Recently, I started hearing the word dogfooding everywhere.
AI companies were dogfooding their coding agents. Product teams were dogfooding new features internally. Startups were describing how their first users were their own employees.
The term sounded like something that had emerged from the current AI wave. But when I looked into it, I found that it was much older.
The expression “eating your own dog food” was popularized in the software industry by Microsoft in the late 1980s. The basic idea was simple: if a company builds software, its own employees should use it in their real work.
This was not just another form of quality assurance.
A test environment can confirm whether a feature works under predefined conditions. Dogfooding exposes the product to real users, real workflows, real pressure and real consequences.
When employees depend on their own company’s unfinished software to do their jobs, problems become difficult to ignore.
Confusing interfaces create real frustration. Missing functionality blocks actual work. Reliability problems affect productivity. Assumptions made during product development collide with everyday usage.
Dogfooding creates a direct connection between building a product and experiencing what it is like to depend on it.
That practice never disappeared.
But AI is changing where dogfooding begins, who can use it and what role it can play in creating new products.
The traditional dogfooding flow
Dogfooding has traditionally been associated with established software companies.
A company was already building a product. It had product managers, developers, infrastructure, internal users and a defined roadmap. At some point during development, the product was deployed internally.
The flow looked something like this:
Build the product → deploy it internally → use it in real work → discover problems → improve it
The product decision came first.
The company had already decided that the software should exist. Dogfooding helped validate, test and improve that decision through internal use.
This model still has considerable value.
Internal users can identify problems earlier. Development teams get access to detailed feedback. The company gains experience operating the product before exposing it to a wider customer base.
However, this kind of dogfooding was easier for certain companies than others.
It worked particularly well when the company itself resembled the target customer.
A software company could use its own developer tools. A communication platform could use its own messaging system. A cloud provider could run internal workloads on its own infrastructure.
It was less accessible to individuals, small teams and companies without the capacity to build internal software around every problem they encountered.
Building even a simple internal application usually required time, budget and dedicated engineering capacity.
Many useful ideas never went further than a spreadsheet, a manual process or an item waiting indefinitely in an internal-tools backlog.
AI changes the starting point
AI-assisted development is reducing the cost of turning small problems into functioning tools.
A developer can build a first version much faster than before. A small team can automate a narrow workflow without starting a formal software project. In some cases, people without a traditional development background can create simple applications through natural-language interaction with coding tools.
This does not mean that building reliable software has become effortless.
Security, maintenance, architecture, testing and operations still matter. Turning a prototype into a dependable product remains difficult.
But the cost of getting from an idea to something usable has changed significantly.
That changes the dogfooding flow.
Instead of beginning with a formal product decision, it can begin with a real problem:
Experience a problem → build a tool → use it yourself → improve it through repeated use → evaluate whether it could become a product
The first user is no longer someone recruited after the product has been designed.
The first user is the person who experienced the problem strongly enough to build something for it.
This is more than product testing.
It is product discovery through firsthand use.
Building for yourself is no longer only a side project
For a long time, “build something for yourself” was common advice for developers looking for side-project ideas.
The reasoning was straightforward: you understand your own problem, you can start without lengthy research and you have immediate access to at least one user.
But many of these projects remained small experiments.
The effort required to build, maintain and improve them was often too high compared with the size of the original problem.
AI changes that calculation.
Problems that were previously too small to justify custom software can now become reasonable starting points.
A repetitive reporting task may become a small internal dashboard.
A complicated spreadsheet workflow may become a focused application.
A manual content process may become an AI-assisted tool.
A personal learning method may become a reusable product.
A small operational frustration may become an automation that a whole team depends on.
The important part is not simply that AI allows more software to be created.
That can also lead to an enormous amount of disposable software: prototypes that are built quickly, demonstrated once and never used again.
Dogfooding provides a filter.
A tool becomes meaningful when it enters a real workflow.
You use it again the next day.
You notice where it slows you down.
You discover that a feature you considered essential is unnecessary.
You find that a supposedly minor limitation prevents regular usage.
You change the tool, return to it and observe whether the change actually helps.
The product evolves through use rather than imagination.
The new dogfooding loop
The modern dogfooding loop can be described in five stages.
1. Start with a problem you genuinely experience
The strongest starting point is not an abstract idea for a product.
It is a recurring problem with enough friction that you already spend time, attention or money working around it.
This gives the project an immediate source of context.
You understand when the problem occurs, what causes it, what existing alternatives fail to solve and what a useful outcome would look like.
That is much stronger than starting with a technology and searching for somewhere to apply it.
2. Build the smallest tool that improves the situation
The goal is not to design the complete future product.
It is to create something useful enough to enter the real workflow.
This may be an internal web application, a script, an AI agent, a browser tool or a lightweight mobile application.
The first version should solve enough of the problem that you are willing to rely on it.
A demonstration is not enough.
A real dogfooding loop begins when the tool has to perform useful work.
3. Use it under real conditions
This is the difference between dogfooding and casually testing a prototype.
When you dogfood a tool, you do not use it only during a planned feedback session.
You use it when you are busy.
You use it with incomplete information.
You use it repeatedly.
You use it in the situations where the original problem actually happens.
This reveals issues that are difficult to predict during design.
You may discover that the tool technically works but interrupts the workflow. It may produce good results but require too much preparation. It may save time in one step while creating additional work somewhere else.
These are product insights, not just software bugs.
4. Improve it based on experienced friction
When the builder is also the user, the feedback loop becomes extremely short.
There is no need to schedule an interview to learn that a button is difficult to find or that a workflow contains an unnecessary step.
