Anthropics Timeline vs. Your 2006 Database
Inside: AI Companies are built ground up to excel with AI. Most companies are not, and that's the gap. And in this gap, I see a massive opportunity for you.
Dario Amodei, CEO, Anthropic says software engineers have 6-12 months left.
Meanwhile, I just left a meeting last week where a $1B+ company spent 90 minutes arguing about whether “active customer” means someone who bought in the last 30 days or 90 days.
Different teams.
Different definitions.
Different databases.
These are not the same timeline.
The Anthropic Reality
At Anthropic, engineers stopped writing code because their stack was built for AI from day one. Clean data. Modern architecture. No legacy anything.
The Enterprise Reality
At most companies, the Head of Data is still explaining why you can’t just “put everything in a vector database” when nobody agrees on what an active customer is.
This isn’t about AI capability. It’s about infrastructure debt that’s 15 - 30 years deep.
The Real Gap
Most of my time with customers these days are spent on talking about AI Readiness.
You know what blocks them? Not models. Not talent. Not budget.
It’s that nobody can answer basic questions:
Where is the source of truth for customer data?
Can we access it in real-time or only batch?
Do we have lineage? Do we have versioning?
Can we trace decisions back to their data sources?
AI-native companies designed from scratch for these questions.
Everyone else is retrofitting.
The Opportunity in the Gap
Here’s what’s interesting about this moment.
AI companies built for a world that doesn’t exist yet.
Enterprises are still operating in the world that does.
Someone has to bridge that gap.
And right now, AI companies are realizing they can’t do it alone. Anthropic can’t retrofit your 2009 database. OpenAI can’t untangle your customer data across 12 systems.
This is where the real opportunity is.
Not in building better models.
In building the infrastructure that lets models actually work.
If you know:
How to design cloud infrastructure
How to build data platforms
How industry-specific workflows actually operate
How to translate technical requirements to business outcomes
You’re not behind. You’re exactly where the market needs you.
But here’s what you need to learn - and learn fast:
The gap between what models can do and what enterprises can actually deploy.
Because that gap is the entire business for the next 5 years.
Most people think they need to learn prompt engineering or RAG architectures.
What they actually need to learn is why a large enterprise can’t answer “what’s this customer’s balance?” without a nightly batch job.
And how to fix that before the AI even shows up.
The window’s still open. But it’s closing fast.
Not because AI is getting harder. Because the people who understand both worlds - AI capability AND enterprise reality - are getting snatched up.
The question isn’t whether you can catch up to Anthropic.
The question is whether you can help enterprises catch up to what AI requires.
That’s the real opportunity.
Next Week
I am releasing a plan for you to prepare for this opportunity.
Make sure you don’t miss that.
Ask your friends to join.
More valuable content coming your way.