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“AI Product Manager” is the new hot new title. It’s in demand, sounds exciting, and, in NYC and Silicon Valley, pay more. LinkedIn, podcasts, and pronouncements by Big Tech can give you a serious case of FOMO.
But is it sustainable?
To answer that, we need to understand how AI businesses actually work.
I’ve been there. I worked at an AI startup and launched a GenAI product. What I learned isn’t what the hype tells you.
Today, launching an AI app has never been easier. Every week we hear about someone who couldn’t code six months ago, now vibe coding a product, raising rounds, and talking about “changing the world.”
But peel back the hype, strip away the self-serving podcasts, investor decks, and VC-fueled valuations, and you see a more sobering reality:
Most AI businesses are terrible.
As a Product Manager — whether you’re building an AI product, considering joining an AI company, or watching your own company pour money into AI — you need to understand the economics.
Because your job and career depend on it.
So, today, I’m going to unpack the reality and talk about what it means for you.
What You’ll Learn Today:
The economics behind ChatGPT, Copilot, and MidJourney
Why AI apps bleed money while SaaS prints profits
The real cost of every “free” user
How hype inflates conversion numbers that don’t add up
How to tell if your AI product or feature is value or just veneer
The signs your company’s AI strategy is smoke and mirrors
How to just if that shiny AI PM job offer is actually sustainable
The one question every PM should use to cut through the hype
Inside Today's Issue:
The Reality: AI Unit Economics Are Brutal
Traditional SaaS vs AI Apps
Do you know why SaaS took off?
Not because of slicker UIs, better features, or Agile.
Customers weren’t clamoring for SaaS. In fact, many CFOs often preferred the old on-premise model because they could book it as an asset on the balance sheet instead of an expense on the income statement (where a SaaS purchase lives).
SaaS took off because investors and boards loved the economics:
Once the platform was built, new users were almost free.
This near-zero marginal cost per new user meant wildly profitable growth.
Variable costs (implementation, success, support) could be scaled with growth, and often paid for by the customer.
Even enterprise deployments charged customers for implementation — something they were already used to with on-premise installations.
The result? 70–90% margins.
Further product investments were meant to keep churn down, renewals high, and extend customer lifetime (LTV).
As long as lifetime value (LTV) was 3x+ acquisition cost (CAC), SaaS delivered hockey-stick growth.
AI apps flip that model. Every new user creates new costs:
API calls
Compute time
Model licensing
Output moderation
This results in 30–60% margins at best. Even Anthropic’s Claude sits around 55%.
And some of that margin may come from “dead subs” — paying but inactive users, often acquired from bundled AI app deals. Those deals hide this, but those are the customers most likely to churn.
I saw this firsthand.
Our board wanted 85% margins. My analysis showed 60% max — and only if we priced high enough to lose the low-end of the market. That risked shrinking our addressable market and limiting our growth. More realistic was 35–45%.
The culprit: ML processing costs. More complex use cases meant more GPU resources, which meant skyrocketing costs.
That’s why:
GitHub Copilot cost Microsoft $30 per user/month on average (and up to $80 for power users) while charging just $10.
MidJourney had to cap image generations.
Even OpenAI had to meter ChatGPT Plus because active users cost more than they paid.
This isn’t software as usual. Here, growth - more cost. Sometimes exponentially.
The Conversion Problem
ChatGPT wants 1 billion users by EOY. Today: ~700–800 million users. Sounds impressive.
But only ~10 million pay for ChatGPT Plus. That’s ~2% conversion.
Even the rosiest estimates put it at 5–7%. Still shockingly low given the hype and reach. And abysmal compared to the average SaaS product.
And remember: free users aren’t free. Compute costs money.
The Hype Cycle Trap
This is where GenAI is right now:
Mass reach today comes more from media hype than product value. Even foundational AI companies are deeply unprofitable. Their current economic models can’t sustain themselves.
Sitting at the peak of the hype cycle isn’t correlated with long-term benefits.
Blockchain. Crypto. Augmented reality. All rode hype cycles to the moon before crashing down to earth.
Sure, LLMs will get better. Cars got better too. Today we have Teslas and Porsches, sedans and SUVs and trucks, EVs and plug-in hybrids. But they’re all cars. The fundamental economics of the car business remain the same, because the product delivers essentially the same value.
Can a software company stay unprofitable for years? Sure — if they keep raising cash off hype. But hype has limits.
Like mobile, social, and streaming, eventually LLMs will commoditize. Drastic changes in their pricing will be inevitable, as that will be the only way to fuel profitable product growth and maintain market share. (Just look at Netflix, Disney+, Apple TV+, Amazon Prime Video, and other streamers raising their prices or adding ads.)
