By all means add tons of quality gates to your SDLC pipeline. But thinking about slowness purely for the sake of slowness will not solve your problems.

- Skills do take up space in the context. The name and description of every skill goes into the system prompt. You can't add unlimited skills without context pressure.

- MCP context spam is less of a big deal now that there is deferred tool loading.

- If you work with the agents long enough you'll run into situations where using bash CLI tools suck and an MCP works better.

- MCP is not dead and has never been dead, it's the right solution in certain cases.

1) the minimum number of employees it takes to maintain the core product

Vs 2) All the employees that it makes sense to hire for revenue and market expansion.

Internet comments usually assume that (1) is the goal. But think of say the sales department. If every salesperson you hire brings in new company revenue that’s greater than their salary + overhead, then why not hire 1000 of them?

There's no scenario where AI goes away completely.

I don't think the "major AI services go away completely" scenario is realistic at all when you look at those companies' revenue and customer demand, but that's a different debate I guess.

There's wildly different reports about whether the cost of just inference (not the training) is expensive or not...

Sam Altman has said “We’re profitable on inference. If we didn’t pay for training, we’d be a very profitable company.”

But a lot of folks are convinced that inference prices are currently being propped up by burning through investor capital?

I think if we look at open source model hosting then it's pretty convincing - Look at say https://openrouter.ai/z-ai/glm-4.7 . There's about 10 different random API providers that are competing on price and they'll serve GLM 4.7 tokens at around $1.50 - $2.50 per output Mtokens. (which by the way is a tenth of the cost of Opus 4.5)

I seriously doubt that all these random services that no one has ever heard of are also being propped up by investor capital. It seems more likely that $1.50 - $2.50 is the "near cost" price.

If that's the actual cost, and considering that the open source models like GLM are still pretty useful when used correctly, then it's pretty clear that AI is here to stay.

I've never worked at a company as large as Google but in my experience things can be a little more optimistic than the post. When earn enough trust with your leadership, such as at the staff/architect level, you'll be able to tell them they are wrong more often and they'll listen. It doesn't have to be a "$50,000 check" every time.

That leads to a very important question - Why doesn't leadership always trust their engineers? And there's a very important answer that isn't mentioned in the blog post - Sometimes the engineers are wrong.

Engineers are extremely good at finding flaws. But not so good at understanding the business perspective. Depending on the greater context there are times where it does make sense to move forward with a flawed idea.

So next time you hear an idea that sounds stupid, take a beat to understand more where the idea is coming from. If you get better at discerning the difference between ideas that are actually fine (despite their flaws), versus ideas that need to die, then you'll earn more trust with your org.

And of course Claude Code has custom slash commands which are also very similar.

Getting a lot of whiplash from all these specifications that are hastily put together and then quickly forgotten.

Yeah I don't know about that, the model providers like OpenAI, Anthropic, etc, literally sell intelligence as a product. And their business model is looking a lot more stable in the long term than all the startups built on top.