OpenAI’s Product Lead Reveals the 4-Part Framework for AI Product Strategy

Build AI products that scale profitably, retain users, and defend against commoditization

Aug 26, 2025

By Miqdad Jaffer, Product Lead at OpenAI:

In every wave of technology, there are two types of founders:

Those who ride the hype and get crushed under their own costs.
Those who turn the wave into a moat and dominate a market for a decade.

AI is no different… except the stakes are higher. Because unlike SaaS or mobile, AI doesn’t forgive bad strategies.

Chegg lost 90% of their valuation because they failed to act on AI quickly enough. While students flocked to ChatGPT for instant, personalized help, Chegg hesitated, reacted late, and the market punished them brutally.

Jasper, once the golden child of AI writing, raised $125M at a $1.2B valuation and became the poster company for “AI wrappers.” But without a real moat, and with SaaS-style pricing that didn’t align with their soaring inference costs, they quickly lost ground. As ChatGPT gained adoption, users churned, prices had to be slashed, and Jasper is no longer the category favourite.

Duolingo, instead of delighting users with thoughtful AI integration, pushed out AI tutors and fired their staff but that felt forced and extractive. The result was devastating: reputational damage, hundreds of thousands of users churning, and 300,000 followers lost in a matter of weeks.

And these aren’t isolated missteps.

There are countless examples of companies bolting AI on as an afterthought, shipping gimmicky features without thinking about economics, or simply waiting too long to act… only to find that the market doesn’t give second chances.

That’s why in my 6-week AI Product Strategy cohort (and by the way, apart from the live sessions, you’ll also get a written review of your own AI Product Strategy + $550 off), we’ll be talking about how every one of these companies thought they could wait it out or ship later.

But in AI, time is compressed.

The adoption window is measured in quarters, not years.
Commoditization happens in weeks, not months.
Investors, users, and the market punish hesitation brutally.

So, without further ado, let’s dive straight into AI Product Strategy 101 for Founders - everything you need to know to not just survive this wave, but own it.

Visual summary showing why AI product strategy separates successful startups from failed demos, with examples like Chegg, Jasper, and Duolingo — From Chegg to Jasper, strategy—not features—determines who wins in AI. This framework shows how smart companies survive and grow.

The Illusion of “Just Add AI”

Right now, every pitch deck has “AI-powered” slapped on the first slide. Founders think it gives them credibility. Investors nod. Customers get curious. But here’s the catch:

AI itself isn’t the moat. Everyone can access GPT-4o, Claude, Llama, Mistral. The barrier to entry is zero. If your strategy is “use OpenAI’s API and wrap a UI around it,” you don’t have a company, you have an expensive demo that can be cloned overnight.

What separates winners from losers is whether you can answer this question:

What happens when your competitors get access to the exact same AI model tomorrow?

If your answer is “we’ll build faster,” you’ve already lost.

Why AI Breaks Founders Without Strategy

Here’s what makes AI brutal:

Costs Don’t Behave Like SaaS: In SaaS, once you build the product, marginal costs per user trend toward zero. In AI, every query, every generation, every inference has a real cost attached — tokens, GPUs, hosting. Without strategy, costs scale faster than revenue.
Commoditization Happens Overnight: In SaaS, features might take years to copy. In AI, they’re cloned in weeks. The only defense is strategic moats: proprietary data, trust, or distribution.
Hype Attracts Competition: Every new AI feature gets 100 clones on Product Hunt. Most vanish. But some take your market if you don’t defend it with strategy.
Investors Are Smarter Now: In 2021, “AI” on a deck raised millions. In 2025, VCs ask: What’s your moat when GPT-5 launches? How do you survive inference costs at 100M queries a month? If you don’t have answers, the check doesn’t come.

AI is not about building the flashiest demo.

It’s about designing the system around the AI:

How will you monetize it profitably when usage scales 10x?
How will you retain customers when the underlying models get better and cheaper every month?
How will you turn your distribution into a compounding advantage?
How will you build trust in an environment where hallucinations and privacy issues erode confidence?

That’s the difference between being AI companies that will die and the ones who’ll rule the future.

The winners will be the founders who don’t just “add AI,” but architect it into a product strategy that scales, defends, and compounds.

And here’s the truth: the gap between winners and losers in AI will open faster than any prior wave in tech.

Because when costs spiral, you don’t get years to fix it: you get months.

When commoditization hits, you don’t have quarters to react: you have weeks.

That’s why AI product strategy isn’t a “nice to have.”

It’s the only thing standing between hypergrowth and collapse.

AI Economics: The New Unit Economics of Startups

In SaaS, the playbook was simple:

Spend once to build the product.
Acquire a user.
Marginal cost to serve them = near zero.
Profits scale with every new customer.

That’s why SaaS margins hover around 70–80%. It’s why SaaS created billion-dollar giants off $29/month subscriptions.

But AI doesn’t play by SaaS rules. In AI, marginal costs are stubbornly real.

Why Marginal Costs Behave Differently in AI vs SaaS

Every AI query has a price tag attached.

