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 The Einstein AI model

February 26, 2025 5 min read science

I shared a controversial take the other day at an event and I decided
to write it down in a longer format: I'm afraid AI won't give us a
"compressed 21st century".

The "compressed 21st century" comes from Dario's "Machine of Loving
Grace" and if you haven't read it, you probably should, it's a
noteworthy essay. In a nutshell the paper claims that, over a year or
two, we'll have a "country of Einsteins sitting in a data center",
and it will result in a compressed 21st century during which all the
scientific discoveries of the 21st century will happen in the span of
only 5-10 years.

I read this essay twice. The first time I was totally amazed: AI will
change everything in science in 5 years, I thought! A few days later
I came back to it and while re-reading I realized that much of it
seemed like wishful thinking.

What we'll actually get, in my opinion, is "a country of yes-men on
servers" (if we just continue on current trends) but let me explain
the difference with a small part of my personal story.

I've always been a straight-A student. Coming from a small village, I
joined the top French engineering school before getting accepted to
MIT for PhD. School was always quite easy for me. I could just get
where the professor was going, where the exam's creators were taking
us and could predict the test questions beforehand.

That's why, when I eventually became a researcher (more specifically
a PhD student), I was completely shocked to discover that I was a
pretty average, underwhelming, mediocre researcher. While many
colleagues around me had interesting ideas, I was constantly hitting
a wall. If something was not written in a book I could not invent it
unless it was a rather useless variation of a known theory. More
annoyingly, I found it very hard to challenge the status-quo, to
question what I had learned. I was no Einstein, I was just very good
at school. Or maybe even: I was no Einstein in part *because* I was
good at school.

History is filled with geniuses struggling during their studies.
Edison was called "addled" by his teacher. Barbara McClintock got
criticized for "weird thinking" before winning a Nobel Prize.
Einstein failed his first attempt at the ETH Zurich entrance exam.
And the list goes on.

The main mistake people usually make is thinking Newton or Einstein
were just scaled-up good students, that a genius comes to life when
you linearly extrapolate a top-10% student.

This perspective misses the most crucial aspect of science: the skill
to ask the right questions and to challenge even what one has
learned. A real science breakthrough is Copernicus proposing, against
all the knowledge of his days -in ML terms we would say "despite all
his training dataset"-, that the earth may orbit the sun rather than
the other way around.

To create an Einstein in a data center, we don't just need a system
that knows all the answers, but rather one that can ask questions
nobody else has thought of or dared to ask. One that writes 'What if
everyone is wrong about this?' when all textbooks, experts, and
common knowledge suggest otherwise.

Just consider the crazy paradigm shift of special relativity and the
guts it took to formulate a first axiom like "let's assume the speed
of light is constant in all frames of reference" defying the common
sense of these days (and even of today...)

Or take CRISPR, generally considered to be an adaptive bacterial
immune system since the 80s until, 25 years after its discovery,
Jennifer Doudna and Emmanuelle Charpentier proposed to use it for
something much broader and general: gene editing, leading to a Nobel
prize. This type of realization -"we've known XX does YY for years,
but what if we've been wrong about it all along? Or what if we could
apply it to the entirely different concept of ZZ instead?" is an
example of out-side-of-knowledge thinking -or paradigm shift- which
is essentially making the progress of science.

Such paradigm shifts happen rarely, maybe 1-2 times a year and are
usually awarded Nobel prizes once everybody has taken stock of the
impact. However rare they are, I agree with Dario in saying that they
take the lion's share in defining scientific progress over a given
century while the rest is mostly noise.

Now let's consider what we're currently using to benchmark recent AI
model intelligence improvement. Some of the most recent AI tests are
for instance the grandiosely named "Humanity's Last Exam" or
"Frontier Math". They consist of very difficult questions -usually
written by PhDs- but with clear, closed-end, answers.

These are exactly the kinds of exams where I excelled in my field.
These benchmarks test if AI models can find the right answers to a
set of questions we already know the answer to.

However, real scientific breakthroughs will come not from answering
known questions, but from asking challenging new questions and
questioning common conceptions and previous ideas.

Remember Douglas Adams' Hitchhiker's Guide? The answer is apparently
42, but nobody knows the right question. That's research in a
nutshell.

In my opinion this is one of the reasons LLMs, while they already
have all of humanity's knowledge in memory, haven't generated any new
knowledge by connecting previously unrelated facts. They're mostly
doing "manifold filling" at the moment - filling in the interpolation
gaps between what humans already know, somehow treating knowledge as
an intangible fabric of reality.

We're currently building very obedient students, not revolutionaries.
This is perfect for today's main goal in the field of creating great
assistants and overly compliant helpers. But until we find a way to
incentivize them to question their knowledge and propose ideas that
potentially go against past training data, they won't give us
scientific revolutions yet.

If we want scientific breakthroughs, we should probably explore how
we're currently measuring the performance of AI models and move to a
measure of knowledge and reasoning able to test if scientific AI
models can for instance:

Challenge their own training data knowledge
Take bold counterfactual approaches
Make general proposals based on tiny hints
Ask non-obvious questions that lead to new research paths

We don't need an A+ student who can answer every question with
general knowledge. We need a B student who sees and questions what
everyone else missed.

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PS: You might be wondering what such a benchmark could look like.
Evaluating it could involve testing a model on some recent discovery
it should not know yet (a modern equivalent of special relativity)
and explore how the model might start asking the right questions on a
topic it has no exposure to the answers or conceptual framework of.
This is challenging because most models are trained on virtually all
human knowledge available today but it seems essential if we want to
benchmark these behaviors. Overall this is really an open question
and I'll be happy to hear your insightful thoughts.

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PPS: Many have been proposing "move 37" as evidence that AI has
already reached Einstein-level intelligence, so I'd like to expand on
this specific example.

Move 37, while impressive, is still essentially a straight-A student
answer to the question posed by the rules of the game of Go.

Similarly, I'm quite convinced AI models will soon be able to come up
with at least one formal mathematical proof that will be considered
much more elegant than those developed by the best mathematicians to
date.

But I don't think these truly qualify as the paradigm shifts we're
discussing.

I believe an Einstein-level breakthrough in Go would involve
something more fundamental, like inventing the rules of Go
itself--exploring the vast landscape of possible game rules to create
something genuinely more compelling than existing games.

Or in mathematics (a more apt analogy since games are human
constructs unlike natural science), it might involve "discovering
connections between previously unrelated mathematical fields to
propose a new area of fruitful mathematical research" (which
typically forms the basis of Fields Medal-winning work).

The bar for what constitutes an Einstein-level scientific paradigm
shift remains really high in my opinion.

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