If you want to fully understand / contribute to / fully leverage the most powerful technology humanity has ever devised (AI), you must learn to read and write code. That’s the only reason anyone should need.
This is the way. But also - keep your head up - your mindset matters more than you think. If you consider yourself a victim of your circumstances with no agency, this will be harder for you. So get your mentality right. Something along the lines of 'this is an exciting new era and I'm well prepared to master this new domain like I have many times before'.
The way I think of it has evolved a lot over the last 5 years. At this point I think human brains probably do something analogous to next token prediction when we think. For all the hype, I think LLMs are actually more, not less, intelligent than that average person realizes. I think it’s legit, actual intelligence, not just “artificial” intelligence. That may be a hot take but it’s just my perception.
IDK if the author's 'metacognition' needs to be a feature of the LLM itself.
I could imagine a harness that 1) reads LLM output 2) uses a research sub-agent to attempt to verify any factual claims 2) rephrase the main agent's output such that it conveys uncertainty if the factual claim cannot be independently verified
No, I'm not considering a career change. But my career will change.
I started off as a javacript developer. Then I was a full stack SWE. Then I was an Applied AI Engineer. I think enterprises will have a need for folks with technical expertise to deliver value - often new software - for a long time.
Until an enterprise like capital one can operate without anyone in the organization knowing how any of their technical infrastructure works (never?) I expect I'll be able to find work.
This article forgot the strongest argument for hand writing your code. When the process is done, you understand it at a depth far beyond any vibe coder who created the same thing
To answer your question directly: yes I am bored of talking about AI. I think it’s funny how the folks who are screaming most loudly about their AI expertise typically have not built anything of value with it. They are so focused on the tool they have forgotten that the output is what mattered all along
Are you telling me juniors aren’t facing hard problems anymore? I doubt that’s the case. They’re probably banging their heads against the wall trying to understand why this or that doesn’t work… just as they always had before. AI isn’t a magical wand that makes everything just “work”. Probably never will be.
Never - data centers will always offer more power if you only care about raw inference speed. HOWEVER I think that we'll reach the 'good enough' bar super soon. In 2-3 years I expect apple macs to be able to run a model as 'good' as Claude 4.6 sonnet at 90% of the inference speed we're used to from a cloud API.
Yes, I'm sure by then there will be better models on offer via cloud providers, but idk if I'll even care. I'm not doing science / research or complex mathematical proofs, I just want a model good enough to vibe code personal projects for fun. So I think at that point I'll stop being a OpenAI / Anthropic customer.
At the end of the day you need humans who understand the business critical (or safety critical) systems that underpin the enterprise.
Someone needs to be held accountable when things go wrong. Someone needs to be able to explain to the CEO why this or that is impossible.
If you want to have AI generate all the code for your business critical software, fine, but you better make sure you understand it well. Sometimes the fastest path to deep understanding is just coding things out yourself - so be it.
This is why the truly critical software doesn’t get developed much faster when AI tools are introduced. The bottleneck isn’t how fast the code can be created, it’s how fast humans can construct their understanding before they put their careers on the line by deploying it.
Ofc… this doesn’t apply to prototypes, hackathons, POCs, etc. for those “low stakes” projects, vibe code away, if you wish.
No matter how fast and accurately your AI apps can spit out code (or PowerPoints, or excel spreadsheets, or business plans, etc) you will still need humans to understand how stuff works. If it’s truly business critical software, you can’t get around the fact that humans need to deeply understand how and why it works, in case something goes wrong and they need to explain to the CEO what happened.
Even in a world where the software is 100% written by AI in 1 millisecond by a country of geniuses in a data center, humans still need to have their hands firmly on the wheel if they won’t want to risk their businesses well being. That means taking the time to understand what the AI put together. That will be the bottleneck regardless of how fast and smart AI is. Because unless the CEO wants to be held accountable for what the AI builds and deploys, humans will need to be there to take the responsibility for its output.