Author Archives: Itamar Novick

About Itamar Novick

I’m a Berkeley-Haas MBA student and formerly the Director of Product Management at Gigya, the leading social optimization platform. I’m almost an internet veteran and a huge fan of Social Media.

Startup anti-pattern #10: Boiling the Ocean

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First, a Story, or two

In the early 2010s, Magic Leap captivated the tech world with its ambitious vision for augmented reality. Backed by billions in funding from companies like Google and Alibaba, it promised a revolutionary AR headset that would change the way people interacted with digital content. The problem? Magic Leap wasn’t just trying to build a better AR device—it wanted to redefine how humans experience reality itself.

Instead of focusing on a single killer application or perfecting the core hardware, the company attempted to tackle everything all at once: proprietary optics, a new software eco-system, a custom operating system, and a full-scale content platform. Years passed, hundreds of millions were spent, and while competitors like Microsoft’s HoloLens and even Apple’s Vision Pro made incremental, targeted progress, Magic Leap struggled under the weight of its grand ambitions. By the time it finally released a product, it failed to live up to expectations, and the company had to pivot away from consumers entirely.

Magic Leap didn’t fail because it lacked vision—it failed because it tried to boil the ocean.

Another example. In 2011, Color Labs launched with one of the most hyped startup debuts in Silicon Valley history. Backed by $41 million in funding before even releasing a product, Color aimed to completely redefine social media by creating a location-based photo-sharing network that would dynamically connect users based on proximity.

The problem? It was trying to solve too many complex challenges at once. Instead of focusing on a simple, engaging user experience, Color attempted to build a revolutionary social graph, an advanced AI-driven photo categorization system, and a new way for users to interact in real-time—all before proving product-market fit.

The result? A confusing, overly complex app that nobody understood or used. Despite the massive funding, Color Labs never found a core audience, and within two years, the company collapsed, selling off its remaining assets to Apple in a fire sale.

What It Is

“Boiling the ocean” is an anti-pattern in which startups attempt to tackle an impossibly large or complex problem(s) all at once, spreading themselves too thin and failing to deliver meaningful progress.

Startups fall into this trap for several reasons:

  • Overambition and arrogance – Founders want to change the world and aim too broadly instead of focusing on achievable, measurable, milestones. It’s easier, especially for highly technical founders, to just stay in “builder” mode being heads-down on a fun project than it is to actually get the product to market
  • Investor Pressure – Big visions attract big funding, but when founders feel compelled to deliver too much too soon, they lose focus. Investors are sometimes part of the problem – pushing the company too go big or go home.
  • Fear of Competition – Trying to solve everything at once to prevent competitors from filling the gaps. It helps avoid having to “chase the competition”
  • Unrealistic Technological Scope – Underestimating the difficulty of building multiple breakthrough technologies simultaneously. This is a pitfall I see sometimes when the founding team is very engineering heavy, or engineering takes over the business.
  • Failure to Prioritize – Simple put – When everything is a priority, nothing is. One feature has to be more important than another, and if they all have the same importance it means you haven’t done a good enough job understanding what matters most.

The result? A company that lacks focus, burns through capital, and struggles to gain traction in any one area.

Why It Matters

Boiling the ocean could be a death sentence for most startups because of their inherent constraints:

  • Limited Resources – Startups have small teams, finite capital, and limited time to prove themselves. Spreading too thin means nothing gets done well.
  • Delayed Execution – Attempting to tackle everything all at once leads to bloated timelines, slow iteration, and missed market opportunities.
  • Increased Complexity – The more ambitious the scope, the harder it is to maintain focus, align teams, and execute efficiently.
  • Failure to Deliver MVP – Without a clear MVP (Minimum Viable Product), startups struggle to test hypotheses, attract early adopters, and gain traction.
  • Burnout & Team Frustration – Engineers, designers, and product teams get demoralized when progress feels slow and unfocused.

History is full of examples of companies that tried to do too much at once and collapsed under their own weight. Theranos, for example, promised a blood-testing revolution but attempted to engineer technology far beyond what was feasible within their timeline, ultimately leading to scandal and collapse.

