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Investment Thesis · February 2026
The Shift
The rules have changed. The wall between domain expertise and software development has collapsed. That changes where value sits, who captures it, and what to do about it.
Steve Turner
1
What Changed
The Wall Collapsed
Before
Domain Expertise
Requirements
Specs
Scoping
Product Mgrs
Engineers
Designers
Handoffs
Architects
Coordinators
Sprints
Meetings
Delay
Budget
Final Product
Months. Millions. Meaning lost.
Now
Domain Expertise
Direct
Final Product
Days. Direct. Nothing lost.
2
The Paradigm Shift
The Old Model vs.
The New Reality
Ideas are cheap, execution is expensive
Execution is cheap, judgment is expensive
Technical talent is the bottleneck
Problem selection is the bottleneck
Domain experts pay engineers to build
Domain experts build directly
Months to prototype
Hours to prototype
50–100 engineers for scale
Small teams at unprecedented scale
3
Context
This Feels Different
35 Years (1990s thru 2025)
Now
Tech
Revolutions
Software. Data. Internet. Mobile. Cloud.
1990sEmail, Excel, Bloomberg — faster access to information
2000sSaaS, data platforms, Salesforce — smarter workflows
2010sCloud, mobile, big data — scale everything
AI
Feels different
How They
Sold
Efficiency & intelligence
Feels fundamentally different
What
Happened
Head counts increased
Not sure
4
The Cost Collapse
Faster Than Moore’s Law
This Is Faster Than Moore's Law
Moore's Law (Transistors)
2x / 2 years
Transistor density doubles every ~2 years. Held for 50+ years.
AI Cost Curve
6x cheaper · 250x more context · 35 months
The best model on earth costs 1/6th what the frontier cost 3 years ago. Context capacity grew 250x. Reasoning went from "autocomplete" to "analyst." Simultaneously.
Moore's Law improved one dimension (density) on a predictable curve. AI is improving three dimensions simultaneously — cost, capacity, and capability — on a curve steeper than anyone predicted.
5
The Numbers
The Cost Collapse
Cost per Million Tokens
Mar 2023
$30
GPT-4
Feb 2026
$5
Claude Opus 4.6
6x
cheaper — and far more capable
Context Window
Mar 2023
4K
~6 pages
Feb 2026
1M
~1,500 pages
250x
larger in under 3 years
6
What This Means
The Stack Collapsed
The 2023 Stack — 12 layers
1 You You
2 Prompt Template Obsolete
3 Prompt Marketplace Obsolete
4 LangChain Orchestration Obsolete
5 Document Chunking Obsolete
6 Embedding Model Obsolete
7 Vector Database Obsolete
8 Retrieval Query Obsolete
9 Reranker Obsolete
10 LLM (GPT-3.5 / GPT-4) Evolved
11 Output Parser Obsolete
12 Result You
Reasoning collapses the stack
The 2026 Stack — 3 layers
You
Claude / GPT
Result
9 layers eliminated
For most bounded tasks. The reasoning model handles it now. Attach the document. Ask the question. Get the analysis.
7
What Changed
The Prompt Got Smaller. The Output Got Better.
90x
shorter prompt, better result
Early AI — 2023
~1,800 words · 47 lines · 2 full pages
SECTION 1: PERSONA
You are a senior private equity associate at a mid-market PE firm with $2-5B AUM. You have 8+ years of experience evaluating platform acquisitions in the $50M-$500M enterprise value range. You think in terms of EBITDA multiples, revenue quality, management team depth, and value creation levers. You are skeptical by default — your job is to find reasons NOT to invest, not to confirm a thesis. You understand the difference between recurring and non-recurring revenue, the importance of customer concentration, and how to evaluate management’s forward projections against historical performance.

When you analyze a deal, you think about: (1) downside protection first, (2) base case returns, (3) upside optionality. You never lead with the bull case. You always identify the “kill shot” — the single risk that would make you walk away — before evaluating the opportunity.

Your communication style is direct and concise. You write for a senior partner who has 15 minutes to decide whether this deal deserves a second look. No throat-clearing. No “it depends.” Take a position.

SECTION 2: OUTPUT FORMAT
Structure your analysis in the following format exactly:

EXECUTIVE SUMMARY (3-4 sentences. Lead with your verdict: Pass, Proceed to DD, or Conditional Proceed. State the single most compelling reason and the single biggest risk.)