A single ChatGPT query costs OpenAI fractions of a cent to several cents depending on the model.
Run that across millions of users, and suddenly your “free tier” burns millions a month.

In SaaS, scale lowers costs. In AI, scale can increase costs unless you’ve built efficiency into your product design.

Here’s the brutal truth: Inference costs are the new AWS bill. And just like early startups got destroyed by runaway cloud costs, AI startups today are bleeding from token bills they can’t control.

Case Study: Perplexity vs Midjourney vs ChatGPT

Perplexity understood the math early. Instead of running raw GPT calls for every query, they built a hybrid retrieval layer + LLM. By pulling relevant docs first, then summarizing, they cut token usage dramatically. Lower costs, faster responses, and more citations = better UX.
Midjourney built community-driven virality on Discord. But the hidden story? GPU costs were astronomical. Every image rendered = compute burned. That’s why they pushed aggressive paid tiers quickly — because free users were unsustainable.
ChatGPT exploded with adoption (100M users in 2 months), but it nearly broke OpenAI’s compute budget. That’s why “ChatGPT Plus” launched at $20/month. Not just a monetization play, but a cost-containment move.

The pattern is clear: founders who survive long enough to scale do so because they design unit economics upfront.

The Hidden Trap of Token Costs & API Reliance

Most early AI startups are API wrappers. They rely 100% on OpenAI, Anthropic, or another foundation model. That’s fine for a prototype. Deadly for a company.

Why?

You don’t control pricing. OpenAI raises API rates tomorrow? Your margins collapse.
You don’t control performance. Model latency or downtime? Your product breaks.
You don’t control differentiation. If the same API is available to everyone, what stops the next founder from copying your entire product in a weekend?

This is why API-first AI products die fast. They mistake building a demo for building a company.

How to Model Costs When Usage Scales 10x

Let’s run a simple thought experiment:

Suppose you charge $29/month per user.
Your average user makes 500 queries/month.
Each query costs you $0.002 in tokens.
That’s $1.00 in raw inference cost per user/month.
Gross margin = ~97%. Beautiful.

Now scale:

You grow from 1,000 users → 100,000 users.
Queries balloon from 500,000 → 50 million/month.
Costs = $100K/month → $10M/year in inference.
Suddenly your AWS bill looks tiny in comparison.

This is the trap. Margins look fine at 1,000 users. They crumble at 100,000 unless you:

Batch or cache intelligently. (Don’t re-generate the same outputs 50 times.)
Use model routing. (Run cheap models for simple tasks, expensive ones only when needed.)
Build proprietary infra. (Training small domain-specific models that are cheaper to run.)

The Real Math Behind AI Profitability

Let’s be blunt: most AI startups right now aren’t profitable, even if they look like they’re growing. They’re subsidizing user adoption with VC dollars while ignoring the economics.

The ones that win are doing three things differently:

Pricing Strategically.
- Free tier = bait.
- Paid tiers kick in fast, with usage-based pricing that scales with costs.
- Example: Midjourney cutting off “free” generations because the math broke.
Building Cost Curves Into Design.
- Perplexity’s retrieval step is a cost moat.
- Grammarly’s incremental fine-tuning makes corrections cheaper over time.
- Canva’s AI tools are lightweight enhancements, not cost-draining centerpieces.
Diversifying Dependence.
- Routing across multiple providers (OpenAI, Anthropic, Cohere, Mistral).
- Training domain-specific models where possible.
- Owning infrastructure when scale demands it.

If you build AI without modeling your unit economics:

You will mistake growth for success. You will bleed money the faster you scale. You will wake up one day with negative margins and no investor patience. But if you design your economics into the product from Day 1, you flip the script:

Your costs drop as usage grows (because caching, routing, infra efficiencies).
Your competitors can’t undercut you (because your economics are structurally better).
Your growth compounds into a real moat, not just hype.

That’s the difference between being a demo and being a decade-defining company.

The 4D Framework for AI Product Strategy

When you’re building an AI company, you don’t lose because your idea was bad.

You lose because your strategy couldn’t withstand scale, commoditization, or costs.

After building, scaling, and exiting an AI company — and watching hundreds of other founders win or die — I built the 4D Framework to pressure-test every product decision.

Think of it as a survival map. If you don’t run your company through this lens, you’re building blind.

(This is the foundational framework. In the AI Product strategy cohort, we’re diving into the advanced framework with examples.)

The 4D’s are:

Direction → Choosing the moat that compounds over time.
Differentiation → Surviving when your feature gets commoditized.
Design → Architecting products that balance adoption with cost efficiency.
Deployment → Scaling without blowing up your P&L

Let’s unpack them one by one.

1. Direction: Choosing the Moat That Actually Compounds

Here’s the reality: AI features are temporary, but moats are permanent. The market doesn’t reward you for building a clever wrapper around GPT-5, because someone else can build the same wrapper tomorrow.

What the market rewards is whether your product grows stronger every single time a new user signs up. That’s what Direction is about: deliberately choosing which compounding moat you will invest in and defend.

