A 200line prompt just killed $285

billion in market value. That's right. A

markdown file, not a product, not a

platform, a markdown file from some

product manager at Anthropic erased $285

billion in market cap on the stock

market in just 48 hours. On January

30th, Anthropic released a set of

plugins for Claude Co-work, its desktop

AI tool. One of them handles legal

contract review. It can triage NDAs. It

can flag non-standard clauses against a

negotiation playbook and generate a ton

of compliance summaries. The kind of

work that until last week required a

parallegal, maybe a Westlaw

subscription, something that had to do

with billable hours, right? The plugin

is open source. Anyone can read it. And

when people did, they found roughly 200

lines of structured markdown prompts.

First year law school content dressed up

with some clever workflow logic. It's

basically a fancy prompt. It shipped

with a little disclaimer. All outputs

should be reviewed by licensed

attorneys. I'll bet by Monday morning.

Thompson Reuters had posted its biggest

single day stock decline on record. It's

down 16%. RELX, the parent company of

Lexus Nexus, fell 14%. Legal Zoom

cratered at 20%. You get the idea. The

selling spread to private equity from

there. Aries Management, KKR, and TPG

all dropped about 10%. If AI compresses

the cost of legal and financial

analysis, then every firm charging

premium fees for that analysis has a big

big margin problem because they can't

charge that much. Not next year and

maybe not now. But here's what almost

nobody is saying clearly enough. The

markdown file itself is not the cause.

It just revealed what has been going on

for a while. the per seat SAS licensing

model, the financial bedrock that the

entire enterprise software economy has

been built on for 20 years, it was

already cracking. The market just hadn't

priced it in yet because frankly, Wall

Street doesn't understand AI that well.

So, this crash wasn't really about

Claude. And we should be precise about

what actually happened because the

narrative has already crystallized into

anthropic crash to the software market.

And that framing, while it's fun for

headlines, misses the real structural

story. What Anthropic actually shipped

was a set of open source starter

plugins, basically templates that any

company can customize for their own

workflows very easily. The legal plugin

was one of 11. It was very competent,

but it's not by itself revolutionary.

Any decent prompt engineer could have

assembled something comparable in an

afternoon. So why did it move $285

billion? because the plugin made visible

what the market has been quietly

worrying about for months. If a text

file can approximate the core workflow

of a $60 billion revenue legal

information industry, then that whole

pricing model that the industry is built

on has a big structural problem. Not a

competitive problem, not a better

product, a structural problem. The kind

that doesn't get solved by shipping

faster or hiring better salespeople.

Thompson Reuters charges per seat. Lexus

Nexus charges per seat. Westlaw charges

per seat. The entire enterprise software

economy from Salesforce to Service Now

to Adobe runs on a model that says every

human who touches this tool must pay a

license fee. That's how these companies

make their money. That's how they

forecast their revenue. That's how Wall

Street values them. That model works

when humans are the bottleneck. It

breaks when AI agents can do the work

without logging in. And the signals were

already everywhere if you knew where to

look. The software industry's average

forward price to earnings ratio has been

compressing for months from X 8 months

ago to about 2x right when the sell-off

hit. That is the largest 4-month

valuation compression since the 2002.com

bust. Earning season has already been

ugly. Software companies are missing

revenue estimates at rates not seen

since the postcoid correction and

broader tech continues to beat. The AI

companies are fine, right? The per seat

model was under pressure before

anthropic shipped this little prompt

file. So the cloud plugin, it didn't

start the fire. It just showed everyone

the building was already burning. Now I

got to be honest, plenty of smart people

think that this sell-off is a big

overreaction. And they might be right

about the selloff, but they would be

wrong about what it means. Jensen Hang

speaking at the Cisco AI Summit a few

days before the crash offered the

strongest version of the

counterargument. This notion that the

software industry is in decline and

being replaced by AI is the most

illogical thing in the world, he said.