Meanwhile, the most successful startups—Amazon, Tesla, Google—started small, iterated, and expanded. Jeff Bezos launched Amazon as an online bookstore before moving into other categories. Tesla focused on one premium car (the Roadster) before expanding into mass-market models. Google started with search before conquering advertising, email, and cloud computing.

Diagnosis

How do you know if your startup is falling into the “boiling the ocean” trap? Ask yourself:

  • Are we trying to solve too many big problems at once?
  • Is our product roadmap filled with major projects that will take years to materialize?
  • Are we constantly pivoting or expanding scope without shipping anything meaningful?
  • Do investors, employees, or customers struggle to clearly articulate what we do?
  • Do we lack a clear MVP that we can launch and test quickly?

If you answer “yes” to multiple questions, you’re likely taking on too much at once.

Misdiagnosis

Not every big vision is bad—after all, some of the greatest companies started with moonshot ideas. The key distinction is whether a startup is tackling its vision in a structured, iterative way or trying to do everything at once.

  • Good ambition: SpaceX had a massive goal—getting humans to Mars. But instead of trying to build everything at once, it started with small, incremental progress: launching satellites, building reusable rockets, and developing a sustainable business model before attempting interplanetary travel.
  • Bad ambition: Juicero raised over $100M to “reinvent” juicing but over-engineered a complex machine for a problem that didn’t exist. Customers quickly realized they could just squeeze the juice packs by hand—rendering the entire product useless.

The key difference? The best startups execute their vision through iteration and prioritization, while companies that fail often try to solve everything at once without proving their core value.

Refactored Solutions

If you feel your company might be boiling the ocean, how do you fix it?

  1. Define and Prioritize the MVP – Identify the smallest, simplest version of your product that can deliver value and prove your concept. If you are already in the market with a product and expanding it keep the MVP mindset in mind with subsequent product launch. Build and launch only what you need to make the measurable impact you need to make and learn the lessons that your company needs before expanding score.
  2. Break Down the Big Vision – Think in phases rather than tackling everything at once. Start small, test, iterate, and expand. Eventually, you will get to it all, but every phase has to independently stand on its merits to help justify investing in the next phase
  3. Limit Scope Creep – Be ruthless about saying no to ideas that don’t directly contribute to your core focus. Moonshots and pies in the sky can be sexy, but at times they can be the entrepreneurs worse enemy
  4. Ship Something Quickly – The sooner you get real user feedback, the sooner you know what actually matters. Quality aside (definitely don’t launch products you are not proud of), the faster you get out there the shorter the path to getting to an A+ product.
  5. Ensure Team Alignment – Everyone should be clear on the company’s immediate priorities and milestones. Focus on the short term and the ultimate vision. Mid-term can be fuzzy.
  6. Use Constraints to Your Advantage – Limited resources force better decisions—use them as a guide to stay focused. Reiterate the same with your team. Startups are here to solve problems big companies can’t solve because they do better under pressure and with limited resources.

If Magic Leap had started by perfecting one core AR feature instead of trying to reinvent reality all at once, it might have succeeded. If Quibi had launched a free version or tested its content model before committing to a $1.75B gamble, it might have pivoted in time. The key is focus, execution, and iteration.

When It Could Help

Are there cases where “boiling the ocean” is actually beneficial? Occasionally.

  • Fundraising & PR: A grand vision can attract investors and media attention, but it must be backed by real, incremental progress. Also, don’t confuse between a grand vision (which ever company needs) and a phased or iterative approach. Your operating plan should be of the latter.
  • Category Creation: If a startup is entering an entirely new industry, some level of ambitious thinking is required. However, even in these cases, breaking the problem down into smaller, achievable steps is crucial.
  • Moonshot Companies: Deep tech, biotech, or AI research startups may need to take bigger bets—but even then, the best companies structure development in phases rather than trying to solve everything at once.

Startups should dream big, but execution is what separates visionaries from failures.

Final Thoughts

The most successful startups don’t boil the ocean—they boil a cup of water first, then scale. Trying to solve everything at once leads to failure, but tackling big visions through small, focused steps leads to world-changing companies.

If you find yourself taking on too much, step back and ask: “What is the smallest thing we can ship that creates value?”

