BENEDICT EVANS – SPRING 2026 AI DECK
The Real Question Is Not Whether AI Matters. It Is Where The Money Will Be Made.
Executive Summary
Evans' Core Thesis
"We know AI is important. We do not yet know where the durable value will accrue."
AI is rapidly becoming one of the most important technology shifts in decades.
However, while adoption is accelerating, the industry's economic structure remains unclear.
The key uncertainty is no longer:
✗ Will AI matter?
The key uncertainty is:
→ Who captures the profits?
Seven Key Insights
1. Technology Revolution ≠ Business Model Revolution
Where We Are Today
Key Observation
The technology is no longer the question.
The business model is.
Many future winners may not even exist today.
Strategic Implication
  • Usage does not equal profits
  • Adoption does not equal defensibility
  • Technology leadership does not guarantee value capture
2. The Great AI Spending Race
What Is Happening?
Hyperscalers are spending hundreds of billions of dollars on AI infrastructure.
  • Microsoft.
  • Google.
  • Amazon.
  • Meta.
The spending continues despite uncertain ROI.
Why?
Simple.
Overspending can be fixed.
Missing the next platform shift cannot.
Historical Parallel
  • Broadband
  • Cloud
  • Mobile
  • AI
Every major technology cycle produced a similar investment surge.
Strategic Implication
Expect:
  • More infrastructure
  • Falling costs
  • Better tools
  • Easier access
Do not automatically expect:
  • Profits
  • Sustainable moats
  • Attractive returns on capital
3. The Most Important Slide In The Deck
Models May Become Infrastructure
Most discussions focus on:
  • OpenAI
  • Anthropic
  • Google
  • Meta
  • xAI
Evans argues this may be the wrong place to look.
What Happens If Models Converge?
The model becomes:
Electricity.
The value shifts to:
Appliances.
Potential AI Value Stack
Strategic Implication
The largest AI businesses may not be model companies.
They may be workflow companies.
4. Coding Is The First Proven AI Use Case
Why has software development moved first?
Because success is measurable.
Coding Has Three Characteristics
  • Output is measurable
  • Feedback is immediate
  • Success is obvious
Compare that with:
  • Strategy
  • Marketing
  • Legal
  • Management
where outcomes are harder to measure.
Strategic Implication
AI adoption spreads fastest where ROI can be quantified.
5. AI Creates Infinite Interns
Perhaps the most useful mental model in the deck.
Think of AI as an unlimited supply of:
  • Analysts
  • Researchers
  • Assistants
  • Paralegals
  • Junior Developers
The New Question
Instead of asking:
"How many employees do we need?"
Ask:
"How many AI-assisted workers can one expert supervise?"
Strategic Implication
Future organisations may be built around:
Expert + AI Team
rather than
Manager + Large Human Team
6. Tasks Change Faster Than Jobs
A critical distinction.
Jobs
A job is a collection of:
  • Judgement
  • Accountability
  • Coordination
  • Persuasion
  • Decision-making
Tasks
Tasks are:
  • Summarising
  • Drafting
  • Researching
  • Analysing
  • Coding
AI attacks tasks.
Not necessarily jobs.
Strategic Implication
Most professions are more likely to evolve than disappear.
7. The Training Problem
This may be the most under-discussed AI challenge.
Historically:
Junior Analyst → Senior Analyst → Portfolio Manager
Junior Lawyer → Associate → Partner
Junior Engineer → Architect
The Question
If AI performs junior work:
Who trains future experts?
Strategic Implication
AI may improve short-term productivity while creating long-term capability risks.
Many organisations have not yet thought through this problem.
The AI Value Capture Framework
The deck ultimately revolves around one framework.
Where Will Value Accrue?
Infrastructure Layer
Chips. Cloud. Models. Data Centres.
Characteristics:
  • Capital intensive
  • Scale driven
  • Risk of commoditisation
Application Layer
Workflow software. Industry solutions. Operational tools.
Characteristics:
  • Customer relationships
  • Embedded workflows
  • Switching costs
Potentially the most attractive layer.
Data Layer
Proprietary operational data.
Characteristics:
  • Hard to replicate
  • Improves products over time
  • Creates compounding advantages
Distribution Layer
Customer access. Trust. Brand. Ecosystems.
Characteristics:
  • Lower acquisition costs
  • Stronger moats
  • Better economics
Final Takeaway
The market currently values AI companies based on intelligence.
History suggests the largest businesses are usually built on:
  • Workflow Ownership
  • Distribution
  • Proprietary Data
  • Customer Lock-In
rather than technology alone.
If that pattern repeats, the biggest AI winners of the 2030s may look less like AI labs and more like industry-specific infrastructure and workflow companies.
The most important question is not:
"Which model wins?"
The more important question is:
"Where in the AI value chain will durable profits ultimately reside?"