And do you know why? Hong's argument is

very simple. AI doesn't replace

software. AI runs on software. The more

AI agents you deploy, the more software

infrastructure you need. More databases,

more APIs, more middleware, etc. So

every AI agent that replaces a

parallegal still needs West Law's data.

It still needs a CRM. It still needs

document management. If anything, AI

should increase the total amount of

software the economy uses. Jensen's not

wrong. He's also not making the argument

he thinks he's making. Nobody's serious

is arguing that the world needs less

software. The argument is that the world

no longer needs to pay for software the

way it currently pays for software. So

Jensen is defending the product and he's

right to do so. The market is attacking

the pricing model. Those are very

different things and confusing them is

how incumbents lose transitions they

should have survived. Print media made

this same mistake. Newspapers had

content people wanted. They had local

information, investigative journalism,

weather. The internet didn't make that

content worthless. What the internet did

was destroy the access model. the idea

that you had to buy a whole newspaper to

get the one section you cared about and

that advertisers would pay premium rates

to reach readers with no alternative.

The content actually survived. The

business model didn't. And that's why so

many newspapers are in trouble. Print

media's content did eventually get

commoditized. Anybody can publish now.

software's content like proprietary

databases like structured workflows,

decades of accumulated enterprise data

that hasn't been commoditized and it

actually probably won't be. Thompson

Reuters case law database isn't

something a startup vibe codes in a

weekend. Salesforce's customer

relationship data is irreplaceable for

many of their clients. Adobe's creative

tool ecosystem has a pretty deep moat.

So the data is safe, but the per seat

access model for that data is not. And

the companies whose entire financial

identity is built around per seat

licensing, they're about to face the

hardest strategic question in enterprise

software. How do you repric your most

valuable assets without destroying your

revenue in the transition? Bank of

America's Vivic Arya published the most

revealing analysis of the crash. He

called the sell-off internally

inconsistent. And he's right in a way

that tells you something important about

where the market's head is at right now

on software and on AI. Investors were

simultaneously running two thesis.

Thesis one, AI infrastructure spending

is unsustainable and the capex boom will

collapse. Thesis 2, AI adoption will be

so powerful that it renders established

software business models obsolete. Both

cannot be true. If AI is powerful enough

to crash $285 billion in software market

cap, the infrastructure required to run

that AI is underbuilt, not overbuilt,

the SAS apocalypse is paradoxically the

strongest possible demand signal for

continued AI infrastructure investment.

And yet both trades were profitable in

different hands at different moments.

The Deep Seek sell-off punished Nvidia

last year. The SAS correction punished

Salesforce at almost the same time this

year. Wall Street does not resolve

logical contradictions. It rotates

between them. One week the market prices

in an AI winter, the next it prices in

an AI revolution so total that legacy

software can't survive it. The

contradiction persists because no single

firm needs to hold both positions. The

market as a whole holds them and the

market as a whole has no obligation to

be coherent. The incoherence is the real

story, not the crash, the incoherence.

But this is not really a story about

stocks. It's bigger. While everyone was

watching Thompson Reuters stock price, a

quieter story broke that almost no one

paid attention to. And that tells you

about where we're all headed more than

any given stock chart. KPMG, one of the

big four accounting firms, pressured

Grant Thornton UK, which is its own

auditor. Yes, the big four have to have

auditors, to cut their audit fees. The

demand was to pass on cost savings from

AI. Grant Thornton initially resisted,

arguing that quote, "High highquality

audits rely heavily on expert human

judgment and that fees reflect the cost

of people." PMG's response, "Per the

Financial Times, lower your prices or

we'll find a new auditor." And Grant

Thornton blinked. PMG's international

audit fees dropped from $416,000 in 2024

to just $357,000

in 2025. They got a 14% discount. And

that story matters to me more than

Thompson Reuters stock price. And I want

to tell you why. The SAS apocalypse was

just a market event. Traders were

repricing stocks based on a change view

of the future. They do that all the

time. The KPMG negotiation is an

operating event. A real company using AI

as a lever in a real business

negotiation to extract a real price

reduction from a real counterparty. The

stock market repricing that could

reverse tomorrow. The KPMG precedent

won't. Think about what KPMG actually

did. They didn't automate their audit.

