I've been wondering the same and started exploring building a startup around this idea. My analysis led me to the conclusion that if AI gets even just 2 orders of magnitude better over the next two years, this will be "easy" and considered table stakes. Like connecting to the internet, syncing with cloud or using printer drivers

I don't think there will be a very big place for standalone next gen RPA pure plays. it makes sense that companies that are trying to deliver value would implement capabilities this. Over time, I expect some conventions/specs will emerge. Either Apple/Google or Anthropic/OpenAI are likely to come up with an implementation that everyone aligns on

In other words, I agree

> if AI gets even just 2 orders of magnitude better over the next two years

You realize this means '100 times better', right?

yes, thanks for pointing out the assumption here. I'm not sure how to quantify AI improvements and tbh not really up to speed on quantifiable rate of improvement from 4 to 4o to o1

100 times better seems to me in line with the bet that's justifying $250B per annum in Cap Ex (just among hyperscalers) but curious how you might project a few years out?

Having said that, my use of 100x better here applies to 100x more effective at navigating use cases not in training set, for example, as opposed to doing things that are 100x more awesome or doing them 100x more efficiently (though seemingly costs, context window and token per unit of electricity seem to continue to improve quickly)

I would think that such an increase in AI capability would basically be AGI...

Just to give a few comparisons, the following things are two orders of magnitude apart:

1. The force felt by a mosquito landing on your arm and getting punched by Mike Tyson in his prime

2. A firecracker exploding and a stick of dynamite exploding

3. The heat from a candle and the heat from a blowtorch

4. The sound from a whisper and the sound from jet engine