16-05-2025
AI's Pace Of Change: Six Indicators You Are Too Slow
AI startups are setting a faster pace of change than ever before. You know you are in trouble, said the legendary CEO Jack Welch, when the pace of change outside the company is faster than that inside. If that's true, then the rate of growth of AI startups should be striking terror into corporate board rooms around the world.
I have been skeptical of the pace at which AI will convert its potential into an economic revolution. However, I do not think that is a reason for complacency. Now is the time to ask if we are moving fast enough to ride the wave when it comes or if we will be washed aside.
AI startups are converting ideas into revenue at 10X the speed of previous generations with a fraction of the cost and far smaller teams. The old logic was that it takes a software startup anything from 3 to 5 years to get to $50M of revenue and another 5 to 7, to go to $1B. The AI generation is making this look sluggish.
Self-coding AI Lovable has posted $40M of annual recurring revenue in 5 months of trading on the back of just $7.5M of venture funding. Bolt's numbers are roughly the same, $30M in 4 months, with just $7.9M. Both of which look like slackers compared with image generation company Midjourney, which scaled to $200M ARR without any funding and an initial team of less than 10. [MK1]
Given the billions of dollars corporations spend to keep up in the AI race, one would think they are keeping up. However, all the evidence is that most are struggling to convert playing with AI into tangible outcomes.
Managers get ahead in large corporations by projecting confidence and certainty. You reassure the board and senior managers by demonstrating you have a plan, that there is 'alignment' between stakeholders, and that you will deliver 'unique' advantages.
The problem with this traditional approach is that nobody is certain how AI will play out. Corporations have struggled to find solid use cases to convert the hype into revenue. Consumers have started to roll their eyes at promises of embedded AI in everything from mobile phones to personal computers.
It is time to admit we don't know. We need to make a virtue of living with the uncertainty. That means lots of disciplined, small-scale efforts to learn what works, before converting it into the next big thing. Guessing how AI will deliver benefit and spending big on a master plan is a dangerous game.
My colleague Michael Kaplun has been working on this problem. How do we know we are going fast enough?
I converted his more thoughtful work into six ugly errors that we see companies committing. If any of these apply, it's time to get the skates on and figure out how to get to the head of the puck.
This is just six big issues we are seeing out there as companies wrestle to turn AI's potential into commercial reality. Hype cycles have a predictable path, and we are headed for the moment at which we all draw breath, realizing that the change isn't as fundamental as we thought. Or at least we were before we saw what Lovable has achieved. The message is clear. We need to move faster.
