Google just shipped the smartest AI

model on the planet. It's Gemini 3.1

Pro. It costs a seventh of the

competition and they don't even need you

to use it. That's right. They shipped a

model that leads on 13 of 16 benchmarks.

It costs roughly a seventh of what Opus

4.6 charges. And Google really doesn't

care. That's not a weird flex on their

part. It might be the most important

strategic signal in AI right now. And

almost nobody is talking about it. The

coverage of Gemini 3.1 Pro has been all

about those benchmarks. And what's been

missing is the question that is

underneath. Why does the richest company

in tech, a company generating over a

hundred billion in annual free cash

flow, build the most powerful reasoning

engine on the market, price it at the

floor, and be perfectly comfortable if

you keep using Claude or Chad GPT for

your daily work? The answer reshapes how

you should think about every model

release from here on out. It changes how

you evaluate your own skills and it

explains why most of the conversation

about which AI I should use is really

asking the wrong question at this point.

So a couple weeks ago I wrote about Opus

4.6 and the way they use 16 AI agents to

build a C compiler. That piece was about

a new kind of labor. Agents coordinating

in teams, managing engineering orgs,

doing weeks of sustained autonomous

work. This video is about something

different. This video is about why the

company with the deepest pockets and the

widest distribution in the history of

computing in the history of the planet

is playing a fundamentally different

game from anybody else. And what that

means for how you evaluate AI models,

choose your tools, and understand which

of your problems are about to get

dramatically easier to solve versus

which ones are not. So, we're going to

talk about one benchmark out of those 16

because I don't usually talk about

benchmarks. That number is 77.1%

and it's on the ARC AGI2 benchmark. Why

do we care? It's not about pattern

matching from training data. ARC AGI2

tests whether a model can solve logic

problems it has never ever seen before.

So it's not about retrieval from

memorized examples, but about genuinely

novel reasoning. Can the model look at a

problem it's never encountered and

figure it out from first principles? I

want you to look at the acceleration on

that benchmark. Gemini 3 Pro, which

shipped just in November, scored 31.1%.

Just 90 days later, 3.1 Pro ships and it

more than doubles that score. That 46

percentage point jump is the largest

single generation reasoning gain any

Frontier model family has ever produced.

Opus 4.6 scored 68.8% on the same

benchmark, which is very close. GPT 5.2

scored a little bit lower. The rest of

the score card tells a very similar

story, right? A very high score on GPQA

Diamond, which is essentially a science

benchmark that's saturated at this

point. A very strong benchmark score on

Live Codebench Pro, which measures

coding abilities. You get the idea.

These numbers are real, but the

benchmark isn't the point. The point is

that Google chose to optimize for pure

reasoning. Enthropic built opus 4.6 Six

for agentic work. Stained autonomous

coding pool calling in loops. Agent

teams coordinating across code bases

sometimes for weeks at a time. Open AI

built codeex 5.3 for specialized coding

pipelines with self- bootstrapping

sandboxes and thousand token per second

throughput at max. Google built Gemini

3.1 Pro for none of those things. They

built it to think harder, not to code

longer, not to manage more agents, to

reason more deeply about problems it has

never seen. That design choice tells you

everything about who Google thinks it is

and where they think they're going with

AI. Demuse Hosabus has been saying the

same sentence for 15 years. Step one,

solve intelligence. Step two, use it to

solve everything else. And so Google is

focused on solving intelligence. He said

it when Deep Mind was a London startup

nobody had heard of. He said it after

Alph Go beat a Go Grandmaster. He said

it at Davos last month. He said it on

the Fortune podcast last week where he

predicted artificial general

intelligence would come very very soon.