There are only three moats that truly matter in AI:

(a) Data Moat

The most durable and defensible moat in AI is proprietary data. If your product generates unique, defensible, structured data every time it’s used, then with each additional user you are pulling further ahead in a way that competitors cannot copy or buy.

Example: Duolingo. They didn’t just add an AI and call it a day. They fine-tuned their models on years of proprietary student learning data: which exercises students struggled with, which corrections worked, how learning paths evolved across geographies and demographics. That dataset is a treasure chest that no new entrant can replicate, no matter how much capital they raise.

Why it matters: Data moats compound. Each new user → more unique data → smarter, cheaper, more personalized models → better user experience → more users. That’s a flywheel, and it gets stronger with time.

Questions to ask yourself:

Are we collecting data competitors will never have access to?
Is that data high-quality, structured, and improving over time?
Can we design feedback loops so the product gets better the more it’s used?

(b) Distribution Moat

Distribution has always been a moat in business, but in AI it is everything.

Example: Notion. When they added AI, they didn’t need to spend millions on customer acquisition. They already had tens of millions of users embedded in workflows, so flipping the switch created instant adoption at scale.

Example: Canva. They didn’t try to market “AI image generation” as a separate gimmick. They embedded it directly into the design process where users already lived, making it feel like a natural extension of the product.

Why it matters: If you don’t own distribution, you’re fighting over scraps against ChatGPT, Gemini, or whatever foundation model launches next. Distribution means your product gets used not because of a feature, but because it’s already where your customers are.

(c) Trust Moat

The most underrated moat in AI is trust. Users don’t only want powerful AI; they want predictable, safe, reliable AI. In many industries, trust isn’t optional — it’s the entire value proposition.

Example: Anthropic. They didn’t try to beat OpenAI on raw scale or parameter count. Instead, they positioned themselves as the company obsessed with safety and alignment. That single positioning choice won them enterprise customers who could not afford the reputational risk of deploying unaligned models.

Example: OpenAI’s enterprise deals. Many companies technically could roll their own models or buy cheaper alternatives, but they pay OpenAI millions because trust in governance, compliance, and reliability is more valuable than raw model weights.

Why it matters: Trust compounds slowly, but once earned, it becomes a moat stronger than features. A single hallucination or breach can break it, but consistent reliability creates lock-in that competitors can’t disrupt with a slightly faster or cheaper model.

If you don’t explicitly choose a Direction, the market will choose one for you. And when you let the market choose, it almost always defaults to commoditization — which is where startups die.

2. Differentiation: Surviving Commoditization

Here’s the brutal truth: if your product is just “AI that does X,” OpenAI (or another foundation model company) will eventually eat you alive. These companies are shipping horizontally at breathtaking speed: adding features across documents, spreadsheets, email, images, and audio. If your entire differentiation is that you “added AI,” you’re already roadkill.

Differentiation means building defenses against inevitable commoditization. It’s about answering: why should a customer choose you, even when OpenAI or Anthropic offers something similar for free or bundled?

Questions to ask yourself:

What specific failure mode of foundation models does my product solve better than anyone else?
Where are general-purpose models overkill — too slow, too expensive, too generic — and where can I build a targeted solution that outperforms them?
How do I design workflows, UX, and integrations that make my product sticky, so customers stay even if others copy the raw feature?

Case Studies:

Perplexity AI. Any LLM can answer questions, but Perplexity differentiated by providing citations, sources, and retrieval-first workflows. That wasn’t just a feature — it was a positioning wedge: “trustable AI search.”
Runway AI. Instead of chasing generic video generation, they focused deeply on creators, editors, and filmmakers. Their differentiation wasn’t “we generate video.” It was “we are the pro-grade tool for professionals who need production-quality outputs.”

Differentiation doesn’t mean “add more features.” It means owning the use case so deeply that the market sees you as the default, even if technically others can replicate your core capability.

3. Design: Architecting for Adoption + Cost Efficiency

This is the graveyard where most AI startups die. They focus on building “wow demos” that light up Twitter for a week, but adoption doesn’t stick and the economics collapse under the weight of inference bills. Good design in AI means finding the balance between user adoption and sustainable cost structure.

Adoption Principles:

Kill friction. Don’t expect users to learn “prompt engineering.” Translate natural actions into AI outputs. Grammarly didn’t ask you to type “Rewrite this in a formal tone”; they gave you a single button that did it.
Meet users where they already work. Put AI inside their workflows (Notion, Canva, Figma) instead of forcing them into a new app. Adoption is 10x easier when you ride existing habits.
Minimum Viable Intelligence. Solve one pain point completely before chasing AGI-level generality. Perplexity’s focus on “AI + trustable answers” was enough to carve out growth — they didn’t need to solve every problem at once.

Cost Efficiency Principles:

Model Routing. Don’t send every query to GPT-5. Use smaller, cheaper models for 80% of tasks and escalate only when necessary.
Caching. If 1,000 users ask the same thing, don’t pay 1,000x for the same output. Cache intelligently.
Prompt Optimization. Every token costs money. Make your prompts concise and efficient.
Batching. Bundle multiple requests into a single inference call where possible.

