mrlongroots

My point was much simpler, and in the context of the parent comment. With a gun to the head, I have no opinion on whether there exists a question that can not be settled empirically or how big that set is, just that I find it a convenient belief to operate off on a daily basis.

> This is an empirical question, right? We could have three teams of researchers, team A focusing purely on understanding, team B focusing purely on solving problems, and team C focusing on a combination of understanding and solving problems. See which one makes the most progress.

This is not me being snarky, but all meaningful questions can be settled empirically---e.g., "what happens to my body if I jump off the cliff". But empirical trials have a cost (time, money, irreversibility etc.) and we model and predict because it's cheaper than the trial.

Having not yet read the post,

> Bottom line, Tao is pushing for human understanding as the primary goal

100%. This applies to SWEs/math folks/etc. I do infra and I see many SWEs take their hands off the wheel. When they encounter perf issues they ask their agent and agent says GC and they say GC. It's rarely GC.

Now we might be well past the point where we need to remember the kubectl flags for rollouts etc. But basic human understanding of what their bots are doing as a goal has never changed. Humans are still liable for when bad things happen, and that hasn't changed over the roller coaster the last 5-odd years have been. LLMs, as astonishing they are at Navier Stokes, are still eminently capable of nuking your filesystem and saying "I can now see that that was wrong" with zero regrets. If you can't understand you can't sign off.

> math is the most pure expression of human understanding

This I don't know about. I think math acquires meaning when it contacts reality: like an iota is pointless until there's some circuit that it explains. Abstract math can diverge from that and can become an exercise in playing with symbols for their own sake.

In my experience, code is a small fraction of the work.

I'm in an infra team and for the last 2 weeks or so I've been trying to understand whether a particular workload will catch fire if a switch is flicked. I'm also new to the team so partly it is me wearing training wheels, familiarizing myself with the telemetry etc, but I will state that I'm not completely lousy at this stuff.

No model in my experience can do anything remotely comparable to the work "what happens to the workload if this switch is flicked" needs. They can't even design a reliable quick experiment to answer what cast should be applied to the binary trace_id in table A for the join to table B to work. They will happily do something idiotic and then conclude that the join does not work.

Opinionated take: ingestion should be treated as a streaming reorganization workload, separate from whatever a "database" is.

You do not even need to change the Iceberg spec, although you could. Just rejig things a bit to produce better statistics (think liquid clustering but on the write path). You still write the same dataset queried the same way, but automatically get better query performance. Workload owners should be able to decide the tier at which this streaming reorganization happens: they know their data staging and bandwidth constraints the best.

> And what might be the outcome?

DoE compute budgets are ~10B USD across labs. AI training is a trillion-dollar workload. Different league.

All that would not help you with an AI training cluster interconnect. See Amin Vahdat's keynote at HotInterconnects 2025. Everyone is building a fabric for this stuff from scratch (Google/Falcon, Amazon/EFA, Azure/MANA, Cornelis/CN5000, and obviously Mellanox).

It is partly this and partly a funding vehicle for American next-gen computing. It is not that hard to estimate FP64 ballpark from a whole bunch of public statistics. And it takes a looot more than raw FLOPs to get a simulation working. And presumably a looot more to translate it into practice. And the openness makes it easier to talk to different vendors and not get in the way of them having all the H1Bs it takes to get these things to work.

Plus one think I like to say is that if a bullet is flying towards you, you could know everything about the chemistry of the gunpowder and the composition of the alloy without it affecting what happens next.

Today's limits are known and undisputable. Tomorrow's limits are a promise: some promises over-deliver, others under-deliver. :)

Regardless, to bring the discussion back to the claim at hand: at all points in future, we will need the ability to reason under partial information. "Absolutely flawlessly complete diagnostics" is an asymptotic goal we get closer to but never reach. This is both very doable for a disciplined human, and very hard to outsource completely to an LLM. Treated as tools operatored by competent users, they are magical. But they can not outperform their user.

> With alzheimer's an autopsy can tell for sure but that's not much help for a patient.

Ok let us unpack this statement.

For your point to hold, I would have to be saying "all kinds of practical diagnostics are invented now. No progress can be made in better diagnostics".

If Alzheimer's can be validated by slicing open a dead patient, there is a causal mechanical explanation for the disease. If we can not confirm that defect without slicing open the patient, that is a limitation of 2026 tools. The "One True Diagnosis" is an Oracle explanation that all real diagnostic techniques try to approach in the asymptotic sense, and it is helpful exactly because it clarifies in discussions like this.