He's actually updated his prediction and

he is a conservative guy. He's seeing it

coming within 5 years at this point. And

he said it on 60 Minutes when he talked

about curing most diseases within a

decade. The sentence hasn't changed

because his mission hasn't changed. This

is not how anyone else in the AI

industry talks. Sam Alman talks about

products, partnerships, distribution,

the race to a billion users. And yeah,

he talks about intelligence, but in the

context of how it's applied so

frequently. When OpenAI put ads in Chad

GPT, they did so because they needed to

monetize a billion person user base. And

Huss, when asked about the Chad GPT ads

at Davos, said he was surprised OpenAI

moved so fast on advertising. Of course,

he said that Google is able to monetize

search and they don't need to monetize

ads in Gemini. As a result, Open AAI

does not have the world's largest search

engine and its profit streams funding

them. And OpenAI is in a different

funding position. The subtext from

Hosabuse was unmistakable. We're not

thinking about monetizing our Google AI

chatbot. We're just thinking about

intelligence. Now, I want to be clear

here. This is not because Google is

somehow above commercial concerns.

Google runs the most profitable

advertising business in human history.

They generated over a hundred billion

dollars in annual free cash flow from

search, YouTube, and cloud. They're

spending $93 billion on capital

expenditure this year, and most of it is

AI. They can afford to let Gemini be a

research vehicle because their economic

engine has nothing to do with whether

you personally prefer Claude or Chat GPT

for your daily workflow. Everyone else

in AI is trying to figure out how to

monetize models. Google is trying to

figure out how to build intelligence.

The money handles itself. Must be nice.

And how does Google get that advantage?

It's not just the profit streams. It's

also that Google has deliberately built

over the last decade a vertical stack in

AI that nobody else has. They design

their own silica. The Ironwood TPU, 7th

generation announced earlier this year,

delivers 10 times the compute power of

the last generation at roughly half the

energy cost per operation. It can link

up 9,216

chips in a single pod. Anthropic just

signed a deal to use a million TPUs

under a multi-year arrangement valued in

the tens of billions of dollars. Meta is

reportedly negotiating a similar

commitment. When your competitors train

their frontier models on your hardware,

you have built something beyond a moat.

You've built an impregnable fortress.

Google trains their own models on that

silicon. They deploy those models

through their own cloud infrastructure.

Google Cloud, which nine out of 10 AI

research labs use in some capacity. They

distribute them to 650

million monthly active Gemini users,

although again that's not the primary

point. And it's up 44% in a single

quarter. plus billions more through

search, Android, YouTube, and Chrome.

They have easily the largest human reach

in history of any company. And they fund

the fundamental research through Deep

Mind, which won a Nobel Prize in

chemistry 18 months ago for Alphafold, a

system that predicted the structure of

virtually every known protein, a problem

biologists have been working on for 50

years. This vertical integration from

transistor design to protein folding is

not an accident. It's the architecture

of a company that believes intelligence

is a problem in computer science, that

the problem is solvable, and that

solving it requires controlling the

entire stack from physics up to

software. Google's Jeff Dean said

they're working to shrink TPU design

cycles from 2 years down to 6 to9 months

by using AI in the chip design process

itself. They're using intelligence to

build the hardware that runs

intelligence. The flywheel is

self-reinforcing and it's accelerating

dramatically. Nobody else has this.

Microsoft has Azure and a partnership

with OpenAI, but they don't make chips

and their consumer distribution in AI is

very fragmented. Copilot has been

rightly criticized for feeling

disjointed across office products.

Amazon has AWS and Tranium chips, but

their models trail the frontier

dramatically. Meta has research talent

and social distribution, but no cloud

business and no chip stack. Anthropic

has arguably the best product for

agentic work today, but they run on

other people's hardware, including

Google's TPUs and Amazon's

infrastructure, and they need every

single customer they can get to justify

their valuation. They cannot afford to

just build pure intelligence. Google is

the only company that could lose the

model race quote unquote entirely. Every

developer and every enterprise customer

choosing Claude or Chat GPT for each

task, and they would still be fine

because the models are not their

business. The models are experiments in

intelligence that they choose to

release, funded by the largest cash

generating machine in technology running

on proprietary silicon and feeding

results back into products used by half

the planet. That changes how you should

interpret what Google ships. So what is

Gemini 3.1 Pro and what isn't it? Gemini

3.1 Pro is not a coding agent per se,

though of course it can write code very

well. It's also not an agent manager,

although it can manage agents. It's not

trying to autonomously close issues

across a 50 person engineering org the

way opus 4.6 did at Raku 10en if that's

what you need. Opus is probably better

at it right now and Google knows that.