Why it matters: The founders who win are the ones who design products where the cost per user goes down as adoption grows. Everyone else builds demos that burn cash and collapse when scale arrives.

4. Deployment: Scaling Without Blowing Up

Scaling is the final boss of AI startups. This is the stage where you either become a unicorn or implode under your own costs.

The paradox of AI is that products can grow faster than any other technology before, but costs can outpace revenue just as fast. Deployment is about building systems that protect your P&L as you scale.

Pricing Strategy:

Move to usage-based or hybrid pricing early.
Tie customer costs directly to the value they perceive.
Never promise unlimited AI features unless you’re prepared to watch your margins disappear.

Infrastructure Strategy:

Use a multi-model approach. Don’t lock yourself into one provider. Route intelligently between OpenAI, Anthropic, Mistral, or open-source models, and play vendors against each other.
Specialize at scale. Once you hit significant volume, train domain-specific models that are cheaper and faster than general-purpose APIs.
Build eval systems to monitor quality, accuracy, latency, and hallucinations at scale.

Team Strategy:

Don’t just hire ML engineers. Hire product engineers who understand the trade-offs between UX, speed, and GPU cost.
Your best hire may be the one who knows when to say “no” to expensive demos that look great on stage but destroy your margins in production.

The Founder’s 4D Lens

Every decision you make as an AI founder should run through this lens:

Direction: Are we building toward a defensible moat, or just another wrapper?
Differentiation: Will this still matter when OpenAI ships the same thing tomorrow?
Design: Does each new user improve our economics, or worsen them?
Deployment: Can we scale to 10x without collapsing our margins?

If you can’t answer “yes” to all four, stop. You’re about to build a feature, not a company.

And features die. But companies with strategy endure.

4D framework for AI product strategy covering direction, differentiation, design, deployment, and the 2Ps of pricing and positioning — A survival map for AI founders. Use the 4D Framework and 2Ps to build defensible AI products, model unit economics, and choose pricing that scales.

2Ps: Pricing And Positioning AI Products

When founders talk about pricing, they usually treat it like an afterthought: “We’ll figure it out after product-market fit.”

That might work in SaaS. In AI? It’s fatal. Because in AI, pricing is not just how you make money. It’s how you control costs, shape user behavior, and build your moat.

If you get it wrong, adoption bleeds you dry. If you get it right, pricing itself becomes your competitive advantage.

Why Pricing Is a Strategic Lever, Not an Afterthought

In SaaS, you could underprice at the beginning, eat some AWS bills, and make it up in scale. Your marginal costs trended toward zero.

In AI, marginal costs are stubbornly real. Every query = tokens, GPUs, latency, inference. That means your pricing is your economic survival strategy.

It controls:

Who you attract (casual browsers vs. high-value enterprises).
How they behave (conserve vs. abuse queries).
When you break even (month 1 vs. year 3).
What positioning you signal (premium vs. utility, pro-grade vs. consumer-grade).

The 4 Archetypes of AI Pricing

1. Usage-Based Pricing (Tokens, Queries, Compute)

How it works: In this model, customers are charged directly for the exact amount of AI resources they consume, whether that’s measured in tokens processed, queries made, or GPU minutes used. Every unit of usage has a clear price tag attached to it, which means the cost structure is highly granular and easy to calculate.

Best for: Usage-based pricing works best for APIs, infrastructure products, and enterprise-facing tools where consumption is predictable, measurable, and directly tied to business value. Companies that position themselves as a “platform layer” rather than an end-user product often lean on this model because it maps neatly onto the way developers and enterprises think about scaling workloads.

Examples:

OpenAI API — charges per 1,000 tokens processed, with transparent rates for each model.
ElevenLabs — charges based on minutes of audio generated, aligning price with output.

Strengths: The biggest strength is that revenue scales directly with costs, which creates a transparent alignment between usage and value. Customers feel they’re paying for exactly what they consume, and the company doesn’t run into the trap of subsidizing heavy users. It also builds trust with developers and enterprises who are used to AWS-style pricing models.

Weaknesses: The major downside is what’s known as “meter anxiety.” Users become hesitant to experiment or adopt at scale because they fear runaway bills. This can limit adoption in consumer-facing markets or in creative applications where usage is unpredictable. It’s also harder to position usage-based pricing as “accessible” or “friendly,” since it feels transactional rather than like a subscription service.

2. Outcome-Based Pricing (Pay for Results, Not Usage)

How it works: Instead of charging for raw consumption, the company charges customers based on the outcome delivered. This could mean paying per lead generated, per fraud case detected, per conversion achieved, or even per line of code shipped. The core idea is that customers are not paying for tokens or minutes — they’re paying only when the AI actually creates measurable business impact.

Best for: This model is best suited for enterprise AI products where the value of outcomes can be measured in dollars and tied directly to KPIs. It works in categories like sales, marketing, fraud detection, and compliance — areas where companies care less about the technology itself and far more about the results.