BUSINESS QUALITY ASSESSMENT
— Revenue quality score (1-10) with justification
— Customer concentration analysis (top 10 customers as % of revenue)
— Recurring vs non-recurring revenue breakdown
— Organic growth rate vs. acquisition-driven growth
— Gross margin trajectory and sustainability

MANAGEMENT & OPERATIONS
— Key person risk assessment
— Bench depth below C-suite
— Operational efficiency (revenue per employee trends)
— Capital allocation history and discipline

FINANCIAL ANALYSIS
— EBITDA quality and adjustments (flag any aggressive add-backs)
— Working capital dynamics
— CapEx requirements (maintenance vs. growth)
— Free cash flow conversion rate
— Debt capacity analysis

VALUATION & RETURNS
— Implied entry multiple on adjusted EBITDA
— Comparable transactions and public comps
— Base case IRR (25th, 50th, 75th percentile)
— Key assumptions driving returns
— Sensitivity on 2-3 critical variables

VALUE CREATION PLAN
— Top 3 operational improvements with quantified impact
— Potential add-on acquisition targets
— Pricing power assessment
— Technology/automation opportunities

RISK MATRIX
— The kill shot (walk-away risk)
— Top 5 risks ranked by probability × impact
— Mitigants for each

VERDICT
— 2-sentence recommendation
— Conditions for proceeding, if applicable

SECTION 3: INSTRUCTIONS
When analyzing the CIM:
DO NOT take management projections at face value. Apply a 20-30% haircut to forward revenue projections and evaluate what the business looks like under conservative assumptions.
DO NOT use generic language like “strong market position” without specific evidence.
DO flag any EBITDA adjustments that exceed 15% of reported EBITDA as potentially aggressive.
DO calculate implied customer lifetime value if data is available.
DO identify the 2-3 metrics that matter most for this specific business and explain why.
DO compare stated growth rates against industry benchmarks.
DO NOT write more than 2 pages total. Density over length.
DO use specific numbers from the CIM. Never say “significant” when you can say “34%.”

// The goal is a document I can hand to my managing partner that gives him everything he needs in a 15-minute read to decide: do we take the next meeting or not?