What 3.1 Pro actually is is the

strongest pure reasoner available at

scale at a price point that makes it

viable for any problem where reasoning

depth matters more than tool

orchestration. at $2 per million input

tokens and just 12 per million output

tokens. It's roughly seven and a half

times cheaper than Opus 4.6 on input and

more than six times cheaper on output.

For a workload processing a billion

tokens a month, that is the difference

between a $15,000 bill and a $2,000

bill. With context caching, Gemini's

costs can drop another 75% from there.

JetBrain's director of AI called it

stronger, faster, and more efficient.

Artificial Analysis currently ranks it

as the top model on their intelligence

index at roughly half the cost of its

nearest frontier peers. The model also

ships with configurable thinking levels,

low, medium, high, and max. So you can

dial reasoning depth and cost up or down

upon request. Simple classification or

summarization? Low thinking, fast and

cheap. Novel scientific problem

requiring multi-step deduction? Well,

let's turn it up to max. Let it work.

This is cost engineering for reasoning

at a granularity nobody else really

offers. And it matters because it means

you don't pay frontier prices for

routine tasks. But here's the real

comparison. And it matters. When you

give these models tools, web search,

code execution, database access, file

systems, and you measure their

performance on complicated real world

tasks that require using those tools

together to get work done, 4.6 catches

up and often pulls ahead. On humanity's

last exam with search and code tools,

opus scores 53.1%

versus Gemini's 51.4%. On GDP val, which

measures expert level office and

financial tasks, Opus leads by 289 ELO

points, which is a massive gap. Remember

that is the realworld work one guys. On

the arena coding leaderboard and expert

human preference rankings, clawed models

consistently win. The pattern here is

unambiguous. Gemini 3.1 Pro is the

strongest naked reasoner. Opus 4.6 is

the strongest equipped reasoner, the

model that's best at combining

intelligence with the ability to use

tools, call APIs, read files, write

code, and sustain that work over hours

and days. GPT 5.3 CEX is the strongest

specialist coder. If intelligence is the

engine, tools are the drivetrain. Google

built a better engine and Anthropic

built a better car. OpenAI built better

racing transmission for any individual

task. The question isn't which model is

smartest. The question is whether the

task got bottlenecked by raw thinking or

whether the ability to act on that

thinking across tools in time is the

real bottleneck. And that question turns

out to be way more interesting than any

benchmark and we are not talking about

it enough. So we're talking about it

now. First let's understand what Gemini

3.1 Pro is meant to solve in terms of

problems. I think a good example is

Gemini 3's Deep Think, which was

released February 12th and is a

specialized reasoning mode that sits

above 3.1 Pro on the intelligence curve.

Deepthink collaborated with human

researchers to solve 18 previously

unsolved problems across mathematics,

physics, computer science, and

economics. These are not incremental

improvements. They're not benchmark

tricks. It's making original research

contributions. A conjecture in online

submodular optimization had stood

unproven since 2015. And if like me you

were wondering what is online submodular

optimization, it is a way of talking

about data that moves around the world

on the internet and mathematicians get

involved. In this case, mathematicians

proposed a seemingly obvious rule. In a

data stream, if you copy an arriving

item, it is always less valuable to do

that than to move the original,

presumably because of the risks of

defects. Now, mathematicians had spent

more than a decade trying to prove this

was true. Gemini Deepth think engineered

a precise three item combinatorial

counter example and proved the

conjecture false in a single run. That

wasn't even the most interesting result

on the max cut problem, which is a

classic network optimization challenge,

which again I'm getting way out of my

depth here and that's kind of the point.