Examples:

AI sales platforms that charge per qualified meeting booked.
Fraud detection systems that charge per fraudulent transaction caught.

Strengths: This model creates perfect alignment between company and customer because the customer pays only when they see value. It allows premium positioning in the market since the pitch becomes: “We only win if you win.” It can also dramatically reduce friction in sales because customers feel there’s no wasted spend.

Weaknesses: The weakness is that it’s much harder to implement in consumer or creative apps where outcomes are subjective or harder to measure. It also shifts risk onto the AI company. If the models underperform or results lag, revenue suffers immediately, even if customers are still using the system heavily. The operational complexity of measuring outcomes at scale can also be significant.

3. Seat-Based Pricing (Per User, Per Month)

How it works: This is the classic SaaS model where customers pay a flat monthly or annual fee per seat or per user. It’s simple, predictable, and familiar, which is why many AI startups gravitate to it even though their underlying economics are different from SaaS.

Best for: Seat-based pricing works best for workflow AI products that embed themselves directly into team collaboration and productivity. If the product becomes part of daily work, it makes sense to tie cost to the number of people using it, because each additional user expands the value of the platform inside the organization.

Examples:

Jasper AI (originally) used a SaaS-style seat model for their writing tool.
Notion AI integrated AI features into its existing per-seat SaaS plans.

Strengths: The greatest strength of seat-based pricing is that it’s incredibly familiar to buyers, especially in the enterprise. CFOs can easily forecast spend, and procurement teams don’t have to relearn a new model. It’s also great for positioning — you can tell the story that you’re “enterprise SaaS with AI inside,” which makes investors and buyers more comfortable.

Weaknesses: The danger is that AI doesn’t behave like SaaS. If usage per seat explodes, for example, one user is hammering the AI 100x more than another — the company eats those costs unless it has carefully tiered or capped usage. This creates a dangerous mismatch between revenue and costs. It also doesn’t align well with variable usage, which makes it risky for high-consumption AI workloads.

4. Hybrid Pricing (Mix of Usage + Subscription)

How it works: Hybrid pricing combines the psychology of subscriptions with the control of usage-based pricing. Typically, this means a base subscription that unlocks access plus additional usage add-ons or caps. Users feel like they’re paying for access, but the company has guardrails to prevent abuse and better align costs with revenue.

Best for: Hybrid pricing works best for consumer and prosumer AI applications where usage is highly variable. It’s also effective for products that need to scale across different segments, from hobbyists who want predictable pricing to enterprises that demand usage-based flexibility.

Examples:

MidJourney uses flat monthly tiers with caps on GPU minutes, which lets them offer “all-you-can-eat” tiers while still limiting runaway costs.
ChatGPT Plus offers flat $20/month pricing for priority access, but enterprise contracts rely on usage-based pricing to manage scale.

Strengths: Hybrid pricing captures the best of both worlds. On one hand, it matches consumer psychology by offering “all-you-can-eat” tiers that feel approachable and predictable. On the other, it protects the company from abuse by layering in caps, limits, or overage charges. It’s also flexible enough to grow with customers, allowing a smooth path from individual hobbyists to large enterprise deployments.

Weaknesses: The weakness is complexity. Hybrid pricing requires careful packaging, clear communication, and constant tuning as model performance, costs, and market expectations evolve. If not managed well, users can get confused by tiers, and companies can lose revenue by setting limits too generously or frustrating customers with overages.

Case Studies: The Good, The Bad, and The Collapse

1. OpenAI API → Usage-Based Done Right

Clear token pricing tied directly to compute.
Transparent, scalable, enterprise-friendly.
Positioning: “We are the rails of AI.”
Result: predictable revenue scaling with costs. No consumer adoption, but dominance in infrastructure.

2. MidJourney → Hybrid Pricing With Guardrails

Subscription tiers ($10–$60/month) with caps on GPU minutes.
Cut off “free trials” fast once GPU costs exploded.
Positioning: “Accessible creativity, but pay to play.”
Result: explosive consumer adoption + cost control.

3. Jasper → Seat-Based Pricing Without Guardrails

$59–$499/month per seat. Looked like SaaS.
Problem: inference usage exploded, but pricing didn’t align with costs.
Worse: commoditization (ChatGPT) killed differentiation.
Positioning failure: “We’re SaaS with AI inside” — but without a moat, they were just a middle layer.
Result: from $125M ARR → stall-out and valuation collapse.

Founder Playbook: How to Choose & Position Pricing

Ask yourself:

What’s my moat? (Data, distribution, trust). Your pricing should reinforce it.
- If data-heavy → usage-based works (aligns with infra positioning).
- If trust-based → outcome pricing works (we win when you win).
- If distribution-heavy → hybrid works (capture consumers, monetize pros).
What behavior do I want to incentivize?
- Casual adoption? → flat pricing.
- Efficient use? → usage-based.
- High ROI users? → outcome-based.
What story am I telling the market?
- Infrastructure (usage).
- Partner (outcome).
- SaaS (seat).
- Democratizer (hybrid).

