Many or even most software engineers are experts in their own codebases though, which means a large proportion of engineers are getting high value out of AI.
What’s not clear to me is: if writing more code per engineer is possible, does that result in fewer engineers or just more software, especially in areas that traditionally got squeezed: UX, testing, DevEx, documentation, etc. Perhaps the bar just gets raised?
Likewise - I think sometimes we ascribe a mythical aura to the concept of “intelligence” because we don’t fully understand it. We should limit that aura to the concept of sentience, because if you can’t call something that can solve complex mathematical and programming problems (amongst many other things) intelligent, the word feels a bit useless.
> The broad consumer reach of ChatGPT creates a powerful distribution channel into the workplace
They mention this line in different forms a couple of times in the article. It’s clear they’re pretty rattled about Anthropic’s momentum in enterprise, I wonder how confident they really are in this rationale.
I really like this too - having the previous plan and implementation in place to create the next plan, but then clearing context once that next plan exists feels like a great way to have exactly the right context at the right time.
I often do follow ups, that would have been short message replies before, as plans, just so I can clear context once it’s ready. I’m hitting the context limit much less often now too.
I suspect it has something to do with a) the average quality of code in open source repos and b) the way the reward signal is applied in RL post-training - does the model face consequences of a brittle implementation for a task?
I wonder if these RL runs can extend over multiple sequential evaluations, where poor design in an early task hampers performance later on, as measured by amount of tokens required to add new functionality without breaking existing functionality.
Maybe. Another potential, more positive, timeline is that semantically ablated content filling everyone’s feeds turns people off, and slowly kills the social feed paradigm.
Politics is accruing and deploying political capital within an organisation - or less abstractly, building relationships and using them.
What you’re describing is a particular form of manipulative and divisive politics which is performed by insecure, desperate or selfish people.
Many engineers are not good at building relationships (the job of coding isn’t optimal for it after all), so painting the people who are good at is as narcissistic may be comforting but isn’t correct.
If LLMs stopped improving today I’m sure you would be correct- as it is I think it’s very hard to predict what the future holds and where the advancements take us.
I don’t see a particularly good reason why LLMs wouldn’t be able to do most programming tasks, with the limitation being our ability to specify the problem sufficiently well.
It seems to me that MCP and Skills are solving 2 different problems and provide solutions that compliment each other quite nicely.
MCP is about integration of external systems and services. Skills are about context management - providing context on demand.
As Simon mentions, one issue with MCP is token use. Skills seem like a straightforward way to manage that problem: just put the MCP tools list inside a skill where they use no tokens until required.
Definitely take that point. But this valuation is perhaps more about how much that traction, brand and data is worth to OpenAI, who cannot buy Copilot. $3bn doesn’t seem so disproportionate in that context especially given the amount of money being attracted to the space.
Just consider what it fundamentally is: a company at the leading edge of a product category that has found absurdly strong technology/use-case fit, and is growing insanely fast.
Looking for a moat in the technology is always a bit of a trap - it’s in the traction, the brand awareness, the user data etc.
Yeah it's very interesting... It appears to lead itself astray: the way it looks at several situational characteristics, gives each a "throw-away" example, only to then mushing all those examples together to make a joke seems to be it's downfall in this particular case.
Also I can't help but think that if it had written out a few example jokes about animals rather than simply "thinking" about jokes, it might have come up with something better
MCP came from Anthropic, A2A from Google so they had big tech backing from day 1.
As a developer, I wouldn’t touch this without confidence I can get gains down the line from interoperability.