The answer to that question might just save your startup.

Startup Anti-Pattern #9: (Founder) Arrogance

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In the early days of Apple, Steve Jobs was famously arrogant.

He believed he knew what users wanted before they even realized it themselves. His confidence led to groundbreaking products like the Mac and the iPhone, but it also resulted in some significant missteps—like the overly expensive Lisa computer and the rigid, closed system of the early Macintosh that alienated developers.

Steve Jobs was an outlier. Most founders who carry the same level of arrogance find that it backfires – sometimes spectacularly.

Take Quibi, for example. Founded by Hollywood mogul Jeffrey Katzenberg and former HP CEO Meg Whitman, Quibi was launched with nearly $2 billion in funding and the belief that short-form, mobile-first premium content would revolutionize entertainment. Katzenberg dismissed concerns from industry experts who doubted people would pay for Quibi when free platforms like TikTok and YouTube existed. He doubled down on a strategy that ignored user feedback, believing in his vision to the end. The result? A spectacular failure—Quibi shut down in just six months, proving that misplaced confidence can be deadly.

What It Is?

Arrogance in startups is an anti-pattern where founders and leaders believe they are always right, ignoring market signals, data, customer feedback, and team input. It manifests in several ways:

  1. Overconfidence in decision-making – Founders assume they have all the answers without validating their assumptions.
  2. Ignoring customer feedback – Dismissing user complaints or requests because they “don’t get the vision.” or because product or marketing failed to properly portray the solution to them.
  3. Lack of adaptability – Insisting on a strategy or product feature despite overwhelming evidence that it’s not working.
  4. Alienating team members – Dismissing dissenting opinions and creating a toxic work culture where only one voice matters.
  5. Underestimating competition – Believing that competitors are irrelevant or inferior, leading to a failure to adjust to market realities.

While confidence is essential in building a startup, unchecked arrogance leads to blind spots that can cripple a company early on or later in the journey.

Why It Matters?

Arrogance doesn’t just make founders difficult to work with—it can cause real, measurable harm to a startup:

Slower Product Iteration – If a founder refuses to accept feedback, the company will waste valuable time and resources building the wrong product. A startup is a journey in the team’s head from where the product is today and where it needs to be to satisfy most customers requirements. The faster you get there, the better!

Poor Team Retention – Employees who feel their expertise is ignored will leave, taking valuable institutional knowledge with them. The stronger an employee is, the more likely they are to get frustrated by yet another founder decision that is based solely on the founders vision or intuition.

Investor Skepticism – Arrogant founders struggle to raise money once investors realize they are unwilling to listen or adapt. As a VC myself I can attest to this first hand. Investors don’t want to work with founders who don’t listen, learn, and adapt.

Reputation Damage – Customers and partners lose trust in a company that refuses to acknowledge its mistakes. Customers, like everybody else, want to be heard. A arrogant team can make it harders for customers to feel the work they are doing with the startup is cooperative.

Diagnosis

Arrogance is particularly dangerous because, in the short term, it can look like visionary leadership. Many successful founders, from Elon Musk to Travis Kalanick, have displayed strong opinions and confidence. However, the difference between arrogance and conviction is data-driven adaptability—great founders recognize when to adjust their approach.How can you tell if arrogance is creeping into your startup’s culture? Ask yourself these questions:

  • Do we test assumptions with real users, or do we assume we already know what they want?
  • Do we regularly review and act on customer feedback?
  • Are we open to pivoting if the data suggests our strategy isn’t working?
  • Do employees feel comfortable challenging leadership without fear of dismissal?
  • Do we recognize and learn from our competitors rather than dismissing them?

If you’re answering “no” to most of these, arrogance may be a growing problem in your startup.

Misdiagnosis

Not all confidence is arrogance. In fact, some level of conviction is necessary for a founder to push through adversity. It’s important to differentiate between:

Visionary Leadership – Being confident in a bold vision but validating ideas with data and feedback.

Arrogance – Dismissing input from customers, employees, and the market because you “know better.”

