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- Tags: AI programación revolución
[!summary]AI is changing programming fast, making it easier to build software by giving clear instructions to intelligent tools. This means writing code by hand is less needed, but understanding problems and guiding AI is more important. We should accept this change, learn to use AI well, and prepare society to support those affected by job shifts.
Highlights
id979738306
In 2020 I left my job in order to write a novel about AI, universal basic income, a society that adapted to the automation of work facing many challenges. At the very end of 2024 I opened a YouTube channel focused on AI, its use in coding tasks, its potential social and economical effects. But while I recognized what was going to happen very early, I thought that we had more time before programming would be completely reshaped, at least a few years. I no longer believe this is the case. Recently, state of the art LLMs are able to complete large subtasks or medium size projects alone, almost unassisted, given a good set of hints about what the end result should be. The degree of success you’ll get is related to the kind of programming you do (the more isolated, and the more textually representable, the better: system programming is particularly apt), and to your ability to create a mental representation of the problem to communicate to the LLM. But, in general, it is now clear that for most projects, writing the code yourself is no longer sensible, if not to have fun.
En consecuencia, muchísimas personas podrán desarrollar apps. Qué implica esto para el emprendimiento, la consultoría, la educación?
id979738531
It is simply impossible not to see the reality of what is happening. Writing code is no longer needed for the most part. It is now a lot more interesting to understand what to do, and how to do it (and, about this second part, LLMs are great partners, too).
Estas son las competencias que va a ser necesario desarrollar para triunfar en un mundo post AI.
id979738783
democratizing code, systems, knowledge. LLMs are going to help us to write better software, faster, and will allow small teams to have a chance to compete with bigger companies. The same thing open source software did in the 90s.
id979751304
this technology is far too important to be in the hands of a few companies. For now, you can do the pre-training better or not, you can do reinforcement learning in a much more effective way than others, but the open models, especially the ones produced in China, continue to compete (even if they are behind) with frontier models of closed labs. There is a sufficient democratization of AI, so far, even if imperfect. But: it is absolutely not obvious that it will be like that forever. I’m scared about the centralization. At the same time, I believe neural networks, at scale, are simply able to do incredible things, and that there is not enough “magic” inside current frontier AI for the other labs and teams not to catch up (otherwise it would be very hard to explain, for instance, why OpenAI, Anthropic and Google are so near in their results, for years now).
Sobre el riesgo de la centralización de la capacidad para desplegar IA en unas cuantas compañías.
id979751488
I have a single suggestion for you, my friend. Whatever you believe about what the Right Thing should be, you can’t control it by refusing what is happening right now. Skipping AI is not going to help you or your career. Think about it. Test these new tools, with care, with weeks of work, not in a five minutes test where you can just reinforce your own beliefs. Find a way to multiply yourself, and if it does not work for you, try again every few months.
Similar a lo que propone Mollick, pero aquí para explorar la capacidad de aumentar el apalancamiento del trabajo a través de agentes de IA.