Programming Deflation

This article is really only useful if LLMs are actually able to close the gap from where they are now to where they want to be in a reasonable amount of time. There are plenty of historical examples of technologies where the last few milestones are nearly impossible to achieve: hypersonic/supersonic travel, nuclear waste disposal, curing cancer, error-free language translation, etc. All of which have had periods of great immediate success, but development/research always gets stuck in the mud (sometimes for decades) because the level complexity to complete the race is exponentially higher than it was at the start.

Not saying you should disregard today's AI advancements, I think some level of preparedness is a necessity, but to go all in on the idea that deep learning will power us to true AGI is a gamble. We've dumped billions of dollars and countless hours of research into developing a cancer cure for decades but we still don't have a cure.

> Don’t bother predicting which future we'll get. Build capabilities that thrive in either scenario.

I feel this is a bit like the "don't be poor" advice (I'm being a little mean here maybe, but not too much). Sure, focus on improving understanding & judgement - I don't think anybody really disagrees that having good judgement is a valuable skill, but how do you improve that? That's a lot trickier to answer, and that's the part where most people struggle. We all intuitively understand that good judgement is valuable, but that doesn't make it any easier to make good judgements.

> Will this lead to fewer programmers or more programmers?

> Economics gives us two contradictory answers simultaneously.

> Substitution. The substitution effect says we'll need fewer programmers—machines are replacing human labor.

> Jevons’. Jevons’ paradox predicts that when something becomes cheaper, demand increases as the cheaper good is economically viable in a wider variety of cases.

The answer is a little more nuanced. Assuming the above, the economy will demand fewer programmers for the previous set of demanded programs.

However. The set of demanded programs will likely evolve. So to over-simplify it absurdly: if before we needed 10 programmers to write different fibonacci generators, now we'll need 1 to write those and 9 to write more complicated stuff.

Additionally, the total number of people doing "programming" may go up or down.

My intuition is that the total number will increase but that the programs we write will be substantially different.

I think this is a bit like attempting your own plumbing. Knowledge was never the barrier to entry nor was getting your code to compile. It just means more laypeople can add "programming" to their DIY project skills.

Maybe a few of them will pursue it further, but most won't. People don't like hard labor or higher-level planning.

Long term, software engineering will have to be more tightly regulated like the rest of engineering.

Literally all new products nowadays come with a great degree of software and hardware. Whether they are a SaaS or a kitchen product.

Programming will still exist, it will be just different. Programming has changed a lot of times before as well. I don't think this time is different.

If programming became suddenly too easy to iterate upon, people would be building new competitors to SAP, Salesforce, Shopify and other solutions overnight, but you rarely see any good competitor coming around.

The necessary involvement behind understanding your customers needs, iterating on it between product and tech is not to be underestimated. AI doesn't help with that at all, at maximum is a marginal iteration improvement.

Knowing what to build has been for a long time the real challenge.