:)
https://chrisfrew.in/blog/two-of-my-favorite-mcp-tools-i-use...
IMO this is the best balance of getting agentic work done while having immediate access to anything else you may need with your development process.
Would you be open to providing more details. Would love to hear more, your workflows, etc.
Planing depends on deterministic view of the future. I used to plan (esp annual plans) until about 5 years. Now I scan for trends and prepare myself for different scenarios that can come in the future. Even if you get it approximately right, you stand apart.
For tech trends, I read Simon, Benedict Evans, Mary Meeker etc. Simon is in a better position make these predictions than anyone else having closely analyzed these trends over the last few years.
Here I wrote about my approach: https://www.jjude.com/shape-the-future/
With most other knowledge work, I don't think that is the case. Maybe actuarial or accounting work, but most knowledge work exists at a cross section of function and taste, and the latter isn't an automatically verifiable output.
Any data, verifiability, rules of thumb, tests, etc are being kept secret. You pay for the result, but don't know the means.
* Context is debatable/result isn't always clear: The way to interpret that/argue your case is different (i.e. you are paying for a service, not a product)
* Access to vast training data: Its very unlikely that they will train you and give you data to their practice especially as they are already in a union like structure/accreditation. Its like paying for a binary (a non-decompilable one) without source code (the result) rather than the source and the validation the practitioner used to get there.
* Variability of real world actors: There will be novel interpretations that invalidate the previous one as new context comes along.
* Velocity vs ability to make judgement: As a lawyer I prefer to be paid higher for less velocity since it means less judgement/less liability/less risk overall for myself and the industry. Why would I change that even at an individual level? Less problem of the commons here.
* Tolerance to failure is low: You can't iterate, get feedback and try again until "the tests pass" in a court room unlike "code on a text file". You need to have the right argument the first time. AI/ML generally only works where the end cost of failure is low (i.e can try again and again to iron out error terms/hallucinations). Its also why I'm skeptical AI will do much in the real economy even with robots soon - failure has bigger consequences in the real world ($$$, lives, etc).
* Self employment: There is no tension between say Google shareholders and its employees as per your example - especially for professions where you must trade in your own name. Why would I disrupt myself? The cost I charge is my profit.
TL;DR: Gatekeeping, changing context, and arms race behavior between participants/clients. Unfortunately I do think software, art, videos, translation, etc are unique in that there's numerous examples online and has the property "if I don't like it just re-roll" -> to me RLVR isn't that efficient - it needs volumes of data to build its view. Software sadly for us SWE's is the perfect domain for this; and we as practitioners of it made it that way through things like open source, TDD, etc and giving it away free on public platforms in numerous quantities.
I have to think 3 years from now we will be having the same conversation about robots doing real physical labor.
"This is the worst they will ever be" feels more apt.
With knowledge work being less high-paying, physical labour supply should increase as well, which drops their price. This means it's actually less likely that the advent of LLM will make physical labour more automated.
It was my feeling with robotics that the more challenging aspect will be making them economically viable rather than simply the challenge of the task itself.
Don't get me wrong; I hope that we do see it in physical work as well. There is more value to society there; and consists of work that is risky and/or hard to do - and is usually needed (food, shelter, etc). It also means that the disruption is an "everyone" problem rather than something that just affects those "intellectual" types.
We can now use natural language to instruct computers generate stock photos and illustrations that would take a professional artist a few years ago, discover new molecule shapes, beat the best Go players, build the code for entire applications, or write documents of various shapes and lengths—but painting a wall? An unsurmountable task that requires a human to execute reliably, not even talking about economics.
Software, by its nature, is practically comprehensively digitized, both in its code history as well as requirements.
You can describe that somewhat formally as:
{What your computer can do} intersect {What you want done (consciously or otherwise)}
Well a computer can technically calculate any computuable task that fits in bounded memory, that is an enormous set so its real limitations are its interfaces. In which case it can send packets, make noises, and display images.
How many human desires are things that can be solved with making noises, displaying images, and sending packets? Turns out quite a few but its not everything.
Basically I'm saying we should hope more sorts of physical interfaces come around (like VR and Robotics) so we cover more human desires. Robotics is a really general physical interface (like how ip packets are an extremely general interface) so its pretty promising if it pans out.
Personally, I find it very hard to even articulate what desires I have. I have this feeling that I might be substantially happier if I was just sitting around a campfire eating food and chatting with people instead of enjoying whatever infinite stuff a super intelligent computer and robots could do for me. At least some of the time.
The ability to accurately describe what you want with all constraints managed and with proactive design is the actual skill. Not programming. The day PMs can do that and have LLMs that can code to that, is the day software engineers en masse will disappear. But that day is likely never.
The non-technical people I've ever worked for were hopelessly terrible at attention to detail. They're hiring me primarily for that anyway.
I'm not too worried about my job just yet.
...and the best of them all, OpenCode[1] :)
[1]: https://opencode.ai
I don't see a similar option for ChatGPT Pro. Here's a closed issue: https://github.com/sst/opencode/issues/704
And no it is not AI slop and we don't vibe code. There are a lot of practical aspects of running software and maintaining / improving code that can be done well with AI if you have the right setup. It is hard to formulate what "right" looks like at this stage as we are still iterating on this as well.
However, in our own experiments we can clearly see dramatic increases in automation. I mean we have agents working overnight as we sleep and this is not even pushing the limits. We are now wrapping major changes that will allows us to run AI agents all the time as long as we can afford them.
I can even see most of these materialising in Q1 2026.
Fun times.
Not everything gets accepted. There is a lot of work that is discarded and much more pending verification and acceptance.
Frankly, and I hope I don’t come as alarmist (judge for yourself from my previous comments on Hn and Reddit) we cannot keep up with the output! And a lot of it is actually good and we should incorporate it even partially.
At the moment we are figuring out how to make things more autonomous while we have the safety and guardrails in place.
The biggest issue I see at this stage is how to make sense of it all as I do not believe we have the understanding of what is happening - just the general notion of it.
I truly believe that we will reach the point where ideas matter more than execution, which what I would expect to be the case with more advanced and better applied AI.