They didn't replace Grand Thornton with

AI. They used the existence of AI. The

fact that everyone now know these now

knows these tasks can be done more

cheaply as a negotiating weapon. The

threat isn't we'll replace you with AI.

The threat is we both know AI changes

the economics. So your old prices,

they're not justified anymore. That's

the playbook and it works in every

knowledge work fee negotiation. Now if

audit fees get renegotiated on the basis

of AI cost savings, legal fees can be

next, then consulting fees, then

implementation fees, then design fees,

then every form of pro-services billing

that currently scales only with the

number of humans touching the work. You

cannot use that scaling assumption. Lean

teams are the future. The cascade

doesn't require anyone to actually

deploy AI at scale. It just requires

buyers to point at that SAS apocalypse

and say, "We know the world changed. So,

let's talk about your assumption that

the work is done per human and let's

talk about your rates." The big four are

a sign of things to come. When they talk

about not automating their own work, but

just negotiating down the cost of

services, that is a big operating

mechanism that is going to shake the

industry. It's not really the markdown

files. its fee negotiation leverage

spreading like wildfires through the

professional services economy like a

crack through an iceberg. All of those

assumptions that humans have to do the

work are shattering. The software did

not die. The data systems underneath

enterprise software, Thompson Reuters

case law databases, Salesforce's

customer graphs, SAP's resource planning

lo SAP's resource planning logic,

Adobe's creative workflow ecosystem.

Those all represent decades of

accumulated, structured, proprietary

information that no markdown file comes

close to replacing. Those data systems

will continue to exist. They must. The

economy runs on them. And there's a

second edge that the market panic has

really overlooked, the single ringable

neck. Enterprises don't just buy

Salesforce because it's the best

possible CRM. You can make the case for

a lot of other software that's better.

They buy Salesforce because when

something goes wrong at 2 am on the

night before the board meeting, there's

a phone number to call and a contract

that says somebody is accountable. That

accountability layer, the vendor

relationship, the SLA, the legal

liability, the proservices team that

shows up when the system breaks, that is

enormously valuable to big

organizations. And no amount of agentic

AI eliminates the need for it. If

anything, the complexity of AIdriven

workflows makes that accountability even

more important, not less. So, the data

edge is real. The accountability edge is

real. What died is the pricing model

that sits over the top. The idea that

you can charge every human who touches

the software a nice convenient fat per

se license fee and that your revenue

scales linearly with that headcount. If

one AI agent can do the research that

previously required 10 parallegals with

10 separate Westlaw loginins, Thompson

Reuters doesn't lose the value of their

data, they lose nine seats of revenue.

The data becomes actually more important

in an AIdriven world. It's the fuel the

agents run on. But the per seat access

model, that's just broken. Here's what

the investor thesis actually comes down

to. The markdown file represents an

existential threat if and only if these

SAS companies run business as usual. If

they just bolt AI features on top of

their existing UI, if they just add a

chatbot, then the market's right.

They're dead. The market is right to

repric them. The survival path is

actually fundamentally different. And

it's the one Thompson Reuters,

ironically, is attempting with co-consel

to pivot from a one-sizefits-all

interface that humans navigate to an

agentic first architecture that AI

agents navigate and charge for the value

of the data and the accountability

rather than the number of humans logging

in. That's not a feature update. That's

a rebuild of the product, the pricing,

and the go to market simultaneously

while your stock price is cratering.

Whether the incumbents pull it off is a

$285 billion question. Literally, they

have the data edge, they have the

ringable neck edge, and those are real.

But pivoting from UI first to agentic

first is the kind of architectural

transformation that does tend to kill

companies that attempt it too slowly.

And the clock is running at a speed that

nobody in enterprise software has ever

experienced. There's a second angle to

this that most SAS apocalypse analysis

completely misses and it might matter

even more than the pricing question.

Think about what enterprise software

companies really spend their money on.