Positioning Mistakes AI Founders Make

Founders obsess over models, features, and infra. But the real battlefield is positioning.

Positioning is how the market perceives you. It’s the story in the customer’s head when they think of your product. And in AI, where tech is commoditized overnight, the story is often the only durable advantage you have.

And most founders get it all wrong!

1. Copying SaaS

Many AI startups lazily mimic SaaS positioning: “per seat pricing,” “enterprise SaaS workflow tool,” “we’re like Salesforce but with AI.”

The problem: you’re not building SaaS.

SaaS = zero marginal costs, scale loves you.
AI = every inference burns real dollars.

When you borrow SaaS positioning, you’re telling the market: “We’re just software.” But you’re not. You’re economics + infra + strategy wrapped in a product.

What to do instead: Position as AI-native. Acknowledge cost dynamics. Build pricing and messaging that signal you understand AI’s economics, not SaaS’s.

2. Hiding Costs

Nothing destroys trust faster than surprise bills. Many founders try to “smooth” the story by hiding inference costs behind flat subscriptions or “unlimited usage.”

The result? users abuse it, your GPU bills explode, and when you change pricing later, you look dishonest.

Positioning problem: You framed yourself as a “magic unlimited AI,” but the business reality can’t sustain it.

What to do instead: Transparency = trust. OpenAI didn’t sugarcoat — they showed per-token pricing. It positioned them as predictable infrastructure. MidJourney capped GPU minutes, positioning as premium creative tooling, not a toy.

Your users don’t need “free.” They need to trust you’re not tricking them.

3. Confused Signals

This is subtle but deadly. Founders often mismatch their product story with their pricing model:

Usage-based but marketed as consumer. Users bounce — they expect “fun app,” not “AWS billing.”
Flat subscription but bleeding on inference. Investors roll their eyes: you’re scaling adoption while margins collapse.

Why it matters: Inconsistency signals you don’t know who you are. And if you don’t know, why should users or investors believe in you?

What to do instead: Align pricing + narrative.

If you’re usage-based, position as rails/infrastructure.
If you’re subscription-based, position as consumer/prosumer with clear boundaries.
If you’re outcome-based, position as an ROI partner.

Your business model is not just finance, it’s messaging.

4. No Story

This is the silent killer. Pricing and features aren’t enough. You need a story investors, press, and users can repeat in one line.

Think about it:

“They’re the AWS of legal AI.” → instantly credible.
“They’re the Canva of AI video.” → clear, viral, consumer story.
“They’re the growth partner, not a tool — they charge per result.” → outcome-driven trust.

If you don’t craft this narrative, others will. And when others define your positioning, you’ve already lost.

What to do instead: Write the story before the deck. Decide what mental box you want to live in — infra, tool, partner, democratizer — and let pricing, packaging, and GTM flow from that.

The Mistakes That Kill AI Startups

The brutal truth about AI startups: most don’t die from competition. They die from their own strategic blind spots.

I’ve seen founders burn millions, lose entire markets, or implode under their own costs. Not because the tech didn’t work, but because the strategy didn’t.

Here are the killers I see again and again.

1. Chasing Features Instead of Moats: Every founder wants to show off flashy features: “Look, our AI writes blogs, our AI generates images, our AI summarizes PDFs.” The problem? Features are copyable. Moats are not. The founders I’ve seen who survive don’t ask: “What can AI do today?” They ask: “What’s the defensible wedge AI gives us that compounds over time?”

2. Blind API Reliance (and the Sudden Margin Collapse): Many early AI startups are just wrappers around OpenAI, Anthropic, or another foundation model. Great for prototyping. Deadly for scaling. I know a founder who built an AI “assistant” app. They were growing like crazy, 50K users in three months. Then the OpenAI API bill hit: $120,000 in one month. Revenue? Less than $10K. The margins collapsed overnight. Investors bailed. Within six months, the startup was gone.

3. Mispricing AI Features as “Free Add-ons”: This is a common trap for SaaS founders. They add AI to an existing product, but they treat it as a “freebie” inside their pricing tiers. That works at 100 users. It kills you at 10,000. Why? Because usage scales exponentially, but your revenue doesn’t. A B2B founder offered AI-powered reporting as part of a $99/mo seat license. Within a year, 20% of queries were AI-driven, costing them thousands per customer… on a plan that was never priced in inference costs. They had to scramble to repackage, and it nearly tanked their churn.

4. Ignoring Evals and User Trust: In SaaS, you can ship fast, patch later, and usually survive. In AI, one bad hallucination can destroy trust forever. A fintech founder told me their AI onboarding tool “accidentally” generated fake compliance recommendations for a client. The client caught it. Trust gone. Deal lost. Another consumer AI app shipped without evals. A viral tweet exposed its biases. Overnight, adoption crashed. Eval systems are not optional. They are your QA, your safety net, and your trust moat. Ignore them, and the market won’t forgive you.

