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AI-First vs AI-Enabled

The foundational distinction that determines every downstream decision -- architecture, pricing, team structure, and whether you can pivot fast enough when the next model breakthrough hits.

From Chapter 1: The AI-First Imperative

Overview

Most companies get AI strategy wrong from the start. They focus on "adding AI" instead of asking whether AI is the foundation or just a feature. This distinction shapes every strategic decision: hiring, architecture, fundraising, competitive positioning, and organizational structure. Confusing the two leads to misallocated resources and missed opportunities.

AI-First companies are built from the ground up with AI at the core of their value proposition. Without AI, the product wouldn't exist. AI-Enabled companies add AI capabilities to existing products or operations. AI enhances what they already do but isn't essential to their core value proposition. Both games can be won -- but you can't play both at once.

The architectural decisions made early tend to lock companies into their approach. Transitioning from AI-enabled to AI-first at scale has no proven public playbook. Starting AI-first lets you move fast. Retrofitting forces you to move carefully around structures that no longer serve the new reality.

The Framework

The Kill Test

The simplest way to know which camp you're in: what happens if you remove the AI?

  • Harvey: Remove the AI, and Harvey doesn't exist. There's no legacy product underneath. PwC partners report their junior lawyers would "riot" if Harvey were taken away.
  • Notion: Turn off Notion AI tomorrow, and you'd still have a functioning workspace for notes, docs, and wikis. The product existed since 2016. AI makes it better. AI doesn't make it exist.

5 Signs You're Looking at AI-First

1. The Company Didn't Exist Before Modern AI Harvey (founded 2022), Glean (launched commercially 2020), Perplexity (started 2022), Mistral (launched 2023, $6B valuation within 18 months). These companies exist because of LLMs and transformer architectures. No legacy product to protect, no existing architecture to work around.

2. Data Architecture Drives the Product Glean's Enterprise Knowledge Graph takes 12-18 months to fully mature for large customers. It learns organizational patterns, team structures, knowledge flows. The graph is the product. Without it, search doesn't work. This creates natural switching costs competitors would need years to overcome.

3. AI Expertise Is Distributed, Not Siloed Midjourney launched in 2022 with just 11 employees and hit $200M in annual revenue by 2023. There's no separate "AI team" because the entire company is the AI team.

4. Pricing Reflects AI as Core Value AI-first companies often use consumption-based or hybrid pricing. Glean charges $30/month per user and claims to save knowledge workers 2-4 hours per week -- nearly 14x ROI at a $100K fully-loaded annual cost. AI-enabled companies often price AI as an add-on (Notion charges $10 extra per member per month).

5. Marketing Says "Built On" Not "Now With" - AI-First: "AI-native search experience from the ground up" (Perplexity) - AI-Enabled: "Now the world's number one generative AI CRM" (Salesforce)

The first claims origin. The second claims addition.

The Comparison

Dimension AI-First AI-Enabled
Kill Test Product ceases to exist Core product survives
Founded Post-modern AI (2020+) Pre-AI, adding capabilities
Data Architecture drives the product Data supports features
Team AI expertise distributed throughout AI/ML as support function
Pricing AI is the core value AI is a premium add-on
Moat Data flywheels and model advantages Traditional moats (brand, network)
Risk Model failure = business failure Model failure = feature degradation

When AI-Enabled Is the Right Choice

AI-first isn't always the answer. Some contexts favor AI-enabled: - Large incumbents with massive existing customer bases (Salesforce has built CRM since 1999) - Highly regulated industries requiring interpretability - Hardware-first companies where software is secondary - Services where human judgment is the product

There's also a risk profile consideration: AI-first startups face existential risk if AI doesn't deliver promised value. AI-enabled companies hedge -- if AI fails, the core product remains.

What Happens When You Pick Wrong

Retrofitting doesn't fail outright. It takes longer and costs more than anyone expects. Over 35% of enterprises cited high upfront expenses as a barrier to AI implementation in 2024, and the skills gap meant 41% faced deployment delays. Legacy systems weren't designed for AI workloads. Data sits fragmented across departments.

How to Use This

Apply the Kill Test to your own product or any company you're evaluating. If removing AI leaves nothing, you're AI-first. If the core product survives, you're AI-enabled. Then check the five signs to confirm your assessment. Neither approach is inherently better, but you must be clear about which one you are -- the worst position is being unclear and under-investing in both. Build your company on AI or with AI, and choose deliberately.

Deep Dive

Read the full chapter: Chapter 1: The AI-First Imperative