Engineering. Thousands of developers

maintaining, updating, debugging, and

extending one-sizefits-all platforms

designed to serve every possible

customer configuration. Docuine employs

thousands of developers. That's the real

cost of enterprise SAS. Not the servers,

not the sales team, but the army of

engineers keeping a general purpose

system alive for millions of users who

each use it just a little bit

differently. Now, think about the

opportunity cost. Every developer

maintaining a legacy SAS UI is a

developer that is not building custom

agentic workflows. Every sprint spent

adding features to a one-sizefits-all

product is a sprint not spent rethinking

the product for an agent first world.

The companies that crashed this week,

they're not just facing a pricing model

crisis, they're facing a resource

allocation crisis. Their most valuable

people are maintaining the old thing

when they need to be building the new

thing desperately. And the transition

requires doing both of those

simultaneously within the same budget.

This is where Agentic software

engineering changes the math in a way

that most people haven't fully

internalized. The cost of building

software is falling to zero. Not slowly

and not theoretically. It's happening

right now. Cursor shipped a system that

generates a thousand code commits per

hour with no human involvement. Strong

DM published a production framework that

states code must not be written by

humans and code must not be reviewed by

humans. That is not laughable. In 2026,

that is what is happening. A researcher

at OpenAI spent $10,000 on codeex tokens

and automated his entire research

workflow. These aren't demos. These are

operational systems running in

production. When building software cost

starts to approach zero, the economics

of buy versus build flip for the first

time in a long time. The entire

enterprise SAS value proposition was

predicated on the idea that it's cheaper

to buy a general purpose tool than to

build a custom one. That was true when

software engineering was expensive and

slow. When an AI agent can build a

custom CRM in an afternoon, calculus can

reverse for some folks. Why pay

Salesforce per seat fees for a tool

designed to serve every company on earth

when you could have a tool designed to

serve your company? That is the promise

of vibe coding. That is the promise of

vibe engineering. Now you might wonder

is that how it actually works? The

honest answer is it depends. And what it

depends on is the hardest problem in the

entire stack. It's harder than

intelligence. It's harder than coding.

And it's harder than pricing models. It

depends on whether an AI agent can take

the vague, implicit, half-articulated

thing a human actually wants and turn it

not just into workable software, but

very quickly into workable software with

minimal sustainment costs. I've

mentioned in a previous video that I am

skeptical of this long term, especially

for enterprises. Remember how we talked

about companies hiring for a single

ringable knack and paying for enterprise

data access? Those remain edges. And

anyone who wants to engineer their way

forward into a cheaper CRM and not

Salesforce must confront them. But they

also must confront the articulation

problem. And that is a real bottleneck.

Not just for SAS companies, but for

anyone who wants to build your own

alternative. When a VP of sales says, I

need a better way to track the pipeline.

That sentence contains less than 5% of

the information required to build a

useful tool. Frankly, less than 1%. The

other 95 or 99% is buried in how the

team actually works. What the unspoken

conventions are, which exceptions matter

and which don't, how this quarter's

priorities differed from last, what

better means in context. Now, a skilled

product manager will spend weeks

extracting that information through

interviews, observation, iteration.

