Intro: Why So Many Startups Get Product Building Wrong
Building software has never been easier, yet building the right product has never been harder. With modern development frameworks, AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... tools and low code platforms, startups can move from idea to prototype in weeks, sometimes days. But speed has created a dangerous illusion that progress equals success. In reality, many startups are simply building the wrong product faster.
In this episode of the DevReady Podcast, Anthony Sapountzis, CTO and Co-Founder of Aerion Technologies and DevReady.ai, is joined by Karina Carter, Fractional Chief Product Officer and Leadership Coach. Karina has over 12 years of experience spanning the United Nations, government research, startups and global technology companies. Her work focuses on helping underperforming teams realign product strategy, rediscover customer value and make better decisions before code is written.
This conversation explores why founders fall in love with solutions too early, how poor user research leads teams astray, and what it actually takes to build products customers want. It is essential listening for founders, product leaders and anyone responsible for turning ideas into scalable software.
From Research to Product: Why Speed Alone Is Not the Answer
Karina’s journey into product management began in academic research roles at the UN, where decision making was slow and impact was often abstract. Moving into product allowed her to ship faster, work directly with customers and see immediate results. However, faster execution did not mean less thinking.
One of the biggest misconceptions Karina sees today is that faster development automatically leads to better outcomes. In reality, speed without clarity often magnifies mistakes. Teams build features that users never wanted, solve problems that do not exist, or over engineer solutions for edge cases.
The key difference between successful and struggling startups is not how quickly they build, but how clearly they understand the problem they are solving. Product success starts long before development begins.
Why Founders Fall in Love with the Wrong Solution
A recurring theme in the episode is founder bias. Many founders experience a problem personally and assume it must be widespread. They then fall in love with a solution before validating whether the problem exists at scale or whether others experience it in the same way.
This issue is especially common among technical founders. Developers can quickly prototype ideas, which makes it tempting to start building immediately. The result is often a polished product that no one is willing to pay for.
Karina explains that founders must actively fight their ego and slow down just enough to test assumptions. Speaking to five customers for thirty minutes each can reveal insights that months of development cannot. Without this step, teams risk optimising for their own preferences rather than real user needs.
Product Discovery Comes Before Product Development
One of the strongest messages in the episode is that product discovery is not optional. It is the work that earns teams the right to build.
Karina describes product discovery as understanding what is happening through data, then uncovering why it is happening through customer conversations. Data alone can show patterns, but it cannot explain motivations, fears or unmet needs. That context only comes from direct engagement with users.
Importantly, customer interviews are not about asking users what to build. Customers rarely know what is technically possible. Instead, great product teams ask questions about daily frustrations, decision making pressure and the moments that keep customers awake at night. These insights allow teams to design solutions that create real value.
Why Most User Research Fails
Many startups believe they are doing user research, but Karina argues that much of it is ineffective. Poorly structured interviews, inconsistent questions and confirmation bias lead teams to hear only positive feedback. Polite customers often tell teams what they think they want to hear, not what they truly think.
Effective research requires consistency and discipline. Asking the same questions across a representative sample allows patterns to emerge. Without this structure, teams cannot distinguish between individual opinions and real behavioural trends.
Another common mistake is over weighting feedback from the loudest users. These customers may represent only a small fraction of the market and may not align with the ideal customer profile. Treating every complaint as a priority can drag products in the wrong direction.
Feedback, Prioritisation and Product Strategy Alignment
Karina explains that feedback must be evaluated holistically, not reactively. Structured tools and prioritisation frameworks help teams balance reach, impact, confidence and effort. However, prioritisation only works when there is a clear product vision and strategy.
Before deciding what to build, teams need clarity on their goals. Objectives and key results provide the context needed to evaluate whether a feature improves retention, activation or revenue. Without this alignment, prioritisation becomes guesswork.
Anthony reinforces that every product decision should be treated as a hypothesis. Ideas should be tested after launch using real data, rather than assumed to be correct upfront. This mindset reduces risk and keeps teams focused on learning rather than defending decisions.
The Role of AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... in Modern Product Teams
The episode also explores the growing influence of AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... tools like ChatGPT and vibe coding platforms. While these tools can accelerate ideation and experimentation, they also introduce new risks.
Karina shares a real example of AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... hallucinating data in a strategy document. Without careful checking, these errors can make their way into critical decisions. AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... outputs must always be reviewed, challenged and validated.