That is why I am sharing this. If you

feel out of your depth, this is part of

what I'm trying to communicate. Gemini

3.1 Pro is about this kind of pure

reasoning. Anyway, Jee and I solved this

problem by pulling in I had to look this

up. The curb bra theorem and measuring

theory from continuous mathematics to

solve a discrete algorithmic puzzle.

Wow, that was a mouthful. Human

algorithm researchers would not

typically reach into geometric

functional analysis to solve a graph

theory problem because as much as they

may sound like gobbledegood to you,

they're actually really different

domains of mathematics. The model

crossed disciplinary boundaries that

human specialists very rarely cross

because the model doesn't see

disciplinary boundaries and that is one

of the strengths of an AI model. It's

tackling problems in physics too where

it tackled a gravitational radian. Look,

the list goes on. It tackled problems in

physics. It caught a critical error in a

cryptography paper. And just two days

before 3.1 Pro shipped, Isomorphic Labs,

which is DeepMind's drug discovery

spin-off, published results from their

AI drug design engine. And the system

had more than doubled AlphaFold 3's

accuracy on the hardest protein

prediction tasks and outperformed gold

standard physics-based methods at a

fraction of the cost and time. So here

we have what? Protein folding. We have

complicated mathematics, conjecture

breaking. We have gravitational

radiation involved, cryptographic error

detection. It's messing around with

crystal growth optimization. These are

very much pure reasoning problems at the

extreme end of the difficulty spectrum.

My head is spinning just trying to

communicate them. And I am a long way

from anywhere close to even

understanding the research. And they

share specific characteristics. The

inputs are well- definfined like a

protein sequence. The problem can be

stated extremely precisely. And the

solution requires a long and sustained

chain of logical deduction that a human

mind can verify but often cannot

generate without years of specialized

training. This is the domain where

Google's investment in intelligence as a

problem really pays off. This is what

means when he says let's solve

intelligence and then use that to solve

everything else. The everything else

starts with science. It starts with the

problems that have the highest ratio of

reasoning difficulty to ambiguity where

the question is very clear but the

answer requires genuine intellectual

horsepower to reach. And it's why I

think the most important question for

anyone reading about Gemini 3.1 Pro is

not is it better than Opus, but rather

what percentage of your actual work is

bottlenecked by that kind of thinking?

And here's where my analysis starts to

get much more personal than most of the

3.1 Pro analyses out there. Because hard

is not one thing. We've been treating

hard work like one thing for too long.

The benchmarks tend to treat it as a

single thing. The model marketing

certainly treats it like a single thing.

The LinkedIn discourse treats it like a

single thing. And the model landscape is

now very differentiated. And it's going

to force us to decompose what hard feels

like. Think about the problems you face

at work. Some of them are hard because

they require deep reasoning. Analyzing a

complicated contract for the clause that

creates downstream liability across

three jurisdictions or working through a

multi-step financial model to find the

sensitivity that changes the investment

decision or diagnosing why a distributed

system fails under a specific load

pattern that only appears at scale.

