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That's cool! You are definitely more on the cutting edge side of things, so props to you. Qwen Code 3 definitely seems like _the_ workhorse local model. I hear so many good things from people using it specifically for task-level coding. I can run it on my M3 Macbook Pro at a decent tok/sec, but not without spinning my fans until it sounds like a 747. I've considered getting a local server with some beefy GPUs for local inference, but I am waiting to see what the new Mac Studio looks like when it is announced later this year.
A lot of orgs are cutting tokens back now, however the smarter ones are looking into open models like GLM 5.2 as a way to cut costs.

When do you think your company will be ready to replace TCS and other such vendors completely with AI agents?
It's possible that the answer is never - since having a vendor is also about _accountability_. AI agents are not going to be accountable for their work, and Anthropic & OpenAI are not going to accept responsibility either.
So, while AI is going to be transformative in both the quality and quantity of outcomes, and even in the way workflows and processes are designed, IT services vendors are likely to always be involved somehow. The question is, how much of the entire value chain can the IT services companies capture.
Note that right now, the only players who are actually capturing significant value are only semiconductor/hardware companies: not even Anthropic and OpenAI are having even positive margins, let alone anything close to what TCS, etc. have historically captured.
So what will the situation look like once this space matures? It is possible that the model layer becomes commoditized - even today open weight models such as Deepseek and GLM are nearly as good in absolute terms and much better in terms of outcomes-per-dollar than the frontier models. Hardware suppliers may continue to capture most of the value, at least as long as China is not able to crack EUV tech. But it's possible that the applications layer and services layer will manage to capture significant value too.

[https://x.com/glocalinvestor/status/2073093608584917276](https://x.com/glocalinvestor/status/2073093608584917276)
Is the AI infrastructure trade running out of steam?
**JPMorgan:** Data Center Watch report says **not even close.**

Worth bookmarking if you're tracking the AI capex debate.

Token usage, GPU leasing rates, and DRAM prices continue to rise. JPM noted in its latest 'Data Center Watch' report that large model usage continues to expand rapidly, token spending has reaccelerated, GPU leasing prices in the non-hyperscale cloud market are still rising, and DRAM spot prices remain strong.

**> LLM token :** June volume +70% MoM (vs May's 33%, April's 5%). YoY growth hit 20x, above May's 12x and April's 15x. Token spending also rebounded, +70% MoM and 16x YoY, snapping the prior two months' slowdown.

**> Unit economics:** Token usage and revenue are diverging. Falling model prices haven't dented market revenue - price erosion is slowing while usage growth outruns the cuts. This is the number that decides if AI commercialization actually works.

\> **Country wars:** US models (OpenAI, Anthropic, Google, xAI) fell to 35% of OpenRouter token share, down from 46% in May and 56% in April - even as their volume grew 30% MoM and 8x YoY. Chinese/low-cost models (DeepSeek, MiniMax, MiMo, GLM) are eating share in cost-sensitive use cases: dev workflows, startups, agent coding.

\> **Rental prices:** A100 at $1.63/GPU-hour (+6.3% MoM, 5th straight monthly rise). H100 at $2.72 (+3.7% MoM, 7th straight month up). B200 at $5.33 (+2.7% MoM).

**> Memory:** AI server DRAM demand is pulling supply from conventional DRAM. Three straight months of modest price declines suggest NAND tightness is easing - but prices are still up 5x+ YoY, so the industry hasn't hit supply abundance yet.

Which number surprises you more: token growth reaccelerating to 20x YoY, or GPU rental rates still climbing after 5-7 straight monthly increases?

Repost this if you're tired of the "AI capex is slowing" take this report has the actual numbers on it.
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Ed Zitron needs to see this.

GLM 5.2, deepseek v4 … 90% as good as opus max and 1/100th the price

I used Claude Code all the way up until March. Right now, I use Opencode with GLM 5.2. It's not everything Claude Code was, but it's like 90% there for 25% of the cost. Similarly, I have coworkers that have switched to Codex since it uses less tokens.
Chinese open models show that there's nothing that special with these closed models since they can catch up within 6 months. Harnesses matter a lot more than you think. How they are optimized means how accurate your responses are and how many tokens you actually use. It's also the only way to get consistent outputs. A few years ago, using only a simple chat interface if you gave the model the same prompt twice you could get 2 different answers. Harnesses are able to filter out the garbage and get you the kind of output you need for work.
Let's also be honest here. LLMs are dumb as hell. These models are trained on the entirety of the combined knowledge of the human race, and they come out only about as intelligent as a college graduate. They require millions of samples just to begin to be able to recognize whatever you're training for.