Another common misdiagnosis is confused “Founder mode” (a specific kind of leadership in which a founder has a direct, hands-on approach to their company rather than breaking up and delegating responsibility through a top-down structure) with arrogance. There is a subtle line between “Founder Mode” and “Founder Blindness”. The best founders know when to step back, listen, and adapt—before their arrogance turns their biggest strength into their biggest liability.

A founder like Jeff Bezos, who famously insisted on long-term thinking at Amazon, was confident but data-driven. He made big bets but constantly adapted based on customer behavior. In contrast, founders like Elizabeth Holmes (Theranos) or Adam Neumann (WeWork) disregarded all warning signs, leading to catastrophic failures.

Last, another misdiagnosis can happen when founders and leaders keep “blaming” the team for poor delivery, assuming that the end user didn’t “get it”. Ask yourself if your organization keeps thinking that users are “stupid” and just need to be taught better? Is the team continuously talking about the users not “Getting it” because Marketing or Product failed to communicate the merits of the product? You might be misdiagnosing the situation thinking that it’s an execution issue instead of arrogant leadership.

Refactored Solutions

How do you fix an arrogance problem in your company?

  1. Prioritize User Research – Build systems for continuously collecting and analyzing customer feedback. Blend vision with data, research to make decisions (while reducing the risk of analysis paralysis and long decision cycles).
  2. Encourage Internal Debate – Create a culture where employees feel safe questioning leadership decisions. Embrace ambiguity and contradiction without penalizing those in your team who participate. Seek intellectual honesty and data points to back theses.
  3. Seek External Mentorship – Surround yourself with advisors and investors who challenge your assumptions. Ideally, those are folks you strongly respect who can challenge your thinking to avoid “unnecessary” arrogance
  4. Test Before Scaling – Don’t assume something will work—run small experiments before committing major resources. Listen to the market be aware of industry trends and competition, and adjust accordingly.
  5. Own Your Mistakes – Admit when you’re wrong, learn from it, and move forward. Your team will appreciate it.

A good exercise? Have every leadership team member make a list of the last five big decisions they made and the data that supported them. If there’s no real data behind those decisions, arrogance might be clouding judgment.

When it could help and final thoughts

Is there ever a time when arrogance is beneficial? Yes.

  • Breaking new ground – Some innovations require ignoring conventional wisdom. If everyone already agrees with your idea, it might not be innovative enough.
  • Fundraising & sales – Investors and customers need to believe in your vision. Confidence can be a powerful tool in selling your startup’s potential.
  • Navigating uncertainty – Startups often operate in ambiguity, and hesitation can be deadly. Sometimes, a strong conviction is necessary to push through doubt.

But these benefits only apply when paired with adaptability. Founders who balance confidence with learning and iteration have a much higher chance of success than those who stubbornly refuse to adjust.

One of the most interesting debates about investing in early-stage startup teams centers on arrogance in founders. You might also hear it referred to as hubris or cockiness among tech entrepreneurs.

You need a certain level of arrogance to be a successful entrepreneur. After all, starting a company from scratch to transform an industry or create a new one within a decade inherently involves some arrogance.

Arrogance is one of the most insidious startup anti-patterns because, at first, it can feel like an asset. But as history has shown, even the best founders fail when they stop listening, learning, and evolving.

The best startups are customer-obsessed, data-driven, and humble enough to adjust their course when needed. If you find yourself dismissing criticism or resisting change, take a step back and ask:

“Am I being a visionary, or am I just being arrogant?”

The right answer can mean the difference between a billion-dollar company and a cautionary tale.

Startup Anti-Pattern #8: Analysis Paralysis

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As part of the continued series on startup anti-patterns, we look at the crippling effect of overanalyzing decisions: “Analysis Paralysis.”

First, a story. In 2014, a promising IOT startup in my portfolio (which I won’t name) was working on a revolutionary home security product. The product was cutting edge from an AI standpoint. The founders were technical experts in the space, with significant research capabilities – a team of experienced data scientists who were obsessed with making the perfect product. Every decision—feature prioritization, UI design, pricing model—underwent exhaustive deliberation.

At first, this thorough approach seemed like a strength. Investors admired the team’s commitment to quality, and the startup attracted a talented group of engineers. However, as competitors rapidly iterated and released new features and products, the company was stuck in internal debates and small optimizations. Every feature had to be optimized before launch and ready to scale, delaying releases and updates.