These are problems where you need to

hold multiple variables up in your head,

follow a chain of logic through branches

and dependencies, and arrive at a

conclusion that isn't very obvious from

the surface. But most problems in

business are not actually hard on a

reasoning axis. They're hard on other

axes entirely. Let me give you a few

categories that I think we don't talk

about enough, and we need to understand

these problem types to actually figure

out how we're going to thrive in the AI

era. First, effort problems. They're not

intellectually difficult. They're just

large. Auditing 3,000 vendor contracts

for compliance changes. Migrating a

legacy codebase with 2 million lines of

cobalt. Reviewing every customer

interaction from last quarter to

identify churn signals. The thinking at

each step is super straightforward. Any

competent person could do any individual

piece. The challenge is sustained

attention and thoroughess across a

massive surface area without dropping

detail. These are the problems Agentic

AI was built for. Opus 4.6 running for

hours on Rockuten's codebase is solving

an effort problem. The 16 agents

building a C compiler over weeks solving

an effort problem. The thinking per step

is not extraordinary. The endurance is

and that's not what Gemini 3.1 Pro is

optimized for. Here's another problem

type. Coordination problems. Getting six

teams aligned on a shared architecture

decision when each team has different

priorities in different technical

contexts. Routing work across

dependencies so that the back-end team

doesn't block, the front-end team

doesn't block the QA team. Managing

information flow so the right people

know the right things at the right time

and nobody wastes 3 days building

something that was already decided

against in a meeting they weren't even

invited to. Rakuten's deployment of Opus

4.6 six where the model autonomously

closed issues and routed them across a

50 person org and six different repos

that is solving a coordination problem.

That's a model solving a human

coordination problem. It understood not

just the code but which team owned the

repo, who has context on what, where to

assign the issues and critically when to

escalate. In other words, the model

developed a kind of relevant engineering

organizational awareness. Those are

capabilities where Opus 4.6 six leads in

a way that Gemini 3.1 Pro does not.

Emotional intelligence problems.

Delivering feedback to a direct report

who's been underperforming, but is going

through a divorce. Navigating a

negotiation where the other party's

stated concern, their price, is not

their real concern, which is control.

Reading a boardroom and knowing that the

CFO's silence means opposition, not

agreement. managing a team through a

reorg where half the people are afraid

of their jobs and the other half are

angling for promotions which sounds a

lot like AI or change management.

Calibrating tone, timing, and

transparency in situations where the

right thing to say depends on dynamics

no model can observe. We actually don't

have models that solve this part well.

Models don't even attempt this with

reliability. And this is a massive

percentage of what makes management and

leadership genuinely hard. It frankly

makes being a solid senior individual

contributor hard because there is no

escaping this kind of emotional

intelligence problem the farther you get

into business. Judgment and willpower

problems. Deciding to kill a project

your team spent six months building

because the market signals shifted.

Saying no to a lucrative client whose

values don't align with your companies.

Choosing the strategically correct but

politically dangerous path when the data

supports it but the executive team does

not want to hear it. making the

unpopular call, accepting the career

risk. Those aren't really reasoning

problems. Any competent analyst would

lay out the logic. Those are courage

problems. They're identity problems and

they're almost entirely unsolvable by AI

because the bottleneck is not computing

the correct answer. It's having the

nerve to act on it. That is a human

challenge. Domain expertise problems. A

senior engineer doesn't debug faster

because they reason better than a

junior. They debug faster because

they've seen that exact stack trace

before. They know the library's

undocumented quirks and they remember

the production incident from 2019 that

had the exact same root cause. A veteran

M&A attorney doesn't evaluate a deal

better because they're smarter. They

evaluate it better because they've

closed 300 deals and they've

internalized which representations and

warranties actually get litigated and

which ones are boilerplate that nobody

ever enforces. This is experience and

pattern recognition. knowledge

accumulated through years of repetition.

It's not really novel reasoning. Models

are getting better at simulating domain

expertise through training data, but the

gap between has read about it and has

lived it in the courtroom is still very

real, particularly in domains with thin

published literature. And here's a last

one. Ambiguity problems. Deciding what

to build when the market signal is

contradictory. Defining strategy when

three plausible interpretations of

customer data exist and each one leads

to a different product roadmap. Figuring

out what the customer actually wants

when they can't articulate it

themselves. They say they want better

reporting, but they actually want their

boss to stop questioning their numbers.

The hard part is not computing an answer

here. The hard part is figuring out what

the question actually is. This is the

domain of product sense, strategic

intuition, and the ability to hold

multiple incomplete mental models in

tension until one of them resolves.

Models can help explore options here,

but they cannot resolve the ambiguity

because it's not computable ambiguity.

