A new MIT and Wharton paper, Writing Code vs. Shipping Code, finds that agentic AI coding tools have driven a sharp jump in commits, projects, and app releases, but the gains shrink as work moves through delivery and iOS app usage has barely moved. It looks like a replay of the dot-com era. Producing software got radically cheaper while the hard parts (understanding customers, building trust, earning attention, distributing, and shipping value) stayed exactly as hard as they ever were. When production becomes abundant, value migrates. In the AI era it appears to be migrating toward discovery, validation, distribution, knowledge management, and trust.
A fascinating new study from MIT and Wharton is challenging one of the most common narratives in technology today.
The headline sounds impressive:
Agentic AI coding tools are driving an explosion in software creation.
The data backs that up. According to the paper Writing Code vs. Shipping Code, AI coding tools have dramatically increased developer output. Commits are up. Projects are up. App releases are up. In some cases, coding activity has increased by more than 100%.
But there is a catch.
The researchers found that these productivity gains shrink dramatically as work moves through the software delivery process. By the time code reaches actual software releases, much of the gain has disappeared. More importantly, while iOS app releases have surged since the arrival of agentic AI tools, app usage has barely moved.
In other words:
We are producing far more software, but not necessarily creating more value.
That observation immediately reminded me of the early 2000s.
During the dot-com boom, the breakthrough wasn’t that companies suddenly discovered customers. The breakthrough was that they discovered websites.
Almost overnight, everyone could launch a .com.
The barriers to publishing online collapsed. Web hosting became easier. Development tools improved. Capital flowed freely. Thousands of startups appeared.
But many of those companies failed for a simple reason:
Building a website was never the hard part.
I remember watching companies spend enormous amounts of money building impressive online storefronts, only to discover that their real problems had nothing to do with technology.
Selling 40-pound bags of pet food online sounds revolutionary until you realize somebody still has to ship them.
Selling CDs online sounds simple until you have to manage inventory, customer acquisition, fulfillment, returns, and margins.
The bottleneck wasn’t HTML.
The bottleneck was business execution.
The same pattern may be emerging with AI. (Related read on this exact tension between agentic coding and shipping discipline: Vibe Coding Is Amazing. It’s Also a Lot.)
AI has dramatically reduced the cost of building software. It has not reduced the cost of understanding customers, building trust, creating distribution, earning attention, or solving meaningful problems.
The MIT researchers describe this as a “weak-link” problem. AI can automate coding, but the rest of the process still depends heavily on human judgment, review, coordination, testing, integration, marketing, and customer adoption.
That is why app releases are soaring while app usage remains flat.
We haven’t suddenly created millions of successful software companies.
We’ve simply made software creation much easier.
When production becomes abundant, value migrates elsewhere.
During the dot-com era, value migrated toward logistics, operations, fulfillment, customer acquisition, and trusted brands.
In the AI era, value may be migrating toward discovery, validation, distribution, knowledge management, and trust.
This is why I believe many organizations are asking the wrong question.
The question is no longer:
“Can we build it?”
AI has largely answered that.
The question is:
“Should we build it?”
And perhaps more importantly:
“Can we get anyone to care?”
The winners of the next phase of AI won’t necessarily be the companies that ship the most software.
They’ll be the companies that still know what is worth shipping.
Frequently Asked Questions
What did the MIT and Wharton “Writing Code vs. Shipping Code” study find?
It found that agentic AI coding tools have driven a sharp increase in raw developer output. Commits, projects, and app releases are all up, with coding activity rising more than 100% in some cohorts. The catch is that the gains compress as work moves through the software delivery process, and iOS app usage has barely changed since the tools arrived. More software is being produced, but not noticeably more value is reaching users.
Why is software production rising while usage stays flat?
AI radically reduces the cost of writing code, but not the cost of understanding customers, building trust, designing experiences, earning distribution, or doing the messy work of shipping. The researchers call it a “weak-link” problem: AI automates coding while the rest of the delivery chain still relies on human judgment, review, coordination, testing, integration, marketing, and customer adoption. The weak link gates the value.
How is the AI app boom similar to the dot-com era?
In both cases, a wave of new tooling collapsed the cost of production. Anyone could launch a website in 2000. Anyone can ship an app today. In both cases, the easy production created the impression of a revolution while the real bottlenecks (logistics, operations, fulfillment, brand, distribution, customer acquisition) stayed exactly as hard as they were before, and the companies that ignored those bottlenecks failed.
What is a “weak-link” problem in agentic AI software delivery?
A weak-link system is one whose performance is gated by its slowest, most fragile component, not its strongest. Agentic coding tools strengthen one link (writing the code) while the rest of the chain (review, integration, testing, deployment, support, marketing, distribution, retention) stays human-paced. Speeding up one link doesn’t speed up the chain. It just produces more work piled up against the unchanged bottlenecks.
Where does value migrate when AI makes production abundant?
Toward the parts of the chain AI does not yet automate well: discovery, validation, distribution, knowledge management, and trust. Choosing the right problem becomes worth more than building any given solution. Reaching the right user becomes worth more than shipping more features. Trustworthy brand, clean knowledge, and direct customer relationships become structural advantages because they cannot be cheaply duplicated by a coding agent.
What should organizations be asking instead of “can we build it?”
Three sharper questions. Should we build it (does it solve a real problem worth solving). Will anyone use it (do we have a credible path to attention, trust, and adoption). And can we actually ship and operate it well (does our delivery chain absorb the new pace without breaking on testing, integration, support, and governance). AI answered “can we build it” for most teams; the harder questions are the ones it cannot answer for you.
Does this mean agentic AI coding tools are overhyped?
No. The productivity gain in writing code is real and large. The mistake is treating that gain as a complete software-economics story. The dot-com era teaches the same lesson in reverse: cheap websites were a genuine breakthrough that still required cheap shipping, working fulfillment, real brands, and actual customer relationships to translate into durable companies. The shipping side of AI is where most of the unanswered work now lives.
