Investors Expect AI Use to Soar, but That's Not Happening

did he do something other than that podcast?

  > at least 10% of the employees use GenAI daily.

- Accidentally tapping the AI mode on the Google search will count as an AI search. DDG doesn't even wait for you to tap and triggers an AI response. Still counts as AI use even if you didn't mean to use

- OpenAI, Google and Microsoft have been advertising heavily (usage will naturally go up)

- Scammers using GenAI to scam increases AI usage and GenAI is GREAT for scammers

- Using AI after a meeting to get a summary is nice but not to enough to make a visible impact in a company output. Most AI usages fall in this bucket

This tech was sold as a civilisation defining. Not GPT-X but the GPT that is out now. Tech that was "ready to join the workforce" while the reality is that these tools are not reliable in the sense he implied. They are not "workers" and won't change the output of your average company in any significant way.

Sweet talking investors is easy, but walking the talk is another thing altogether. Your average business has no interest or time in supervising a worker that at random times behaves unpredictably and doesn't learn not to make mistakes when told off.

What I wonder is beyond "using" AI, is what value the companies are actually seeing. Revenue growth at both OpenAI and Anthropic are increasing rapidly at the moment, but it's not clear if individual companies are really growing their useage, or if it is everyone starting to try it out.

Personally, I have used it sparingly at work, as the lack of memory seems to make it quite difficult to use for most of my coding tasks. I see other people spending hours or even days trying to craft sub-agents and prompts, but not delivering much, if any, output above average. Any output that looks correct, but really isn't cause a number of headaches.

For the VC's, one issue is constant increase in compute. Currently it looks to me like every new release is only slightly better, but the compute and training costs increase at the same rate. The AI companies need the end users to need their product so much they can significantly increase the price to the end users. I think this is what they want to see in "adoption", such a high demand that they can see the future of increasing prices.

40% of companies and 10% of employees can be using AI daily, but just for a small amount of tasks, and that usage can be leveling off.

At the same time, AI can be so inefficient that servicing this small amount of usage is running providers out of capacity.

This is a bad combination because it points to the economic instability of the current system. There isn't enough value to drive higher usage and/or higher prices and even if there was, the current costs are exponentially higher.

Companies are shoving AI into everything and making it intrusive into everyone's workflow. Thus they can show how "adoption" is increasing!

But adoption and engagement don't equal productive, useful results. In my experience it simply doesn't and the bottom is going to fall out on all these adoption metrics when people see the productivity gains aren't real.

The only place I've seen real utility is for coding. All other tasks, such as Gemini for document writing, produces something that's about 80% ok, and 20% errors and garbage. The work of going back through with a fine toothed comb to root out the garbage is actually more work and less productive than any simply writing the darn thing from scratch.

I fear that the future of AI driven productivity is going to push a mountain of shoddy work into the mainstream. Imagine if the loan documents for your new car had all the qualities of a spam email. It's going to be a nightmare for the administrative world to untangle what is real from the AI slop.

Unfortunately it's the coders who are most excited to put themselves out of business with incredible code-generation facilities. The techies that remain employed will be the feature vibers with 6-figure salaries supplied by the efforts of the now-unemployed programmers. The cycle will thus continue.

A popular belief these days is that investors from 2000 ultimately got it right. Truth - they simply got it wrong. They dumped tons of money into things that had no hope of justifying an ROI. They thought adoption of the technology would happen at a pace that was unprecedented or even possible. They assumed things would happen in 3 years that actually took 20. Yes - Shocker!

This may be a flippant comment, but it actually represents one of the reasons it is difficult to track GenAI usage!

Multiple sources have hypothesized (often based on discrepancies in data) that the gains from workers using GenAI are not necessarily propagated to their employers. E.g. any time savings may be dedicated to other professional or leisure pursuits.