This is a version of unbundling, which happens all the time in markets and can be incredibly strategically disruptive… the most valuable components of a product are carved out and performed by a faster/cheaper/better solution. I think Ethan is right to call… more
This is a version of unbundling, which happens all the time in markets and can be incredibly strategically disruptive… the most valuable components of a product are carved out and performed by a faster/cheaper/better solution. I think Ethan is right to call out this trend for certain jobs, which will shine a light on which parts of the job are truly valued (and paid for) by the market… because it’s not always what we think at face value. Are universities paying mathematicians for proofs or teaching?Ethan Mollick
What you are seeing in math right now is a consequence of the jagged frontier, and a precursor of what is to come in other professions. Yes, mathematicians do… more
What you are seeing in math right now is a consequence of the jagged frontier, and a precursor of what is to come in other professions. Yes, mathematicians do math, but they also have other tasks they view as important (mentor students, maintain a scientific community, safeguard the future of a field, foster a love of math) that AI can't do
At least one worry that mathematicians seem to have is that by focusing on the flashiest, most obvious element of what mathematicians do (make proofs), the AI companies are damaging the other tasks that AI can't do. AI can discover superhuman proofs, but that is not all that the math profession is about, and actually can undermine and reduce the attention to the other aspects of the job that are important to mathematicians. It becomes harder to defend the value of the many other tasks mathematicians do to the outside world if the most visible part is taken away.
I suspect we will see more of this across fields and professions that will increasingly be forced to help people understand that their jobs consist not only the most visible tasks that AI can do, but also tasks that the AI cannot do or does badly.like 83love 7insightful 7support 4
As AI moves from experimentation to production, discipline is going to matter just as much as ambition.
At LinkedIn we saw a while ago that the cost trends were clear, and that the future of our products would be powered by increasingly compute-expensive AI… more
As AI moves from experimentation to production, discipline is going to matter just as much as ambition.
At LinkedIn we saw a while ago that the cost trends were clear, and that the future of our products would be powered by increasingly compute-expensive AI workloads that are more intelligent, more capable, and more useful. But those workloads have to be ROI positive, and that’s not something you can just wake up and do overnight. So we set ourselves a challenge that sounds counterintuitive in today’s environment: how do we deliver increasingly sophisticated AI experiences while keeping our compute footprint as close to flat as possible?
This is where owning our own data centers and infrastructure as an applied ML company really pays off, because we can put in the instrumentation and make efficiency a long term investment up and down the stack rather than a one-time push. None of it came from a single breakthrough - it’s the outcome of hundreds of improvements, from optimizing GPU utilization and workload allocation to distilling larger models into smaller and more efficient ones to rethinking how work is distributed across training, inference, storage, and systems design. The results of that intentional push are starting to play out in terms of better experiences for our members and customers while giving our engineers the flexibility to keep innovating.
Raghu and I recently sat down with Paresh Dave at WIRED to talk through all of this, check it out!
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