Now, and this is the critical piece,

look at those six different problem

types I just described and ask yourself,

which ones does a dramatic improvement

in pure reasoning actually help? Be

honest. Reasoning helps reasoning

problems. That's obvious. The Gemini

Deep Think results are pure wins for the

reasoning axis. They're enormously

valuable problems because a single

insight in drug discovery can be worth

billions of dollars. A breakthrough in

material science can reshape an entire

industry. A novel proof can unlock an

entire new branch of mathematics. The

problems are some of the highest value

problems we humans work on. So, it's not

that Google isn't tackling things that

are valuable, but we should ask whether

they're tackling things that are used in

daily work. Now, to be fair, pure

reasoning problems do exist in

mainstream business. They're just rarer

and much more specialized than people

tend to assume. I think we sometimes

think a lot of business is reasoning

because we like to flatter ourselves.

It's not. Here's an example of a few

reasoning problems that are real in

business. Multi-jurisdiction tax

optimization is an example of a genuine

reasoning problem. The tax codes across

say 12 countries are all known inputs.

The question is very well defined but

the interaction effects between them

create a combinatorial space

mathematically that is genuinely hard to

reason through. Complex derivative

pricing that's another one. So is novel

regulatory compliance. Not read these

3,000 contracts. That's an effort

problem. But does this new financial

instrument trigger reporting obligations

simultaneously under say DoddFrank,

Basil 3, and the Hong Kong SFC's updated

guidelines? That's multi-step logical

deduction across interacting rule

systems. And it's the kind of thing

Gemini 3.1 Pro on high would handle

really well. Structural fraud detection,

not machine learning pattern

classification, but tracing a chain of

seven transactions across four entities

and reasoning about whether the

structure implies layered money

movement. That is a reasoning problem.

But I want you to notice the pattern in

these ones I described. These business

reasoning problems cluster in

specialized quantitative domains that

look a lot more like applied science

than most of the knowledge work that you

and I do. Did you notice none of them is

coding? And critically, the people who

do this work spend most of their time on

everything except the reasoning. The tax

attorney spends maybe 10% of her week on

the genuine multi-jurisdiction

interaction puzzle and 90% on client

management, document gathering,

coordination with local council,

navigating ambiguity about what the

client actually wants to achieve, etc.

The supply chain director's hardest

problem is not the multiconstrain

optimization path. It's actually getting

three different vice presidents to agree

on demand forecasts before the math can

even get started. In each of these

cases, the reasoning slice is real and

it's high value, but it's embedded

inside a much larger mass of effort, of

coordination, of ambiguity type work,

which means that a model optimized for

pure reasoning is a tool that helps with

the most intellectually demanding 10% of

these roles. But a model optimized for

tools in sustained work ends up helping

with the other 90%. For most knowledge

workers on most days, for most of us,

the problems we face are hard on effort.

They're hard on coordination. They're

hard on emotional intelligence. They're

hard on ambiguity. They're tough on

domain expertise. But the pure reasoning

component, that's a really narrow slice.

I don't have a precise number and I'm

very skeptical of anyone who claims to,

but I do know this. When I look at my

own work, the moments when someone says,

"I need to think harder about this." are

vastly outnumbered by the moments when

someone says, "I need to coordinate with

20 people on this," or, "I need to get

through all of this," or, "I need to

figure out what we're actually trying to

do here," or, "We need to get aligned."

That's why I think Opus 4.6 is going to

end up getting more daily usage in the

office. And I think Google can live with

that. Google would rather you use

Gemini, of course, they're not

indifferent. They have a cloud business

to grow and an ecosystem they want to

feed. But their AI research program

doesn't depend on winning your daily

workflow like it does for Anthropic.

Google is competing for the periodic

moment when a problem shows up that

requires deep novel reasoning. And in

that moment, they want to be the best

and they want to be the cheapest.

They're also positioning for the

scientific frontier, where pure

reasoning problems are dense, where the

payoffs are measured in Nobel prizes and

trillion dollar industries, and where

Google's vertical stack from TPU silicon

to deep mind research gives them a

pipeline nobody else can match. The rest

of the time, you'll probably use Claude

or Chat GPT and Google will sell the

TPUs that some of those models run on.

