Reality of "Artificial Intelligence"
Opinion
Practically, what is AI? Silicon valley has a
convenient answer: a powerful autonomous agent that
will spell the end of human white collar work as we
know it. A system that can reason, plan, and
eventually outcompete humans across most cognitive
domains. Dario Amodei, Anthropic CEO, in
Machines of Loving Grace, explores this idea quite deeply. AI systems
driving massive economic expansion by completely
taking over knowledge work. It’s a compelling story.
It’s also one that a lot of people have a strong
financial incentive to believe. That alone doesn’t
make it wrong. But it should make you skeptical.
This optimistic view is in large part backed up by
the idea that these systems have reasoning
capability in the same way a human does: stable
internal world models, memory and experience,
grounded understanding… I believe an observation of
the confabulated, so-called “thinking” token output
of any frontier LLM is enough evidence to see how
fatal of a misnomer this is.
Be that as it may, language models are genuinely
capable for the easy 80% of most tasks in many
economically important domains like healthcare,
management, and engineering. The problem is that the
easy 80% was never the valuble part. The work that
makes something correct, safe, useable lives in the
last 20%, exactly where AI becomes unreliable due to
but not limited by hallucinations, overconfidence,
temporal staleness... These are embarassingly
exposed, foundational issues with LLMs that make
many of the grandiose promises of AI hollow. At
least for now, and likely many years to come.
The truth is this: the transformer architecture is
roughly eight years old. Companies building at the
frontier have existed for only a few years. And yet
these systems are being pushed into education,
medicine, hiring, and governance at enormous scale.
Historically, there is always a lag between
invention and understanding. Electricity, for
example, took over a century between discovery and
commercialization. That lag is where society figures
out how, when, where, and by whom a new invention
should be used.
I propose that after striping away the marketing and
getting a clear hold on the fundamentals of LLMs,
two practical uses stand out.
First, AI is an extremely powerful interface to
human knowledge. The sum of human writing is now
explorable in natural language at a higher
resolution than ever before. A doctor can surface
relevant case studies in seconds. A student without
access to a great teacher can get one on demand.
This is not insignificant. It is a compression (but
not deletion) of the access gap that has always
existed between those with expert networks and
institutional resources and those without. The value
here is real and already being realized.
Second, for tasks that are well-bounded, simple,
repetitious, rote, AI's potential is actually not
fully realized. Drafting boilerplate, summarizing
structured documents, generating first-pass code
from well-specified requirements... These are tasks
where the ceiling of required correctness is low
enough that the 80% is actually sufficient. The
economic value here is also real, but it is the
value of, say,
a faster typist, not a new colleague.
These two uses, knowledge retrieval and rote
augmentation, are, I believe, the honest foundation
of what AI can deliver today. And this is the line
we build from: instead of selling autonomous agents
replacing human judgment, we are developing tightly
scoped, polished tools that make the capable more
capable. This is a narrower claim than what the
broader industry is making. It is also a true one.