Code Isn’t Product

Photo by Juanjo Jaramillo on Unsplash

A year ago I wrote about how AI would shift the bottlenecks in product development from the middle of the cycle — writing code — to the end, where we help customers understand what we built, try it, and turn it into value (money). And to the front, where we figure out what’s worth building.

Time for a check-in.

What I’m seeing is something more acute than a bottleneck shift. It’s a market-driven confusion between creating code and creating products. That distinction is about to get more obvious.

The 100x Problem

AI is making it possible to ship 100 times as many new products (or features) as before. The engineering throughput is becoming real. Boards are impressed. CEOs are under pressure to do much more with less R&D. AI is the stated justification for slashing engineering/product/design teams. (Meta, Amazon, Intel, Microsoft, Alphabet…)

Here’s what isn’t scaling: customer attention.

Your prospects don’t have 100 times the bandwidth to evaluate new offerings. They’re receiving more pitches, not fewer. (Cf: AI slop.) Their buying processes haven’t accelerated. Budgets aren’t going up.

Channeling April Dunford, this is a positioning problem. The dozen words you use to describe a new product matter more than ever. In a world with 100x more offerings, it’s what will determine whether anyone even hears about yours.

And positioning that works is built from the language your actual users actually use to describe their perceived problems — not the language your engineers use to explain the solution. Which means actually talking with customers and prospects, rather than synthetic users. A lot. Maybe even before production code is shipped.

We are dramatically increasing the urgency of doing good discovery. But that’s not in the zeitgeist right now, so expect a summer of DOA products.

The Forward-Deployed Engineer Tell

No surprise that forward-deployed engineers are trending. Expert, dedicated technical resources placed on-site at enterprise customers, helping them figure out how to plan and deploy and extract value from AI-enhanced products. I understand the appeal. It sounds proactive and customer-centric.

For me, this is evidence of shipping tools or kits or frameworks or platforms, rather than finished products that solve well-defined problems.

If your customers need a full-time engineer sitting next to them to understand what your product is supposed to do, how it fits their environment, and how they measure success from it… these are product problems, not deployment problems. (Or you’re eager to adopt a professional services model.) The discovery and decision work that should have happened before launch is now happening after the contract is signed, on the customer’s dime or yours.

Forward-deployed engineers are expensive, hard to hire, and don’t scale. They are a workaround for patchy product thinking. When I see a company moving urgently this way, I see bits winning out over solutions.

The Next Bench Syndrome, Revisited

There’s another model getting heavy promotion right now: solo product-engineers building end to end without committees or reviews or management overhead. They trust their own judgment, shipping 50x faster. This model works in a narrow set of circumstances.

At HP, this dynamic was called the next bench syndrome [1]. Engineers built tools for the person sitting at the next bench — someone whose problems, language, workflows, and buying behavior they understood intimately. When your user is essentially you, you can trust your own taste. The feedback loop is tight, the intuitions are well-calibrated, the product decisions are reliable.

The general case is a problem, though. Engineers often aren’t adept at extracting what veterinarians need, or teachers, or firefighters, or government employees managing complex procurement processes. Users have different problems, different language, different success metrics, and very different buying behaviors. Building for them requires going out into the world to find out — systematically — what’s true.

The product-engineer model isn’t a universal pattern. Extrapolating it to the general market is how we get waves of technically impressive, commercially disappointing products.

What Comes Next

I expect the next two to four quarters to be even more painful for product people. Boards will keep pushing for speed. Engineering throughput will keep climbing. Upcoming AI IPOs will be astronomical. The pressure to treat shipping as succeeding will intensify, because the metrics look great in the short run. We’ll be Cassandras: punished for predicting problems.

Most of these products won’t stick — not because the code is bad, but because the discovery was skipped; the positioning was wrong; the pricing and economics were slapdash; the deployment barriers were underestimated; and the users weren’t who the builders imagined. These aren’t new failure modes. They’re the same ones we’ve seen across every technology transition. AI gets us there much faster.

When that reckoning comes, two things will become valuable again: strategic product thinking at the front of the cycle, and brilliant product marketing at the back. Not as overhead, but as work that determines whether code turns into revenue.

We’ve learned this lesson before. Tuition is about to go up.

Sound Byte

AI is giving us 100x the code. Nobody is giving us 100x the customers or 100x the revenue.

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[1] Bill Hewlett coined this term. From John Minck’s internal history of HP: “The Hewlett-defined ‘next-bench syndrome’ was another key to our creativity. Not only did every design engineer use HP test equipment every day, but they also lived next to other groups who were doing similar but adjacent measurements. The informal culture in the labs afforded a lot of cross-fertilization at the traditional coffee breaks twice a day.”
The same mechanism that drove innovation in instruments became a liability when HP entered consumer markets — calculators for non-engineers, personal computers, printers.

BTW I’m honored to have been at HP in the early 80’s. A visionary company with world-class products, great leadership, thoughtful long-term employee practices, and dedicated to serving the broader community as well as shareholders. Bill and Dave pioneered everything that made Silicon Valley humane and inspiring.