alt Hacker NewsCEOs admit AI had no impact on employment or productivity
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Are businesses all running on sunk cost fallacy now? These findings have been coming out for a while but it doesn't seem to change anything.
I believe the lack of quick evident profit increases are partly a failure of imagination or a failure of understanding that AI agents are different from people. More impressive or faster in some ways, but much much less reliable in others.
The evolution of harnesses like claude code or open cause, and metaharnesses like Ralph loops, gas town, claws, etc. Will progressively allow for gradually better results and abilities even if models stopped evolving, and if the Mythos eval numbers are to be believed, there is still no hard ceiling to be felt yet.
At the same time, small models that can run on PCs VRAM/UNIFIED RAM have like Qwen are becoming more useful.
I predict that having more and more loops within loops within loops and layers of cloud/local models of different capabilities will solve a great many limitations of LLMS today...at the cost of speed and token count.
We've never had a tool that is at the same time so unreliable and complicated as GenAI before. It will take us a minute to figure out how to use it properly.
There's an interesting race happening here.
On one side, there is the usual process of figuring out how to properly use this new tech. It is to be expected that some experimentation is necessary to figure out what applications AI boosts productivity for and what applications it doesn't. There is unusually strong evangelism pushing AI into everything, so the negatives are going to be salient and may make it hard to spot some of the successes.
On the other side is something a little bit new: Deliberate enshittification. OpenAI and others no doubt saw the power crunch coming years in advance, yet it's still happening and is, ostensibly, the reason why prices are starting to go up. This was not unexpected. It's the business model. Build to the capacity that is cheaply available while offering your customers a sweetheart deal to get them addicted, and then jack up the prices when the competition has no cheap power to build upon. The result is locked in customers and locked out competition.
On one side, you have people learning when AI is appropriate and how to use it efficiently. On the other side, you have a small number of AI companies trying to extract every last bit of value so that any productivity gains wind up in their owners' pockets. Will the gains of more appropriately applying AI be entirely wiped out by enshittification?
I wish anytime someone used the word "productivity" there was an accompanying definition.
AI isn’t going away, but it’s also clear the much promised impacts aren’t there and aren’t coming anytime soon. A bit like the claims a few years back that we’d all have self driving cars by now.
The most likely outcome is an AI bubble correction that will be somewhat painful and wipe out many/most AI startups, followed by AI settling into day to day in a way that’s useful and found in many places, but not world-as-we-know-it-ending like the AI bros predict.
This article is underlining the stark contrast between the viewpoints of “AI Enthusiasts” and everyone else.
Don’t get me wrong, I use these tools daily. That being said I’m having a very hard time finding where the productivity gains are.
I imagine I’m far from alone in that search and when you pair that with the constant marketing and glowing “analysis” from some of the enthusiasts about how this technology is “solving coding” or “changing the face of security” or even leading to AGI it starts to tickle that part of my brain where I keep blockchain, NFTs and copper bracelets.
So TLDR the tech is good but the hype-slaves and their masters are killing it with overpromising and under delivering.
Did the hype cycle not have an impact on employment with the various layoffs? Or is this and admission that the layoffs were for other reasons and were just attributed to AI?
I’m not surprised about productivity though. Efficiency gains are limited by the actual bottlenecks. And truthfully, I think people are deluding themselves a bit about how effective vibe coding is and how much faster they are actually moving when you consider developers still need to form an understanding of the codebase and its systems.
Outside of coding, is there really a use case for LLMs that has the potential to make big efficiency gains? Idk.
A lot of organizations live in some game theoretic equilibrium that prevents cost improvements from being metabolized by the org without burning the cost elsewhere.
For example, consider a commodity business for software product X. All vendors of this product had their costs reduced by a factor of 100 over night for developing new product. They could increase their profits, lower their price, or re-invest the dividend. In software, the buyer usually buys on quality - so they all re-invest.
Now they are spending the same amount on product development, for the same price tag, and earning the same profit - but they might be shipping much faster.
My company has pushed engineering all-in for AI in the last few months
Our stock price has also gone down 70% in the last few months
Naturally, we're pivoting our platform to put AI front and center