There are going to be diseases where we do not yet have causal explanations. Or where we treat them without establishing them. Hypertension is one example: while technically it can be caused by vascular stiffness, some weirdness with the RAAS system, some hyperadrenergic weirdness, practically you get a lot of mileage out of just prescribing people telmisartan if they're old.

That does not mean the frontier of hypertension is settled, or the 10% who do not have a vascular stiffness problem would not benefit from better causal models of hypertension. Science is us continuously pushing back against the fog: of the tools we have in 2026, some are great, some are imperfect, some are promising etc.

Pyschiatry gets complicated because the failures are not mechanical. Even if you could image every single neuron in a person's head we do not have a very good way to define an algorithm for these issues. I do not have a good answer for psychiatry.

> This is a kind of thinking a lot of programmers fall prey to. The real world, outside of code, is a very fuzzy and inherently analog place.

Having said that, I would vehemently reject and push back against this, and without doubting your sincerity, characterize it as an ad hominem.

The vast majority of issues with the human body are mechanical in nature. Restricted blood flow, unwanted tissue, a broken bone, a bad valve etc. These are causal descriptions of "disease". Where causal descriptions exist, the "One True Diagnosis" principle holds. Psychiatry just happens to be unique in that it is a fuzzy science where we rely on checklists and ultimately all diagnosis is probabilistic.

EDIT:

> This is a kind of thinking a lot of programmers fall prey to. The real world, outside of code, is a very fuzzy and inherently analog place. There is very rarely one in any complex system having a complex problem needing a complex solution. At some point even the definition of diagnosis gets fuzzy.

I would also push back against this mindset in general. This is not a falsifiable claim, it is incoherence as an argument, and I do not need to be a programmer to hold this position.

That the real world is analog is irrelevant to its amenability to causal explanations. Or "fuzzy": "fuzzy" in this context just does not mean anything.

I am not trying to sound exasperated or win internet points, just impress this point on you and anyone reading this. We can write math to predict weather, make it tractable to solve using approximations, tolerate IEEE 754 weirdness, and finally tell what the clouds will do a week from now. This is nature telling us that there is a pattern to how it behaves, and it is the only weapon we have as scientists.

To say that nature is not amenable to explanations is a very defeatist thing to say: neither Newton nor Einstein nor any of the million-odd people that have built modern society would exist if nature did not have causal explanations. I urge you to reject this defeatist thinking.

> I think „the diagnosis” is over simplification and lots of professionals would disagree that there’s always a single one.

"The Diagnosis" does not mean "one root cause".

Situation: my car has some unexplained vibrations. 1. Mechanic A says that it is the engine mounts 2. Mechanic B says that it is some weirdness in how the exhaust assembly is hanging to the underbody 3. Mechanic C says that it is just my wife farting

I replace engine mounts and 40% of the problem is reduced. I then drive without my wife and the remaining 60% is solved.

"The Diagnosis" was: 40% mounts, 60% wife, 0% exhaust.

There is always one "The Diagnosis".

> There is no guarantee that the LLM will help you converge on anything.

Absolutely. The guarantee does not come from the LLM. The LLM is a simply an improved version of Google Search.

The guarantee can only come from a systemic application of epistemic discipline and reasoning, which is very much (smart) human territory.

Put it another way, I could make good decisions with/without LLMs, with some uncertain diagnostics as input. I would have to trawl through 50 papers myself, and it is possible that my decision arrives 5 years too late as a result. LLMs enable trawling and do some of the legwork in connecting the dots, but are ultimately only as capable as the orchestrating human.

Yeah I think the OP is muddling the point by conflating "physician's version of the diagnosis" with "The Diagnosis".

There is absolutely one "The Diagnosis". Human body is a machine, albeit a very complex one, and all measurement sources have noise. But they are all measuring one reality, and if there is a problem, there should be one explanation that all measurements align with. They can be noisy but can never be conflicting (instrument error notwithstanding).

Physicians' ability to arrive at "The Diagnosis" would vary, but it does not mean one does not exist. I am not sure if characterizing human body as derministic or not is relevant here.

Maybe I am missing something but I just find this wrong.

Everything is a puzzle: there is one "Truth" or one diagnosis. You (a smart human) should be able to converge on it by cross-examining your LLMs. By themselves, they have no interest in revealing this, no stakes, which makes them tools only useful at the hands of a capable investigator.