SECTION 4: TONE & CONSTRAINTS
— Write at a Wharton MBA level, not a blog post level
— Use tables where they communicate faster than prose
— Bold the single most important number in each section
— If the CIM is missing critical data, flag it explicitly as a red flag
— End every section with a one-line “so what” that connects back to the investment decision
Reasoning
improves
Today — 2025/2026
~20 words · 2 lines
Analyze this CIM as if you're a senior PE associate deciding whether to recommend this deal to your managing partner. Be direct. Take a position.
That's it. Attach the CIM. Hit send.
Same output quality
The reasoning model already knows how a PE professional thinks. Every instruction from the 2-page prompt? The model has it internalized.
8
Where Value Lives
Where IP Lives Now
The Logic Layer
Domain Knowledge
AI / LLM
(Commodity)
The Logic Layer — Where IP lives
The structured reasoning that turns commodity AI into a differentiated product.
Domain Knowledge — What you know
Years of expertise, industry context, edge cases, and judgment calls.
AI / LLM — What everyone has
The engine. Powerful, but commodity.
9
Where Value Lives
Where to Invest
Low ValueHigh Value
Generic + Commodity
LLM APIs
Cloud compute
Base models
Unique + High Value
The Logic Layer
Decision frameworks
Structured workflows
Generic + Low Value
Basic prompts
Off-the-shelf tools
Generic workflows
Unique + Emerging
Domain knowledge
Expert intuition
Industry context
GenericUnique
The top-right quadrant
This is where defensible value lives. Not the AI itself — everyone has that. The structured thinking you wrap around it.
10
The Evidence
The New Economics
Cursor
AI-powered code editor
ARR$1B
Employees~300
Time to $1B~2 yrs
Rev / employee
$3.3M
Midjourney
AI image generation
Revenue$200M+
Employees~40
VC Funding$0
Rev / employee
$5M+
Bolt.new
AI full-stack app builder
ARR (est.)$40M+
Employees<50
To Product~6 mo
Rev / employee
~$800K+
Perplexity
AI-native search engine
ARR$100M+
Employees~200
Valuation$9B
Rev / employee
~$500K
Traditional SaaS: $200K–$400K revenue per employee. These companies: $500K–$5M+. The AI is commodity. The logic layer is the product.
11
Speed of Shipping
Old Playbook vs. AI-Native
DimensionTraditional (Pre-2023)AI-Native (2025+)Shift
Time to $1M ARR18–36 months3–6 months6x faster
Team to launch8–15 people1–3 people (+ AI)5x leaner
Seed capital$1M–$3M$0–$50K20x cheaper
Rev / employee$200K–$400K$1M–$5M5–25x higher
Code to shipMonths of custom devDays with AI tools10x faster
Design qualityRequires dedicated designerAI generates production UI$0 design cost
Market researchHire analysts ($50K+)AI produces analysis100x cheaper
Competitive moatCapital, headcount, brandSpeed, taste, domain expertiseStructural shift
12
What Took a Team
One Person + AI
7
Roles that would have been hired
$415K+
Annual fully-loaded cost
1
Person who did the work
~$2,400
Annual AI + API costs
Traditional Team
$415,000+ fully loaded
One Person + AI
~$2,400
173x
cost reduction. The team didn't get cheaper. It got unnecessary.
13
Industry Impact
Professional Services
Traditional Model
AI-Native Model
Junior researches precedents (hours)
AI surfaces relevant precedents (minutes)
Analyst builds financial model (days)
AI generates model from parameters (hours)
Team reviews 10,000 documents (weeks)
AI reviews and flags issues (hours)
Associate drafts contract (hours)
AI drafts from requirements (minutes)
Consultant synthesizes notes (a day)
AI synthesizes with themes (minutes)
32.5
days/yr reclaimed by lawyers using AI
60%
of in-house counsel see no savings yet
14
Evaluating Companies
AI-Enabled vs. AI-Native
AI-Enabled
AI-Native
AI added to existing product
Product impossible without AI
Traditional team with AI tools
Small team, AI handles execution
80%+ gross margins, low marginal costs
40–70% gross margins, costs scale with usage
Could exist (worse) without AI
Business model requires AI to function
Red Flags
Green Flags
Thin wrapper — nice UI on a single API call
Deep domain expertise in edge cases and workflows
No answer for model dependency
Model-agnostic architecture
"We're collecting data" with no flywheel
Clear data flywheel — usage improves product
ARR hiding negative gross margins
Honest unit economics with sustainable path
15
Winners & Losers
The Scorecard
CompanySectorVerdictBecause
PalantirEnterprise SWWinnerAI-native data platform with irreplaceable logic layers. Deep AIP adoption.
ShopifyCommerceWinnerAI integrated across merchant tools. AI-powered checkout capturing share.
CrowdStrikeCybersecurityWinnerAI makes threat detection exponentially better at scale. Data flywheel.
CursorDev ToolsWinner~300 employees, ~$1B ARR. Product is the logic layer.
KlarnaFintechFenceCut headcount aggressively, had to rehire. AI transformation requires judgment.
SalesforceEnterprise SWFenceAgentforce ambitious but seat-based model threatened by AI agents.
AdobeCreative SWFenceFirefly integrated but AI-native tools capturing low end.
CheggEducationLoserCore product (homework answers) now free via ChatGPT. No logic layer.
UpworkProf. ServicesLoserMarketplace for tasks AI can now do directly.
PearsonEducationLoserContent-as-product model exposed. AI generates and tutors from any source.
16
Capabilities Shipping Now
What’s Next
Capability
What It Enables
What It Displaces
Computer Use Agents
Automate any web workflow end-to-end without APIs
Manual ops, SaaS glue work, brittle RPA
Persistent Memory
A colleague with institutional knowledge across sessions
Onboarding overhead, tribal knowledge loss
Voice-to-Application
Describe an app, watch it build itself
Low-code platforms, offshore prototyping
Multi-Agent Orchestration
One person scales to an AI team
Cross-functional staffing, junior roles
Deep Research Agents
Analyst-grade reports with citations
Junior analysts, desk research, due diligence
Autonomous Coding
Plans, implements, tests, submits PRs
Mid-level dev teams, outsourced shops
17
Where Value Shifts
The Convergence
Year
Where the Value Is
2023 — Prompt Craft
Knowing how to talk to the AI. Structured prompts, template marketplaces.
2024 — Tool Selection
Knowing which tools to combine. Right model + right context + right workflow.
2025 — Judgment & Taste
AI can build anything. Value is knowing what to build, for whom, and why.
2026 — Orchestration
Directing AI teams toward complex goals. Management of machines, not people.
Signal
Implication
Biggest opportunity
Domain experts who build. 10+ years in a field plus AI execution speed. The combination is unfair.
Watch closely
The middle management layer. Roles that coordinate and translate requirements. AI agents are learning to do exactly this.
AI capabilities aren't launching like software — they're compounding like interest. Each wave makes the next one faster.
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Winners & Losers
The AI Graveyard
2023 — 2026

Here Lies the Middleware

Prompt Engineering
2023 — 2025
"Write 2,000 words to explain how a PE professional thinks."
Killed by: Reasoning Models
Obsolete
AutoGPT & Early Agents
2023 — 2024
"Give it a goal. Watch it burn $40 in tokens. Achieve nothing."
Killed by: Competent Agents
Obsolete
AI Wrapper Apps
2023 — 2025
"A nice UI over the OpenAI API with a preset prompt. That'll be $20/month."
Killed by: ChatGPT & Claude
Obsolete
Prompt Marketplaces
2023 — 2024
"Buy this prompt for $5. It tells the AI to think like a marketer."
Killed by: Models Already Know
Obsolete
🏥

The ICU

Not dead — but the use case that justified the hype is shrinking fast.

🦕
RAG Pipelines
Narrowing
"Chunk, embed, retrieve, rerank, pray."
Threatened by: 1M Token Context
🦼
LangChain
Fading
"200 lines of Python to make AI think in steps."
Threatened by: Native Reasoning
🩹
Casual Fine-Tuning
Narrowing
"Spend $100K training a model. Base model does it free."
Threatened by: Base Model Quality
🦽
Vector DB Hype
Narrowing
"Built empires on 4K context limits."
Threatened by: Just Dump It In
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The Shift
Steve Turner · February 2026
Read the Full Thesis →
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