5. Thinking “Scale Will Fix Economics” (When It Actually Worsens Them): This is the deadliest delusion: “Sure, margins are thin now, but once we scale, the costs will balance out.” Wrong. In SaaS, scale improves margins. In AI, scale often makes them worse because every new query burns dollars. I read a story about the founder who raised $20M, convinced scale would save them. They subsidized free usage to juice adoption. At 100K users, they were spending more than $1M/month on compute. By 200K users, they were dead.

Every one of these founders thought they could “figure it out later.”

But unfortunately, AI doesn’t give you that luxury.

Simple Frameworks to Avoid These Mistakes

Warnings are useless without playbooks. Here’s how to de-risk each killer.

1. From Features → Moats

Ask: What compounds with every user we add?
Build: proprietary data loops, sticky workflows, or brand trust.
Framework: For every feature idea, map it to a moat. If it doesn’t strengthen data, distribution, or trust, deprioritize it.

2. From API Reliance → API Strategy

Start with APIs (speed), but build toward hybrid infra.
Use multi-model routing (cheap models for 80% of tasks, LLMs for edge cases).
Identify “data exhaust” from usage → fine-tune smaller, cheaper models over time.
Set a runway trigger: “When API costs >20% of revenue, start infra investment.”

3. From Free Add-ons → Aligned Pricing

Always tie pricing to usage or value delivered.
If bundling into SaaS, cap usage in tiers.
Track “AI cost per user” weekly. If it’s >30% of their plan price, you’re underwater.
Tell the story early: “AI is premium, because it costs real money.” Customers will respect honesty.

4. From Ignoring Evals → Trust Moat

Build eval pipelines before scale. Measure accuracy, bias, latency.
Set thresholds: “We don’t ship if accuracy <90%.”
Communicate trust. Publish reliability metrics (Anthropic’s alignment story is a positioning moat).
Train your team: AI QA isn’t optional.

5. From “Scale Will Save Us” → Scale Discipline

The model costs at 10x and 100x before launch.
Stress-test: if 10x users kills your P&L, you don’t have product-market fit.
Scale only what improves margins — caching, infra, routing.
Remember: scale multiplies mistakes. Fix unit economics first.

Founder’s Playbook: Making AI Strategy Actionable

The danger with a lot of AI strategy talk is that it sounds impressive but doesn’t give you anything concrete to actually implement. Founders leave panels and podcasts nodding along, but Monday morning they’re staring at their roadmap wondering what to actually do differently.

That’s why this playbook matters. It’s not a theory. It’s the five moves you can use right now to make AI strategy actionable inside your company. Think of it as the discipline that separates demos from businesses.

1. How to Stress-Test Your AI Unit Economics

One of the most common mistakes I see is founders running financial models at “today's scale.” They model costs at 1,000 users, show a neat LTV:CAC ratio, and assume that if it works now, it’ll work later. That’s how startups end up blindsided.

AI is brutal because costs don’t behave like SaaS. Every new user increases inference costs, and unless you’ve designed efficiency into your product, the economics actually get worse as you grow.

To avoid that, build a stress-test model before you ship anything:

Estimate average queries per user per month.
Multiply that by the cost per query (tokens, GPU minutes, latency).
Compare it directly to revenue per user.

Then run the simulation at 10x and 100x scale. This is where most startups break. It looks fine at 1,000 users, but at 100,000 users the GPU bill is eight figures and your gross margin goes negative.

As a founder, you want to set thresholds: if AI costs are more than 20% of revenue, you’re in a danger zone. If they climb past 40–50%, you’re in a death spiral. The sooner you see it in a spreadsheet, the sooner you can design around it with caching, batching, or model routing before the problem shows up in your burn rate.

2. How to Write an AI PRD That Accounts for Costs & Adoption

Traditional PRDs are written like feature wishlists: “We’re going to build summarization because users want faster notes.” But in AI, that’s not enough. You need to account for the economics of running the feature and whether it actually drives adoption that sticks.

Every AI PRD should include two new sections:

Cost Analysis. What is the estimated cost per user per month to support this feature? If you have 10,000 users making 200 queries each, what does that translate into in raw inference costs? Can we cut that number down by using cheaper models for simpler queries, or caching repeated outputs so we don’t pay for the same thing twice?
Adoption Analysis. Is this feature something people will use once for novelty, or is it embedded in their daily workflow? Does it reinforce a moat like data collection, trust, or distribution — or is it just another cool button that won’t matter six months from now?

If you can’t answer these two, don’t greenlight the feature. You’re not building SaaS; every decision carries an economic footprint and a strategic trade-off.

3. How to Pressure-Test Differentiation Against Commoditization

This is the founder's nightmare: you build a product, raise a round, and two months later OpenAI or Anthropic releases the same feature inside their foundation model. Overnight, you’ve been commoditized.

The way to avoid that fate is to constantly pressure-test your differentiation. Ask yourself the “OpenAI Test”: if OpenAI shipped this exact feature tomorrow for free inside ChatGPT, would we still exist? If the answer is no, you don’t have a business, you have a wrapper.