So, what does this mean for you and me

tomorrow? Here's where it gets really

applicable to work. Three things I want

to call out. First, stop looking at

benchmarks and start mapping traction in

your domain. I've said stop looking at

benchmarks before. Here's what I mean by

traction. What matters to you should be

which model handles the specific tasks

in your specific workflow most reliably

and that's all that should matter and

you should be the expert on that. Are

you the smartest person in your field

about which AI model handles which test

type for you? Because you should be

because the gap between I use chat GPT

for everything and hey I route financial

modeling to Gemini on high thinking. I

route coding to claude code. I route

quick research to Gemini flash and I do

deep document analysis with Opus. That

gap is the difference between commodity

usage and actual leverage. The models

have differentiated enough at this point

that model routing is its own skill set

and nobody's going to build that routing

mile for your domain and your business

except you. A cardiovascular surgeon is

going to route differently and yes, they

will use AI from a supply chain analyst

routing differently from a creative

director. The task to model mapping is

very domain specific. And it's the kind

of practical knowledge that compounds

every single week as the models get

better. You should be the expert. And

yes, I'm going to put together guides

for this that I'll put on the Substack

to help you get there. Second, start

disentangling the dimensions of

difficulty in your work. What in your

world is genuinely bottlenecked by

reasoning? What's bottlenecked by effort

or coordination or emotional

intelligence? By domain expertise, by

ambiguity, by something I haven't even

named yet. Maybe it's political risk or

regulatory uncertainty or talent

scarcity. This problem decomposition

matters because each dimension is

getting automated on a very different

timeline at a different rate by

different tools. Pure reasoning problems

are getting dramatically easier to solve

right now. That's what the ARC AGI2

score doubling in 3 months means. But

effort problems are getting automated in

a different way. They're getting

automated by agentic models that sustain

work for hours or days. Think Opus 4.6

or Codex 5.3 encoding problems.

Coordination problems are starting to

yield to agent teams and tool augmented

orchestration. Domain expertise is

slowly being absorbed into the training

data. Although the gap between I've

actually done it and I've just thought

about it, that's still a real thing. And

we find that that's why we need some

very good engineers and very good

staffers who have real lived experience

on the ground at a senior level.

Emotional intelligence, judgment,

ambiguity, courage, those are not

problems touched by AI today. Those will

be the last dimensions to yield if at

all. And this is where your map of work

matters. If you know that most of your

value comes from axes that AI isn't

automating, frankly, you can sleep a

little better, but you should also be

smarter about where you allocate AI on

the pieces that are tractable with AI.

If you discover that most of your value

comes from the reasoning axis or the

effort axis, you need to move

deliberately toward dimensions where

human judgment dominates and you need to

get really good at routing your work to

tools that are good at reasoning or good

at sustained work for hours or days. If

you're like, I don't know, how do I do

this? I will put together a promptable

guide for you. But you can't predict

which parts of your value are durable

and which are dissolving if you don't

think about it. If you don't engage with

it, if you don't decompose your work

into this type of difficulty, and I wish

the model makers would make this easier

by talking honestly about what their

models are good at and what they're not,

but right now we mostly have them

bragging about benchmark scores. and you

get the impression they're getting

generally smarter and you get confused

and you wonder, well, if Gemini 3.1 Pro

is the best in the world, why is it not

good at managing teams of agents?

Because that's a different kind of

intelligence and frankly, they're not

optimizing for it. Third task, build the

taste, and yes, it's a buildable skill

to evaluate AI output in your domain.

Every model improvement is making this

question more urgent for you, not less.

When Opus can sustain autonomous coding

for weeks and Gemini can reason through

novel logic problems, the question is

not can AI do it. Increasingly the

question is can you tell whether what AI

produced is actually good. Lisa Carbone

is a mathematician at Ruters and she

used Gemini deep think to review a

highly technical mathematics paper and

it caught a subtle logical flaw that had

passed human peer review. Look, that's

very impressive for the model. But

notice what it required from Carbone.