By 2018, the company had a state-of-the-art product—but no real traction. Customers were frustrated with delays in updates to the amazing hardware they bought. Meanwhile, Ring, a competitor with a simpler and cheap solution, captured the market. Ultimately, the company failed not because of poor technology but because they could stop analyzing and optimizing solutions, leading to significant delays.

What It Is

“Analysis Paralysis” is the anti-pattern where startups become so obsessed with making the perfect decision that they fail to make any decision or don’t make decision fast enough. Rather than moving forward with a well-informed but imperfect plan, teams get stuck in endless cycles of research, deliberation, and second-guessing.

Startups are full of uncertainty, and founders often believe that with enough data and discussions, they can eliminate all risk before acting. However, in reality, no amount of analysis can completely eliminate uncertainty. Velocity and momentum often win the startup game – the cost of not making a decision often outweighs the risk of making an imperfect one.

Why It Matters

Startups thrive on speed and iteration. When teams fall into Analysis Paralysis, they risk:

  1. Missed Market Opportunities – Startups that hesitate lose out to faster-moving competitors who are willing to launch quickly, learn from customer feedback, and iterate. Very few products are perfect on arrival (read: your startup doesn’t have billions of dollars available to deliver close to perfect product like Apple). Often startups just need to put a stake in the ground, get the product out there, and learn from customers in the real world
  2. Wasted Time and Resources – Overanalyzing decisions drains resources that could be better spent executing and testing in real-world conditions. Every day that goes by inches your company one day closer to extinction.
  3. Low Team Morale – Employees working in an environment of indecision can become frustrated, disengaged, or even leave, further slowing progress. Obviously, employees want to be heard and bottom-up innovation often wins, but eventually the team wants and needs leaders who can make tough calls and chart a course for the company. If folks won’t see progress in a timely manner they will get frustrated.
  4. Investor Skepticism – Venture-backed startups must show progress. If investors see endless discussions without action, they may lose confidence in the leadership.

Diagnosis

To determine if your startup is suffering from Analysis Paralysis, ask yourself the following questions:

Are key decisions frequently delayed due to excessive internal debate? Obviously, you want to leave room for debate, but note everything is and should be debatable.

Does your team frequently revisit past decisions instead of executing? Retros when a major failure happens are okay, but that’s different than consistent and continuous revisiting of decisions made. Sometimes the team just needs to move on.

Are you gathering more and more data but struggling to take action? This could also be an indication that your team is now gather the right data, or maybe the data is “garbage in, garbage out”. this could either be an analytics problem or you might not be asking the right questions. If not, it could be another sign of analysis paralysis.

Do competitors seem to be moving faster while you’re still deciding? This is less about feature parity of chasing the competition, and more about pure execution momentum.

Do team members feel exhausted from constant deliberation with no resolution?
The frustration could be driven by a single, or few, specifical team mates who tend to have hard time moving on, or it could be a cultural indication that your startup is suffering from analysis paralysis.

If the answer to a many of these questions is yes, your team might be suffering from analysis paralysis.

Misdiagnosis

Not all thorough decision-making is bad. The key distinction is whether the analysis is enabling action or preventing it.

For example, in Some industries (such as healthcare – pharma or medical devices) careful regulatory and considerations are required to get a product to market. However, even in these cases, delaying indefinitely is dangerous.

Analysis is key for success. Don’t fall into using the analysis paralysis to “shut down” voices within the company. It’s not just important that employees and stakeholders be heard – it’s critical that they are heard because often they have great insights to bring the table. Even if those insights shouldn’t stop you from moving ahead they might help you refine your strategy and approach.

Refactored Solutions

If your startup is stuck in Analysis Paralysis, consider these steps:

  1. Set Decision Deadlines. Establish clear timeframes for making key decisions. When the deadline arrives, make the best choice with the information available and move forward.
  2. Use the 70% Rule. Jeff Bezos popularized the idea that decisions should be made with 70% of the information you wish you had. Waiting for 90% or 100% is often too slow.
  3. Prioritize Speed Over Perfection. Encourage a bias toward action, and exhibit that yourself as a founder in the company . In most cases, launching a minimally viable version and iterating based on feedback is better than over-engineering upfront.
  4. Empower Decision-Makers. Avoid groupthink and endless consensus-seeking. Clearly Delegate authority and trust individuals or small teams to make decisions within their domain. Back your leaders and make sure that the team knows that are making a decision and once it’s made they shouldn’t question those decision until more data is present.

