frontier model

Is Kimi K2 near-frontier though? At least when run in an agent harness, and for general coding questions, it seems pretty far from it. I know what the benchmarks say, they always say it's great and close to frontier models, but is this other's impression in practice? Maybe my prompting style works best with GPT-type models, but I'm just not seeing that for the type of engineering work I do, which is fairly typical stuff.

You don't even need to go this expensive. An AMD Ryzen Strix Halo (AI Max+ 395) machine with 128 GiB of unified RAM will set you back about $2500 these days. I can get about 20 tokens/s on Qwen3 Coder Next at an 8 bit quant, or 17 tokens per second on Minimax M2.5 at a 3 bit quant.

Now, these models are a bit weaker, but they're in the realm of Claude Sonnet to Claude Opus 4. 6-12 months behind SOTA on something that's well within a personal hobby budget.

20k for such a setup for a hobbyist? You can leave the somewhat away and go into sub 1% region globally. A kw of power is still 2k/year at least for me, not that I expect it will run continuously but still not negligible if you can do with 100-200 a year on cheap subscriptions.

Horrific comparison point. LLM inference is way more expensive locally for single users than running batch inference at scale in a datacenter on actual GPUs/TPUs.

Reminder to others that $20k is the one time startup cost, and is amortized perhaps 2-4k/year (plus power). That is in the realm of a mere gym membership around me for a family

> a cost that a (somewhat wealthy) hobbyist can afford

$20,000 is a lot to drop on a hobby. We're probably talking less than 10%, maybe less than 5% of all hobbyists could afford that.

If I remember correctly Dario had claimed that AI inference gross profit margins are 40%-50%

>24 tokens/second

this is marketing not reality.

Get a few lines of code and it becomes unusable.

I've faced the same but my conclusion is the opposite.

In the past 6 months, all my code has been written by claude code and gemini cli. I have written code backend, frontend, infrastructure and iOS. Considering my career trajectory all of this was impossible a couple of years ago.

But the technical debt has been enormous. And I'll be honest, my understanding of these technologies hasn't been 'expert' level. I'm 100% sure any experienced dev could go through my code and may think it's a load of crap requiring serious re-architecture.

It works (that's great!) but the 'software engineering' side of things is still subpar.

Code is, I think, rapidly becoming a commodity. It used to be that the code itself was what was valuable (Microsoft MS-DOS vs. the IBM PC hardware). And it has stayed that way for a long time.

FOSS meant that the cost of building on reusable components was nearly zero. Large public clouds meant the cost of running code was negligible. And now the model providers (Anthropic, Google, OpenAI) means that the cost of producing the code is relatively small. When the marginal cost of producing code approaches zero, we start optimizing for all the things around it. Code is now like steel. It's somewhat valuable by itself, but we don't need the town blacksmith to make us things anymore.

What is still valuable is the intuition to know what to build, and when to build it. That's the je ne sais quoi still left in our profession.

Both forces are playing out simultaneously - which is what makes this hard to forecast.

The generalist capability boost is real. I'm shipping things that would have required frontend, backend, and devops specialists two years ago. But a new specialization is quietly emerging alongside that: people who understand how LLM pipelines behave in production.

This is genuinely hard knowledge that doesn't transfer from traditional engineering. Multi-step agent pipelines fail in ways that look nothing like normal software bugs - context contamination between model calls, confidence-correlated hallucinations that vary by model family, retry logic that creates feedback loops in agentic chains. Debugging this requires understanding the statistical behavior of models as much as the code.

My guess: the profession splits more than it unifies. Most developers will use LLMs to be faster generalists on standard work. A smaller group will specialize in building the infrastructure those LLMs run on - model routing, context management, failure isolation, eval pipelines. That second group isn't really a generalist or a traditional specialist. It's something new.

The Fowler article's 'supervisory middle loop' concept hints at this - someone has to monitor what the agents are doing, and that role requires both breadth and a very specific kind of depth.

I’ve become the same way. Instead of specializing in the unique implementations, I’ve leaned more into planning everything out even more completely and writing skills backed by industry standards and other developer’s best practices (also including LOTS of anti-patterns). My work flow has improved dramatically since then, but I do worry that I am not developing the skills to properly _debug_ these implementations, as the skills did most of the work.

IMHO, it doesn't, but I have changed the title to avoid any confusion.

Yeah that editorialized title is entirely wrong for this post. Problem is the real title is "Fragments: February 18" which is no good here either.

I suggest something like "Tidbits from the Thoughtworks Future of Software Development Retreat" (from the first sentence, captures the content reasonably well.)

Tech debt is totally misnamed. 'Tech debt' behaves more like equity than debt: if you project goes nowhere, the 'tech debt' becomes a non-issues.

I like the "cognitive debt" idea outlined here: https://margaretstorey.com/blog/2026/02/09/cognitive-debt/ (from a participant of the retreat) and especially the pithy "velocity without understanding is not sustainable" phrase.

IMHO, it doesn't, but I have changed the title to avoid any confusion.

IMHO, it doesn't, but I have changed the title to avoid any confusion.

There exist a large number of people who are absolutely convinced that LLM providers are all running inference at a loss in order to capture the market and will drive the prices up sky high as soon as everyone is hooked.

I think this is often a mental excuse for continuing to avoid engaging with this tech, in the hope that it will all go away.

Anthropic reduced their gross margin forecast per external reporting (below) to 40%, and have exceeded internal forecasts on inference costs. This does not take into account amortized training costs which are substantial (well over 50% of revenue) and accounted for as occurring below gross profit. If you view training as a cost of staying in the game, then it is justifiable to view it as at least a partially variable cost that should be accounted for in gross margin, particularly given that the models stay on leading edge for only a few months. If that's the case then gross margins are probably minimal, maybe or negative.

https://www.theinformation.com/articles/anthropic-lowers-pro...

I took this to mean more like not jumping right on OpenClaw, but wait a quarter or so to give it at least a little time to shake out. There are so many new tools coming out I think it's beneficial not to be the guinea pig.

I work in a NIS2 regulated sector and I'm not sure we can ever let any AI agent run in anything we do. We have a centralized sollution where people can build their own chatbots with various configurations and cross models. That's in the isolation of the browser though, and while I'm sure employees are putting things into it they shouldn't, at least it's inside our setup and not in whatever chatbot they haven't yet run out of tokens on. Security wise though, I'm not sure how you can meet any form of compliance if you grant AI's access unless you have four eye validation on every single action it takes... which is just never going to happen.

We've experimented with rolling open source models on local hardware, but it's so easy to inject things into them that it's not really going anywhere. It's going to be a massive challenge, because if we don't provide the tools, employees are going to figure out how to do it on their own.

> but prompt injection springs eternal.

Yes, but some are mitigated when discoverd, and some more critical areas need to be isolated from the LLM so taking their time to provision LLM into their lifecycle is important, and they're happy to spend the time doing it right, rather than just throwing the latest edge tech into their system.

I'm not sure you read the article, it's not referring to financials, but tech debt.

1. Singham is not a fugitive from American justice just yet - although refusing to cooperate with Congress may lead him to be.

2. Is it a problem if a rich guy funds activities in America that suspiciously align with a foreign power? That has interesting implications for many pro Israel billionaires and organizations.

3. Only a paranoid MAGA troll would characterize the left wing groups he funds as domestic terrorists. Code Pink? Pro Palestinian protest groups? Come on.