What could have been

>There isn’t a single day where I don’t have to deal with software that’s broken but no one cares to fix

Since when does this have anything to do with AI? Commercial/enterprise software has always been this way. If it's not going to cost the company in some measurable way issues can get ignored for years. This kind of stuff was occurring before the internet exists. It boomed with the massive growth of personal computers. It continues to today.

GenAI has almost nothing to do with it.

I wonder about the world where, instead of investing in AI, everyone invested in API.

Like, surfacing APIs, fostering interoperability... I don't want an AI agent, but I might be interested in an agent operating with fixed rules, and with a limited set of capabilities.

Instead we're trying to train systems to move a mouse in a browser and praying it doesn't accidentally send 60 pairs of shoes to a random address in Topeka.

> What could have been if instead of spending so much energy and resources on developing “AI features” we focused on making our existing technology better?

This is a bit like the question "what if we spent our time developing technology to help people rather than developing weapons for war?"

The answer is that, the only reason you were able to get so many people working on the same thing at once, was because of the pressing need at hand (that "need" could be real or merely perceived). Without that, everyone would have their own various ideas about what projects are the best use of their time, and would be progressing in much smaller steps in a bunch of different directions.

To put it another way - instead of building the Great Pyramids, those thousands of workers (likely slaves) could have all individually spent that time building homes for their families. But, those homes wouldn't still be around and remembered millenia later.

I've been using an app recently that added a bunch of AI features, but the basic search is still slow and often doesn't work. Every time I open it, I kind of brace myself, but it still disappoints me.

It feels like more and more products are focused on looking impressive, when all I really want is for the everyday features to just work well.

More less you could say similar things about most of the crypto space too. I think maybe it's because we're at the point where a lot of things that tech can do, it's more than capable of doing, but they're just not easy to do out of a dorm room and without a lot of domain knowledge.

Humans are fundamentally irrational. Not devoid of rationality, but not limited by it. Many social phenomena are downstream from that fact.

Humans have fashions. If something is considered cool, many people start doing that thing, because it automatically gives them a bit of appreciation from most other people. It is often rational to follow a fashion and reap the social benefits it brings.

People are bad at estimating probabilities. They heavily discount the future, and want everything now, hence FOMO. At the same time, they tend to believe in glowing future prospects uncritically, because it helps build social cohesion and power structures.

This is why fads periodically flush all over our industry, and our society, and the whole civilization. And again, it becomes rational to follow the trend and ride the wave. Say the magic word (OOP, XML, Agile, Social, Mobile, Cloud, SaaS, ML, more to come), and it becomes easier to get a job, press coverage, conference invites, investments.

Then the bubble deflates, the useful parts remain (often quite a bit), the fascination, hype, attention, and FOMO find a new worthy object.

So companies add "AI features" partly because it's cool (news coverage, promotions), partly because of the FOMO (uncertainty is high, but what if we'd be missing a billion-dollar opportunity?), partly because of social cohesion (following fashion is natural, being a contrarian may be respectable, but looking ignorant is unacceptable). It's not about carefully calculated material returns on a a carefully measured investment. It may look inane, but it's not always stupidity, much like sacrificing some far-future perspectives in exchange of stock growing this quarter is not about stupidity.

While I'm somewhat sympathetic to this view, there's another angle here too. The largesse of investment on a vague idea means that lots of other ideas get funding, incidentally.

Every VC pitch is about some ground-breaking tech or unassailable moat that will be built around a massive SAM; in reality early traction is all about solving that annoying and stupid problem your customers hate doing but that you can do for them. The disconnect between the extraordinary pitch and the mundane shipped solution is the core of so much business.

That same disconnect also means that a lot of real and good problems will be solved with money that was meant for AGI but ends up developing other, good technology.

My biggest fear is that we are not investing in the basic, atoms-based tech that we need in the US to not be left behind in the cheap energy future: batteries, solar, and wind is being gutted right now due to chaotic government behavior, the actions of madmen that are incapable of understanding the economy today, much less where tech will take it in 5-10 years. We are also underinvesting in basics like housing, or construction tech. Hopefully some of the AI money goes to fixing those gaping holes in the country's capital allocation.

I've been watching this my whole life. UML, SOA, Mongo, cloud, blockchain, now LLMs, probably 10 others in between. When tools are new there's a collective mania between VCs, execs, and engineers that this tool unlike literally every other one doesn't have trade offs that make it only an appropriate choice in some situations. Sometimes the trade offs aren't discoverable in the nascent stage, a lot of it is monkey-see-monkey-do which is the case even today with React and cloud as default IMHO. LLMs are great but they're just a tool.

>What could have been if instead of spending so much energy and resources on developing “AI features” we focused on making our existing technology better?

I think we'd still be talking about Web 3.0 DeFi.

Greenfield development is infinitely easier than brownfield.

It's damn hard work to dig in, uncover what's wrong, and fix something broken - especially if someone's workflow depends on the breakage.

Flashy AI features get attention and even if they piss you off, they make you believe the thing is fresh. Sorry but you're human.

TFA kind of assumes that the companies involved would have improved their software in a world in which those resources weren't spent on AI. Since much software contained long-unfixed bugs well before the GenAI boom, I'm not convinced.

This raises an interesting question.

The amount of money that's been spent on AI related investments over the past 2-5 years really has been astonishing - like single digit percentage points of GDP astonishing.

I think it's clear to at there are productivity boosts to be had from applying this technology to fields like programming. If you completely disagree with that statement I have a hunch that nothing could convince you otherwise at this point.

But at what point could those productivity boosts offset the overall spend? (If we assume we don't get to some weird AGI that upturns all forms of economics.)

Two points of comparison. Open source has been credibly estimated to have provided over 8 trillion dollars of value to the global economy over the past few decades: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4693148 - could AI-assisted programming provide a similar boost over the next decade or so?

The buildout of railways in the 1800s took resources comparable to the AI buildout of the past few years and lost a lot of investors a lot of money, but are regarded as a huge economic boost despite those losses.