u/WeBeBallin

Just interviewed Susanne Ditlevsen (President of the Royal Danish Academy of Science and professor of mathematics and statistics, University of Copenhagen), and we had a fun talk on the role of science, and what environment fosters "good science":

The tension she names: society wants science to justify itself up front — what's the deliverable, what's the application, where's the business case. But her view is that nearly every real breakthrough started as plain curiosity, with no case attached. And the part that's hard to sell publicly: a large chunk of good research produces nothing visible. It goes in the dustbin. She's blunt that plenty of her own work has ended there for good — and that this isn't waste, it's the cost of getting anywhere at all.

Her example: a paper she just submitted took two years. When you read the finished thing, you see a clean result. What you don't see are all the dead ends — and you have to walk down those dead ends to find the path that works. Fund only the work with a guaranteed output and you've quietly defunded the dead ends, which means defunding the breakthroughs too.

She points to the Institute for Advanced Study as the model — Einstein, Gödel, von Neumann, no deliverables demanded, just "go think." Her worry is that modern academia is losing that: the people best placed to chase the good questions now burn their sharpest hours writing grant applications to justify the work instead of doing it.

Anecdotally, it seems the people that "agree" with her take, already have one foot in the science camp, seeing they're the ones that can actually relate; taking this point even further, I suppose that means the further governments get from having science-trained members, the further away we push this idea.

Just interviewed Susanne Ditlevsen (President of the Royal Danish Academy of Science and professor of mathematics and statistics, University of Copenhagen), and we had a fun talk on the role of science, and what environment fosters "good science":

The tension she names: society wants science to justify itself up front — what's the deliverable, what's the application, where's the business case. But her view is that nearly every real breakthrough started as plain curiosity, with no case attached. And the part that's hard to sell publicly: a large chunk of good research produces nothing visible. It goes in the dustbin. She's blunt that plenty of her own work has ended there for good — and that this isn't waste, it's the cost of getting anywhere at all.

Her example: a paper she just submitted took two years. When you read the finished thing, you see a clean result. What you don't see are all the dead ends — and you have to walk down those dead ends to find the path that works. Fund only the work with a guaranteed output and you've quietly defunded the dead ends, which means defunding the breakthroughs too.

She points to the Institute for Advanced Study as the model — Einstein, Gödel, von Neumann, no deliverables demanded, just "go think." Her worry is that modern academia is losing that: the people best placed to chase the good questions now burn their sharpest hours writing grant applications to justify the work instead of doing it.

Anecdotally, it seems the people that "agree" with her take, already have one foot in the science camp, seeing they're the ones that can actually relate; taking this point even further, I suppose that means the further governments get from having science-trained members, the further away we push this idea.

Just interviewed Per Stenström — one of the most prominent computer architects to come out of Europe — and asked him about John Backus's 1977 Turing Award lecture – Backus (inventor of Fortran) coined the term "Von Neumann bottleneck":

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That was 49 years ago. Every CPU we've built since has the same architecture.

Per's answer is that the bottleneck never went away — we just got extraordinarily good at hiding it. Cache hierarchies, prefetching, out-of-order execution, speculative execution, cache coherence: the entire post-1980s history of CPU innovation is a stack of workarounds that make the bottleneck invisible for typical workloads without actually removing it.

His take on why we haven't replaced the architecture is essentially legacy — the software ecosystem built on Von Neumann is so vast that migrating to anything fundamentally different would cost decades of investment. His sharper point is that Von Neumann isn't "right" in any absolute sense: the architecture has to be in harmony with the underlying technology, and semiconductors happen to support what Von Neumann needs.

The thread I really wanted his read on was whether we'll ever see a genuine shift away from Von Neumann, or whether AI just pulls another generation of workarounds out of us. After 40+ years in the field he's honestly skeptical. He gave phase change memory as a recent cautionary tale: non-volatile, high-density, performance-competitive with DRAM, Intel and Micron poured huge money into it — and it died because of legacy. Even when a clearly viable alternative shows up, the cost of changing everything built around the current architecture tends to win.

The candidates he treats seriously are processing-in-memory (compute units distributed inside the memory itself — though he was honest this might be Von Neumann with a better layout rather than a genuine break) and entirely new substrates like quantum, which are a different paradigm but probably won't replace classical for general-purpose work.

I’d love a take on this from anyone closer to AI accelerator design or new-substrate work.

Link to full conversation here:

https://www.youtube.com/watch?v=NXVTACHB4Es