Failing to Understand the Exponential, Again
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Re: Failing to Understand the Exponential, Again
#2This extrapolates based on a good set of data points to predict when AI will reach significant milestones like being able to “work on tasks for a full 8 hours” (estimates by 2026). Which is ok - but it bears keeping https://xkcd.com/605/ in mind when doing extrapolation.
Re: Failing to Understand the Exponential, Again
#3> Instead, even a relatively conservative extrapolation of these trends suggests that 2026 will be a pivotal year for the widespread integration of AI into the economy:
> Models will be able to autonomously work for full days (8 working hours) by mid-2026. At least one model will match the performance of human experts across many industries before the end of 2026.
> By the end of 2027, models will frequently outperform experts on many tasks.
First commandment of tech hype: the pivotal, groundbreaking singularity is always just 1-2 years away.
I mean seriously, why is that? Even when people like OP try to be principled and use seemingly objective evaluation data, they find that the BIG big thing is 1-2 years away.
Self driving cars? 1-2 years away.
AR glasses replacing phones? 1-2 years away.
All of us living our life in the metaverse? 1-2 years away.
Again, I have to commend OP on putting in the work with the serious graphs, but there’s something more at play here.
Is it purely a matter of data cherry picking? Is it the unknowns unknowns leading to the data driven approaches being completely blind to their medium/long term limitations?
Re: Failing to Understand the Exponential, Again
#4> Again we can observe a similar trend, with the latest GPT-5 already astonishingly close to human performance:
I have issues with "human performance" as single data point in times where education keeps to excel in some countries and degrades in others.
How far away are we from saying, better than "X percent of humans" ?
Re: Failing to Understand the Exponential, Again
#5> Instead, even a relatively conservative extrapolation of these trends suggests that 2026 will be a pivotal year for the widespread integration of AI into the economy: > Models will be able to autonomously work for full days (8 working hours) by mid-2026. At least one model will match the performance of human experts across many industries before the end of 2026. > By the end of 2027, models will frequently outperfo…
Self driving cars have existed for at least a year now. It only took a decade of “1 years away” but it exists now, and will likely require another decade of scaling up the hardware.
I think AGI is going to follow a similar trend. A decade of being “1 years away”. Meanwhile, unlike self driving the industry is preemptively solving the scaling up of hardware concurrently.
Re: Failing to Understand the Exponential, Again
#6Exponential curves don't last for long fortunately, or the universe would have turned into a quark soup. The example of COVID is especially ironic, considering it stopped being a real concern within 3 years of its advent despite the exponential growth in the early years.
Those who understand exponentials should also try to understand stock and flow.
Re: Failing to Understand the Exponential, Again
#7> Given consistent trends of exponential performance improvements over many years and across many industries, it would be extremely surprising if these improvements suddenly stopped.
I'm sure people were saying that about commercial airline speeds in the 1970's too.
But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors.
With LLM's at the moment, the limiting factors might turn out to be training data, cost, or inherent limits of the transformer approach and the fact that LLM's fundamentally cannot learn outside of their context window. Or a combination of all of these.
The tricky thing about S curves is, you never know where you are on them until the slowdown actually happens. Are we still only in the beginning of the growth part? Or the middle where improvement is linear rather than exponential? And then the growth starts slowing...
Re: Failing to Understand the Exponential, Again
#8[deleted]
Re: Failing to Understand the Exponential, Again
#9> Instead, even a relatively conservative extrapolation of these trends suggests that 2026 will be a pivotal year for the widespread integration of AI into the economy: > Models will be able to autonomously work for full days (8 working hours) by mid-2026. At least one model will match the performance of human experts across many industries before the end of 2026. > By the end of 2027, models will frequently outperfo…
Many people seem to assert that "constant relative growth in capabilities/sales/whatever" is a totally reasonable (or even obvious or inevitable) prior assumption, and then point to "OMG relative growth produces an exponential curve!" as the rest of their argument. And at least the AI 2027 people tried to one-up that by asserting an increasing relative growth rate to produce a superexponential curve.
I'd be a fool to say that we'll ever hit a hard plateau in AI capabilities, but I'll have a hard time believing any projected exponential-growth-to-infinity until I see it with my own eyes.
Re: Failing to Understand the Exponential, Again
#10The model (of the world) is not the world.
Just because the model fits so far does not mean it will continue to fit.