The friction is experienced directly.
This can lead to rapid improvements, but only when the builder is honest about the experience.
It is easy to excuse problems in something you created yourself. You know the shortcuts. You understand the hidden logic. You remember why strange decisions were made.
Real dogfooding requires treating your own frustration as evidence rather than defending the design.
5. Decide whether the tool should remain internal or become a product
Not every useful internal tool should become a commercial product.
Some problems are too specific. Some depend heavily on one company’s processes. Some are useful but do not create enough value to support a business.
Dogfooding provides evidence that the problem is real for at least one person or team.
It does not prove that a market exists.
The next step is to look beyond the original user.
Do other people experience the same problem?
Do they solve it in a similar way?
Is the problem painful enough that they would change their current behaviour?
Can the solution work outside the environment where it was created?
Would someone trust the tool without direct access to its builder?
Dogfooding can produce the initial product insight. External validation is still required before turning that insight into a business.
More people can now become the first user
One of the most important changes is that dogfooding is becoming accessible to a much wider group.
It is no longer mainly a practice for large software companies with thousands of internal users.
An individual can build and use a personal tool.
A developer can automate part of their own workflow.
A small team can create a solution for an internal bottleneck.
A department can prototype a tool before requesting a larger company-wide system.
A founder can operate with the first version of a product before looking for early adopters.
A company can test a new capability inside its own operations before deciding whether it belongs in its customer offering.
This creates a more gradual path from problem to product.
Previously, there was often a large gap between having an idea and building something useful enough to test.
Now, the initial solution can emerge much closer to the problem itself.
That does not automatically produce better products.
But it creates more opportunities for products to begin from genuine usage rather than speculative requirements.
I was dogfooding before I knew the term
Learning about dogfooding also made me recognize a pattern in several of my own projects.
Some started as personal or internal tools.
There was no formal product strategy at the beginning. I was trying to solve something for myself, my family or a small group of people close to the problem.
Because I was also one of the first users, I could experience the weaknesses directly.
Features that seemed valuable during development sometimes mattered very little in actual use. Small inconveniences became more important after repeated exposure. Real usage suggested improvements that I would not have discovered through planning alone.
Only after using the tools did the product question appear:
Could this also be useful to other people?
That sequence matters.
The product did not begin with a broad statement such as “there must be a market for this.”
It began with evidence that the problem existed and that the solution was useful in at least one real context.
That is not enough to confirm a successful product, but it is a much more grounded starting point.
Dogfooding is not the same as building whatever you want
There is a risk of romanticizing the idea of building for yourself.
Being your own first user has clear advantages, but it also creates blind spots.
You know the product too well.
You do not need onboarding because you already understand its purpose.
You can tolerate incomplete features because you know what is coming next.
You can manually recover when something fails.
You may represent only a small and unusual part of the potential market.
The tool may fit your workflow precisely while being too rigid for anyone else.
This is why dogfooding should be treated as an early learning mechanism, not as complete product validation.
The builder’s experience can answer questions such as:
- Does the problem occur frequently enough to matter?
- Does the solution provide real value?
- Which parts of the workflow create the most friction?
- Is the tool useful enough to become part of regular behaviour?
- What needs to improve before another person can use it?
It cannot answer all the questions required for a market-ready product.
External users are still needed to test whether the problem is shared, whether the value is understandable and whether the solution works outside its original environment.
Dogfooding provides depth.
External validation provides breadth.
A useful product needs both.
The danger of fake dogfooding
As dogfooding becomes fashionable again, it can also become performative.
A company may require employees to use a new internal AI tool and describe the rollout as dogfooding.
But forced usage is not always meaningful usage.
Employees may use the tool because management expects it. Teams may search for artificial use cases to improve adoption numbers. Feedback may become overly positive because the product is associated with an important internal initiative.
The company sees activity and assumes that it is learning.
This is fake dogfooding.
Real dogfooding is driven by a genuine need.
The tool becomes part of the workflow because it helps people accomplish something. When it fails, people care because the failure affects their work.
Fake dogfooding is driven by the need to demonstrate adoption.
The use exists mainly because someone decided that the product should be used.
A simple question can help separate the two:
Would people continue using this tool if nobody measured or rewarded its usage?
When the answer is no, the organization may be measuring compliance rather than product value.
This does not mean that internal adoption must always be completely voluntary.
New tools often require training, migration and an initial push. People may not immediately change established habits even when a better solution exists.
But the goal of internal rollout should be to discover whether the tool creates real value—not to prove that the original product decision was correct.
Negative feedback is part of successful dogfooding.
A tool that is genuinely used will expose uncomfortable problems.
That is the point.
AI makes building easier. Dogfooding makes it grounded.
AI is enabling more people to create software.
That is a major change, but it creates a new problem: when building becomes easier, deciding what deserves to be built becomes more important.
The world does not necessarily need every application that can now be generated.
It does not need endless prototypes built around imagined demand.
It does not need internal tools that exist only because using AI has become an organizational objective.
Dogfooding cannot solve every product problem, but it can keep creation close to real usage.
It starts with someone who genuinely needs the tool.
It puts the solution into a real workflow.
It creates a repeated cycle of building, using and learning.
And it delays the larger product decision until there is at least some firsthand evidence that the solution provides value.
The traditional form of dogfooding helped software companies improve products they were already building.
The AI-era form can begin earlier.
It can help individuals, teams and companies discover products through their own everyday problems.
That is why dogfooding feels newly relevant.
Not because an old practice disappeared and suddenly returned.
But because AI has moved it closer to the beginning of the product journey—and made that journey accessible to many more people.