AI may be transformative. But as a business model, AI-native SaaS isn’t the slam dunk many assume.
Where AI Businesses Could Work
Some AI startups have found viable niches, especially in industries drowning in complex workflows like HR, sales, accounting, legal, finance, and healthcare administration.
Examples:
Automating contract and invoice matching
Connecting CRM and contract systems
Streamlining back-office document processing
Clinical documentation improvement and medical coding
Maybe not billion-dollar moonshots. But the potential to be real, sustainable businesses.
The common thread? They solve a real, painful problem where AI itself isn’t the product, it’s the accelerant.
Keeping It Real as a Product Manager
So, how do you take all this noise and apply it to your world? Let’s consider 3 areas:
Evaluating the success of your AI product
Evaluating your employer’s AI investments
Evaluating an AI Product Manager job opportunity
1. Evaluating the Success of Your AI Product
Don’t get distracted by vanity metrics. If you’re building an AI product, ask:
Conversion: Are users converting at healthy rates, or are you burning money serving them?
Margins: What does it actually cost to serve one additional customer? Are you scaling revenue or scaling losses?
Retention: Do users stick around after the hype wears off?
Value without AI: Does your product solve a real problem? If it’s value is solely based on the fact that’s it AI, you probably have a hype play.
If your AI product or feature is just a wrapper on GPT with no defensibility, you’re in trouble.
2. Evaluating Your Employer’s AI Investments
Every company wants to “do AI.” But does your employer actually have a strategy? Or are they just throwing darts?
Look for signs of substance:
Clear vision and strategy: Can leadership articulate how AI investments will pay off?
Grounded business model hypothesis: Are they investing in discovering a sustainable business model with solid unit economics to fuel sustainable growth?
Defensible value: Does the company own proprietary data, workflows, integrations, or patents that give it an edge? Or is it just reselling OpenAI’s API?
Pragmatism over hype: Are AI features bolted on to hit investor buzzwords, or truly embedded in solving real customer problems?
If you don’t see these signals, your company may be wasting cash on AI experiments that never pay off. At this level of investment, failed experiments inevitably lead to layoffs.
3. Evaluating an AI PM Job Opportunity
Before you jump on board, dig into the same questions as above. It’s fun to be on the cutting edge, but not at the cost of being out of a job in 1-3 years because of inflated expectations and poor strategy.
And if it’s a startup, check on these:
Target market definition: Do they have a clearly defined target market with a well-understood problem and clear revenue potential?
Positioning: Is AI the only value prop, or is it enhancing a real solution to a painful customer problem?
Customer acquisition and conversion rates: How are they acquiring customers? Are people actually paying, or is it a free-user bonanza?
Customer retention: Do they have repeatable, sticky use cases? Or just hype-driven spikes in interest and traffic?
Margins and costs: Is their current pricing providing sustainable margins for growth? Do they understand the costs to serve a user? To add a new user? If they dodge, that’s a red flag.
Funding dependency: Did they raise money because of AI hype and investor FOMO, or because of viable economics in the business? Could they survive without another VC round? If not, how long is the runway and what are the critical milestones to hit to get to the next round?
Path to profitability: Is there a clear plan to get to operating profitability?
If the economics don’t work, the job may not be sustainable — no matter how shiny the job description looks.
Your PM Playbook
AI is not proven as a business model. Margins are dramatically lower, costs scale with usage, and price wars will become fierce.
Conversion rates are weak. Even the best-known AI product on the planet has <2–5% conversion.
Retention and real value matter most. If the product is standing solely on its use of AI, it won’t stand for long.
Beware of hype-driven strategies. Look for coherent business models, not buzzword-chasing.
Treat AI as an accelerant, not the core. The strongest businesses solve real problems, with or without AI.
The One and Only Question You Should Be Asking as a PM
AI is incredible technology. Here to stay. But incredible technology does not automatically make a great business. Nor a sustainable job.
The companies (and Product Managers) who win won’t be the ones with the best vibe coded prototypes, the flashiest demos, or “we raised $100M” headlines.
They’ll be the ones asking the unglamorous question:
“Where is the repeatable, profitable value?”
Because in the end, hype burns cash and kills jobs. Value builds businesses and sustains communities.
That’s it for this weekend.
Have a joyful week, and, if you can, make it joyful for someone else too.
cheers,
shardul
Download These Additional Resources for Your Learning:
Briefing Doc summarizing the key takeaways from this essay
Study Guide of the core lessons to accelerate your learning
FAQ of the essential concepts to enhance your critical thinking
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Shardul Mehta
I ❤️ product managers.