The judgment to know which paper to

review, the expertise to evaluate

whether the model's finding was correct,

and the domain authority to act on the

result. The model did find the flaw. The

human had to validate that and give the

model the task. Both steps were

necessary. Neither was sufficient by

itself. that judgment, the ability to

look at a financial model and know the

assumptions are wrong, to read a legal

analysis and spot the missing precedent,

that is a skill that continues to

compound. Every other skill is getting

cheaper. But that one, that one's

getting more valuable because the models

are getting better at generating very

plausible looking output that requires

genuine expertise to dig into and check

and verify. And so, yes, while I'm

putting together some guides that will

help you dig in, I want to emphasize you

don't necessarily need my guides. The

point is your work and your thinking.

You need the work. You need to do the

work of applying whatever materials you

get, my guide, something else, YouTube,

to figure out what in your world is

actually good AI output. So yes, I'm

building guides that go deeper on model

routing by domain, that go deeper on

problem access mapping because I want

this to be easier and I haven't seen

them anywhere else. But the work of

applying them to your world, that's your

work. that's always been your work and

there's no substitute for it and it's

incredibly valuable right now. I want to

step back and I want to look at Google's

quiet game here. There's a version of

the AI story that's all about speed,

that's all about market share, who ships

the fastest, who wins the enterprise,

who reaches a billion users. That's the

story that OpenAI and Anthropic are

living. It's an important story. The

products they're building are changing

how we work and how we live all the

time. But there's another version of the

AI story, and that's the version where a

company backed by a hundred billion

dollars in annual cash flow is running

on proprietary silicon that it designs

and manufactures, employing a team that

won a Nobel Prize, and operating under a

CEO who has been saying, "Solve

intelligence since long before other

people took AI seriously." That company

isn't trying to win the product race.

That company is Google and they think

the product race is a little bit of a

sideshow. The main event is intelligence

itself. And if you solve intelligence,

the products take care of themselves.

Gemini 3.1 Pro is ultimately a marker on

that road. It is the purest reasoning

model available at scale at the lowest

price from the only company with the

infrastructure to keep pushing the

reasoning frontier indefinitely. It will

not be the most used model this month.

Claude will handle more daily tasks. I

think chat GPT may well have more daily

active users for a long time to come.

Google would prefer that to be

otherwise, but they can afford to be

very patient because they're building

the thing underneath the thing. The

engine that disproves conjectures, the

engine that discovers drugbinding sites,

the engine that catches errors in

peer-reviewed papers, and that pushes

the boundary of what thinking means when

a machine does it for you. The practical

takeaway is not which model to use. Lots

of other YouTube videos will tell you

that. I'm not here to tell you that.

It's that the model landscape has

differentiated clearly enough about

which AI I should use that that is

actually becoming the wrong question to

ask. The right question is which AI

should I use for which problem? And how

do I even know what kind of problem I'm

solving? Is it a reasoning problem? Is

it an effort problem? Is it a

coordination problem? Is it an ambiguity

problem? Each one has a different best

tool, a different automation timeline,

and a different implication for your

career. Get specific. Build a map to

your domain and what problems are AI

tractable with which models because the

tools are now specific enough to reward

that. And the people who route them well

are going to way outperform the people

who use one model for everything. And

that margin is going to widen every

single month. Look, the fog around the

AI race remains thick. It is hard to get

signal but we can see enough to see

this. We know that routing the model for

the work makes a difference. So let's

not make it complicated. Let's not sit

there and stress about whether Gemini

3.1 Pro is the best and I have to switch

everything to that. That is the wrong

question to ask. Just ask what is the

kind of problem I'm facing? What is the

model at the frontier that I need to use

for that kind of problem? And by the

way, some models, especially effort

problems, they don't even need a model

at the frontier. You can use a dumb

model for that, and that's totally fine.

One of the big skills going forward is

going to be learning when you need a

smart model or not. So, that is Gemini

3.1 Pro. It is indeed the smartest model

on the planet, and I don't think Google

cares all that much whether you use it

at work tomorrow or not. Tears.