Why Dogfooding Alone Won't Save Your Product

Executives at major technology firms regularly broadcast their engagement with their own platforms. Mark Zuckerberg is developing an AI agent to assist with his CEO duties. Elon Musk maintains an active presence on X. This pattern reflects a time-honored promotional tactic: those pitching tomorrow's innovations want audiences to believe they're already inhabiting that future. Yet this public performance often gets lumped together with dogfooding, a practice that has recently become standard among teams rushing to develop and release AI-powered products. When executed thoughtfully, it can identify defects, expose problematic user flows, and foster understanding of customer needs before launch. However, its effectiveness has distinct boundaries—typically far narrower than most development teams assume.

What Is Dogfooding?

Dogfooding is using your own products internally to make them better.

The terminology entered my vocabulary during my tenure at Microsoft beginning in 2013, when an enthusiastic member of the onboarding team remarked, "We eat our own dogfood here." The phrase gained traction throughout the technology sector starting in 1988, when Microsoft executive Paul Maritz distributed an email with the subject line "Eating our own Dogfood," urging staff to boost their consumption of the organization's software.

The premise is straightforward: when the people building a product also use it, they gain direct insight into whether it performs as designed. The objective involves discovering bugs, system failures, and problematic interactions before the broader user base encounters them, while also identifying absent capabilities that surface only through actual operation.

In its most effective form, dogfooding functions as a mechanism for evaluating products across diverse conditions and high volumes while fostering collective responsibility and understanding throughout the company. When a product fails, the consequences affect not just external customers but also the individuals responsible for building it. Through this direct connection between problem and consequence, engineering teams can address issues before they reach the wider market.

Dogfooding Is Not "Drinking the Kool-Aid"

Dogfooding differs from the similarly phrased concept of "drinking the Kool-Aid," which describes uncritical acceptance of organizational beliefs or practices driven by allegiance to a shared vision. However, the distinction blurs easily: numerous current instances of dogfooding amount to theatrical demonstrations of commitment rather than authentic quality assurance.

Consider McDonald's Chief Executive Chris Kempczinski, who faced ridicule online for releasing footage of himself cautiously sampling the company's Big Arch burger. The public response was swift and decisive, fairly or not: observers concluded this individual does not actually dine at McDonald's. The video gained widespread attention because audiences detected inauthenticity—a leader pretending to be a genuine customer of his own offering. The same dynamic applies when your organization's staff engages in dogfooding: they become insiders simulating usage rather than fresh-faced customers discovering the product.

The actual value of dogfooding hinges on the underlying intent: Is this purely for public relations purposes? Or would the organization pursue this approach regardless, as a legitimate method to validate system performance, independent of media coverage? When leadership's primary concern centers on investor perception, authentic quality control is unlikely to follow.

QA Testing vs. User Testing vs. Dogfooding

To those unfamiliar with these disciplines, casual employee interaction with a product might appear indistinguishable from quality-assurance work and customer research combined.

Quality-Assurance Testing

Quality-assurance (QA) testing is a structured, systematic process to evaluate that a product works as intended (i.e., the product's reliability).

For example, when a user submits a form requesting reimbursement, the system should process the submission without errors, dead ends, or technical failures. Whether the individual finds the reimbursement workflow enjoyable falls outside the scope of quality assurance.

QA professionals typically conduct this testing—specialists whose role centers on impersonating end customers and deliberately attempting to cause product failures before release.

The objective differs from simulating authentic user behavior; instead, it focuses on verifying consistent system operation by traversing every possible interaction path and identifying defects, failure scenarios, edge cases, and system state problems.

User Research

User research is a structured approach to gathering data from customers or representative users about the product or service, how easy it is to use, and how well it meets users' needs.

User researchers distinguish themselves by not posing as customers; instead, they recruit actual or representative participants to obtain direct input. Rather than systematically clicking every interface element or exploring every menu option to identify breakage points, the focus shifts to observing genuine usage patterns to assess whether users comprehend the system's behavior and can successfully reach their objectives.

Dogfooding Is Not QA or User Research

Dogfooding occupies middle ground between QA and user research, drawing on staff members as the primary feedback source while attempting to simulate "authentic" usage patterns.

Yet can such usage truly be authentic when the employee possesses deeper familiarity with technical terminology than typical end users and already grasps the underlying data architecture and system interconnections? The prevalent—and typically mistaken—premise assumes that end users operate from the same conceptual framework as internal staff. Experience repeatedly demonstrates otherwise: customers approach the product from fundamentally different perspectives, with distinct prior knowledge and different expectations. This represents the "curse of knowledge" phenomenon: once you understand how something operates, you cannot accurately simulate the experience of not understanding it.

Consequently, usability observations from staff can skew research conclusions and even contradict findings from actual user research.

When applied appropriately to strengthen system dependability, dogfooding augments conventional QA testing: additional personnel examining and testing a system uncovers additional failure modes and generates more data points. It can also establish an upper limit on system usability: if staff members find the interface confusing, customers will almost certainly struggle more. (However, staff success does not guarantee that actual users will experience the same ease.)

The following table summarizes the distinctions:

• QA Testing: Reliability in overall functionality of the product or service
• User Research: Usability, user comprehension, and user-needs analysis
• Dogfooding: Internal staff's perspective on reliability and usability within a semirealistic context

Reliability: Crucial in the AI Era

Distinguishing between reliability and usability matters significantly, as these represent two separate requirements for successful products. A product must achieve functional dependability before it can deliver usability. A system that crashes during tasks or skips workflow steps cannot be usable by definition. For deterministic systems, even those with intricate workflows, reliability assessment proves relatively straightforward: when a user performs action x, result y consistently follows.

Nondeterministic AI models complicate reliability evaluation, since these systems generate probabilistic rather than deterministic outputs. Assessing reliability now requires examining multiple factors:

• Does the system consistently deliver accurate answers?
• Does it select the appropriate data sources by default?
• Does it incorporate the user's circumstances? Does it apply that context correctly?
• What safeguards exist to prevent severe failures or unpredictable behavior?

Dogfooding can help address some of these considerations: when multiple individuals with varying perspectives engage with the system, they can compare their interpretations of its behavior.

Nevertheless, dogfooding cannot substitute for QA and user research. It surpasses doing nothing, but retrofitting a design based on staff input proves more expensive than building with customer feedback from the beginning.

Should You Dogfood?

Dogfooding can serve as a supplementary information source during product development. It functions most effectively alongside alternative approaches that deliver more targeted, more practical insights. Each method addresses distinct questions, and none can replace the others:

• Dogfooding: Does the product function? Does it fail? Can the team identify obvious missing elements?
• QA testing: Does the product contain failure points, bugs, or overlooked areas? Are there gaps in functionality, error handling, or exception management that require design attention?
• User research: Can a person unfamiliar with the product reach their goals? What are users attempting to accomplish, and how does the product align—or fail to align—with their actual circumstances?

Before launching any feedback initiative, consider: whose viewpoint are we capturing, and is that the viewpoint we require? If your staff represents the sole source of feedback on a design choice, you possess internal opinion, not user research.

Ultimately, dogfooding reveals how your organization perceives your product. Research demonstrates what your customers actually experience.