Both speakers agree that AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... is best used to support creative exploration, competitor analysis and synthesis. It should not replace critical thinking, strategic judgement or customer understanding. Over reliance on AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... can lead teams further away from the truth, not closer to it.
Why Simplicity Requires More Work, Not Less
One of the most insightful moments in the conversation is Karina’s observation that the simplest products often take the longest to design. When something feels intuitive and effortless, it is usually the result of deep thinking, iteration and refinement.
AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... generated outputs often become messier over time, not clearer. Human judgement is required to simplify, align and communicate ideas effectively. In an increasingly noisy market, thoughtfully designed, human led products may become a mark of quality.
Building software well is not about doing more. It is about doing the right work in the right order.
Building the Right Product Is a Discipline
Why do most startups build the wrong product? Because they confuse speed with progress, solutions with problems, and activity with impact. This episode of the DevReady Podcast makes it clear that building the right product requires discipline, humility and patience.
Product discovery, customer understanding and strategic alignment are not obstacles to development. They are what make development worthwhile. Teams that slow down to think, test assumptions and listen deeply are far more likely to build products that matter.
For founders and product leaders, the takeaway is simple. Building software is a privilege that must be earned. When you do the work upfront, you give your product the best possible chance to succeed.
Topics Covered
- Fractional CPOs and modern product leadership
- Moving from research and government into product management
- Data driven decision making and customer centric product strategy
- Growth mindset and radical responsibility in product teams
- User research, cognitive bias and asking better questions
- Product discovery before development and avoiding premature building
- Feedback prioritisation and aligning to the ideal customer profile
- The impact of AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... tools like ChatGPT on product development and strategy
Important Time Stamps
- From the UN to Tech Products: Karina Carter’s Journey into Product Leadership (0:37 – 5:59)
- Data Shows the What, Customers Explain the Why (6:00 – 10:55)
- Confirmation Bias Is Killing Your Product Decisions (10:56 – 14:47)
- How to Prioritise Product Feedback Without Losing Focus (14:48 – 20:41)
- Why Founders Fall in Love with the Wrong Solution (20:42 – 24:30)
- Why AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... Tools Are Helping Teams Build the Wrong Product Faster (24:31 – 27:03)
- Never Let AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... Do Your Thinking for You (27:04 – 33:41)
Key Takeaways
- Most startups fail not because of poor execution, but because they build the wrong product without validating the problem first.
- Product discovery should always come before development and is essential for understanding real customer needs and behaviours.
- User research only works when it is structured, unbiased and focused on understanding problems rather than asking users what to build.
- The loudest customer feedback rarely represents the wider market and should not drive product decisions in isolation.
- AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... tools can accelerate ideation and analysis, but critical thinking and human judgement must remain central to product strategy.
- Every product decision should be treated as a hypothesis and validated with real data after launch.
- Building simple, intuitive products requires deep thinking, iteration and discipline, not speed alone.
Useful Links
FAQs
Why do most startups build the wrong product?
Most startups build the wrong product because they fall in love with a solution before fully understanding the problem. Founders often skip product discovery, rely on assumptions or personal experience, and start building too quickly. Without validating real customer demand, teams risk solving problems that do not exist at scale.
What is product discovery and why is it important?
Product discovery is the process of understanding customer problems, behaviours and needs before development begins. It combines data analysis with customer conversations to uncover what is happening and why. Strong product discovery reduces risk, prevents wasted development effort and increases the chances of achieving product market fit.
How should startups conduct effective user research?
Effective user research uses consistent, well designed interview questions across a representative sample of users. It focuses on understanding problems, motivations and frustrations rather than asking customers what features they want. Avoiding confirmation bias and leading questions is critical to getting meaningful insights.
Should startups listen to all customer feedback?
No. Customer feedback should be evaluated holistically, not reactively. The loudest customers often represent a small minority and may not align with the ideal customer profile. Product teams should look for patterns across multiple signals and prioritise feedback that supports their product strategy and goals.
Can AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... tools like ChatGPT replace product thinking?
AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... tools cannot replace product thinking. While they are useful for ideation, research synthesis and competitor analysis, they can hallucinate data and lack context. Critical thinking, judgement and customer understanding must remain human led, with AIArtificial Intelligence (AI) is a very large and broad spectrum of technologies which most people would be familiar with through... used as a supporting tool rather than a decision maker.
When should a startup start building software?
A startup should only begin building software after it has validated the problem, understood its customers and defined a clear product strategy. Building is a privilege earned through discovery, alignment and evidence, not something to rush into. This approach reduces costly rework and increases long term success.