Whether an agent can do the same thing,

not just write the code, but understand

the need deeply enough to write the

right code is one of the biggest

questions in software right now. I am

skeptical that we're there yet, except

in a few cases where you have

extraordinary context availability

across the enterprise. But Agentic

Search is making progress on exactly

that problem. Agents can explore

context. They can ask clarifying

questions and they do now. And they can

observe usage patterns and iteratively

refine their understanding of what a

human actually needs. So, it's starting

to come, but the question is timing. For

SAS incumbents, this means the window

has not yet closed. Their data edge and

their accountability edge really do buy

them time, but only if they use that

time to pivot to Agentic first, rather

than bolting AI onto the existing UI and

saying a prayer. Here's the thing that

connects the SAS apocalypse to your

actual life. The same dynamic that is

threatening enterprise SAS companies,

the difference between bolting AI on top

of your existing approach and actually

rethinking how you work from the ground

up applies to every individual knowledge

worker that is watching this video. If

you're using chat GPT to proofread

emails you could have written anyway,

you are bolting AI on the top. If you're

using Claude to summarize documents you

could have read anyway, you're bolting

AI on the top. If you add Copilot to

your IDE, but your development workflow

is just the same as it was two years ago

or even five months ago, you're bolting

AI on the top. And just like the SAS

companies that are bolting AI features

onto their existing products and hoping

the market does not notice, you are

decorating a structural problem in your

own career rather than solving it. The

pace right now is almost

incomprehensible. 20 minutes after Opus

4.6 dropped, Codeex dropped. And Codeex

can ship entire desktop apps if properly

prompted end to end from scratch. OpenAI

isn't done with Codeex though. They also

launched Frontier in the same week as

they dropped Codex 5.3. Frontier is an

enterprise agent platform. So that means

that it you can use Frontier to deploy

enterprise agents securely across your

entire data ecosystem. Remember when I

said that context was evolving and

agents were getting better at searching

for context and learning from context

clues how to build good software?

Frontier is part of why Claude Co has

gone from an interesting demo to a $285

billion market event. If you ask an AI

model right now to help you figure out

how to use AI, you will get advice

that's 6 months out of date. Even the AI

cannot keep up with itself. This is what

hyper acceleration feels like. And that

word does sound like marketing. It

sounds like hype. I will have people in

the comments who say I'm overhyping, but

you got to live through it. And then it

sounds like another Tuesday. The gap

between I use AI tools and I've

rethought how I work around extremely

rapidly evolving AI capabilities is all

of our individual versions of what

happened in the SAS market. The first

approach feels really productive.

Bolting on AI lets you feel like you're

keeping up. The second approach

fundamentally rethinking how you work

from the ground up. That's what changes

outcomes and the window to make that

transition keeps compressing every time

there's a new update, which frankly is

every few days. If you haven't tried

Opus 4.6 and experienced what a good

million token context window feels like,

you're already out of date. If you

haven't used Cloud Co-work or Codeex or

played around with OpenAI Frontier,

please try them. Not because any one

tool is the answer, but because the

experience of using these systems

changes your mental model of what's

possible. And your mental model of what

is possible is the thing that determines

whether you are bolting on AI in your

own career and praying or whether you're

rebuilding for an AI future that is

coming like a title wave. The SAS

companies that survive the SAS

apocalypse will be the ones that rethink

their architecture before the market

makes them. The knowledge workers who

thrive through the transition will be

the ones who rethink their workflows

before the boss forces them to. It's the

same dynamic. It's the same urgency.

It's just at a different scale. The per

seat SAS pricing model is broken. The

data and accountability underneath it

are not. And the same logic applies to

you. Your skills, your domain expertise,

the thing that makes you passionate

about work, that didn't break. But the

assumption that you can just take that

to work and not use AI or only use AI a

little bit or use AI in a chatbot, that

is broken. And you're going to need to

look at how you fundamentally rethink

your workflows to get there. And that is

exactly what I'm putting together in the

exercises that go with this video on my

Substack. got a bunch of exercises that

help you think about how you can take

your unique role and essentially do the

repricing, do the rebuilding that the

SAS companies are talking about, but at

individual scale for your individual

workflows, how you think about AI, not

as a bolt-on, but as a fundamental

shift. A 200line markdown file did not

decide who wins and loses, but it did

compress a transition that everybody

expected to take 5 years into a 48 hour

repricing event. And the repricing

hasn't stopped. It's just getting

started. The clock is ticking. It's not

stopping. And I want you to make good

decisions with your career. And we'll

have to see if the SAS companies make

good decisions with their futures as

companies because by the time you watch

this, whatever the stock market price

says, the AI that you hear about in this

video will already be overtaken by some

other news. That is how fast we're

moving. AI isn't stopping, and we're all

going to have to dig in to get through

this together. I know you can do it.