Run a quarterly differentiation audit where you map out:

What do we do that foundation models can’t?
Where do we win that general-purpose LLMs fail (like industry-specific data, compliance workflows, or domain expertise)?
What integrations, UX flows, or trust signals do we provide that make us sticky even when competitors can technically replicate our features?

If you can’t point to at least one area of defensibility, you need to pivot toward building moats: proprietary data, workflow lock-in, or trust branding. Commoditization is inevitable; defensibility is a choice.

4. How to Present AI Strategy to Investors (and Get the Check)

Here’s the reality: investors are no longer impressed by “AI-powered X for Y.” They’ve seen a thousand of those decks, and they’ve funded some that died fast because the economics didn’t work.

When you pitch, you need to frame your story not around features, but around survival and defensibility. Investors want to know:

What is your moat? Does something compound with scale — data, distribution, or trust?
What are your unit economics at 10x scale? Can you show you’ve thought beyond today’s costs?
How do you survive commoditization? Why can’t GPT kill you tomorrow?
What’s the positioning story? Are you the “AWS of X,” the “Canva of Y,” or the “growth partner” that shares in customer outcomes?

The more concrete you can be, the better. Show your pricing model as part of your story:

“Our usage-based pricing aligns value delivered with costs incurred, which means margins improve with scale.” That’s not just pricing, it’s positioning, and it signals to investors that you’re building a real business, not a hype play.

5. How to Hire for AI Product Leadership

The last step is people. Most founders underestimate how different AI product leadership is from SaaS product leadership. You can’t just hire a generic PM and expect them to navigate token costs, inference trade-offs, and commoditization.

You need leaders who can bridge three worlds at once:

Product strategy: they think in terms of moats, adoption loops, and positioning.
Economics: they know how to model token costs, GPU trade-offs, and caching strategies.
AI mindset: they understand how models behave, where they fail, and how to design evals that keep user trust intact.

The best hires are often hybrids: engineers who’ve launched products, or PMs who’ve managed infra-heavy projects. They need to be as comfortable discussing pricing strategy with a CEO as they are debugging an eval pipeline with an engineer.

If you hire PMs who think AI is “just another feature,” you’ll bleed cash. If you hire engineers who only obsess over model performance but ignore adoption and costs, you’ll build beautiful demos nobody uses. Hire people who see AI as a system: technology, business, and user psychology woven together.

In a nutshell, turning AI strategy into action is not about inspiration. It’s about discipline.

You stress-test your economics so scale doesn’t kill you.
You write PRDs that force you to confront costs and adoption upfront.
You audit your differentiation so you don’t get commoditized.
You pitch investors on strategy, not demos.
You hire leaders who can think across product, infra, and economics.

That’s how you survive the chaos of AI.

Because the founders who win aren’t the ones with the flashiest features. They’re the ones with the discipline to run their company like a system… where every decision compounds into economics, defensibility, and trust.

Founder's AI playbook showing 5 ways to build real strategy, common positioning mistakes, and silent killers that destroy AI startups — Most AI startups don’t fail on tech. They fail on strategy. Use this playbook to avoid fatal mistakes and turn your product into a company.

Why Now Is the Defining Moment for Founders

Every generation of technology creates winners and losers. The internet did. SaaS did. Mobile did.

But AI is different. It’s not just another wave. It’s the fastest-moving, most brutal, and least forgiving wave we’ve ever seen.

The market is already crowded. Every week, hundreds of “AI-powered” apps launch. Investors are flooded with decks. Customers are overwhelmed with choices. Features commoditize in weeks. APIs get cheaper, faster, and more accessible by the month.

But here’s the paradox: while the market is crowded, real strategy is rare.

Most founders are chasing demos. Most are wrapping APIs. Most are ignoring economics, mispricing features, and hoping scale will save them.

It won’t.

AI is the only wave where poor strategy bleeds money faster than any wave before it. In SaaS, you could limp along for years before bad unit economics caught up with you. In AI, a single month of runaway inference costs can sink you. In SaaS, you could hide behind features. In AI, commoditization makes your “unique” feature irrelevant overnight.

That’s why the founders who master AI product strategy now will own the next decade. They’ll be the ones who:

Build moats instead of chasing features.
Turn pricing into positioning instead of hiding costs.
Use stress-tested economics instead of wishful models.
Build trust with evals instead of gambling with user confidence.
Treat AI as a system, not a gimmick.

The gap between winners and losers will open faster than ever before and once it opens, it won’t close.

That’s why I built our AI Product Strategy cohort. Because no founder can afford to guess their way through this wave. Inside, we break down the playbooks, frameworks, and real-world scars so you can design AI products that are profitable, defensible, and trusted and so you can scale without breaking your economics or losing your moat.

The market will remember the founders who mastered strategy during this moment.

Everyone else will be forgotten.

The question is: which one will you be?

Join the Cohort

($550 off + get a written review of your AI Product Strategy): Don’t guess your way through the most defining wave of our time.