When It Could Help

In some cases, thoughtful deliberation can be valuable:

  • High-Stakes Decisions. When a decision is truly irreversible or has major legal, financial, or ethical consequences, careful analysis is warranted. A decision still needs to be made so having a reasonable deadline to achieve the decision could make sense.
  • Strategic Inflection Points. If pivoting the company or entering a new market, taking extra time to gather insights can prevent costly mistakes. It’s often okay to “pay” in capital or time to gather data in order to make better decision in the future.

Conclusion

Analysis Paralysis can quietly, over time, kill a startup. Startups win not by avoiding mistakes, but by learning from them quickly and adjusting.

Founders who obsess over perfect decisions often end up making none at all, or at least not as many as they should have. If you find your team stuck in endless deliberation, it’s time to shift gears—because in the startup world, momentum and speed are everything.

Startup Anti-Pattern #7: Chasing “Blue Oceans”

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As part of the continued series on startup anti-patterns, we look at the perilous pursuit of endless new unknown markets: “Chasing Blue Oceans.”

What It Is

“Chasing Blue Oceans” is the anti-pattern where startups repeatedly seek untapped markets that lack competition but also lack the necessary customer demand or market size to sustain a venture-scale business.

Inspired by the popular business strategy book Blue Ocean Strategy, this anti-pattern emerges when founders prioritize chasing open field opportunities ahead of disrupting well understood categories. In the process founders and team members might be biased and over validate whether that new space is actually worth playing in.

The logic behind the strategy is simple: go where there is no competition. It feels seems and right – why compete with incumbents and other folks i you can, instead, chase a less competitive category. Unlike “Red Oceans” filled with rivals fighting over market share, “Blue Oceans” represent fresh opportunities.

But in reality, these oceans are very often “blue” because there’s no real market to compete over in the first place. Startups that fall into this trap are often visionary but miscalculate the balance between innovation and market reality. They are also risking being “too early” to the market, a well know starup fail point.

Why It Matters

The allure of Blue Oceans can lead to costly distractions, wasted capital, and eventual startup failure. Here’s why:

  1. The Market Isn’t Big Enough – The biggest pitfall is misjudging the size of the opportunity. Venture-scale businesses require large and growing markets, but many Blue Oceans are small ponds. If a company can’t find enough paying customers, even the most innovative solution or the best team won’t succeed.
  2. Slow or Non-Existent Adoption – Even if a market has potential, it may not be ready for a new solution. Early-stage industries often lack the infrastructure, customer behavior, or urgency to adopt cutting-edge technology, leading to long sales cycles, slow growth, and an inability to scale. No scale = no funding. Companies die because they run out of many, not because they don’t have great directions and ideas.
  3. Endless Pivoting Without Progress – Startups chasing Blue Oceans could fall into a pattern of constant pivots, each aimed at discovering a more promising market. Instead of iterating toward product-market fit, they end up on a never-ending quest for an opportunity that may not exist.
  4. Lack of Competitive Pressure Can Be a Bad Sign – While competition can be intimidating, it often validates that a real market exists. If no one else is operating in a space, it’s worth asking why. Are you truly ahead of the curve, or is there simply no real business to be built there?

Diagnosis

To determine if your startup is “aimlessly” Chasing Blue Oceans, ask yourself:

  • Are there real, paying customers who urgently need your product? If your sales pipeline is full of “interested” but non-committal prospects, your market may not be viable. Also, are those customers growing themselves and are they willing to pay enough for your product to justify the efforts involves and selling, building the product, and servicing those customers
  • How many companies have successfully built a business in your space? If the answer is close to zero, there’s a good chance the opportunity is too niche. No competition is bad sign.
  • Are you shifting markets every 6–12 months? Frequent pivots in search of a better market are a strong signal that you’re in Blue Ocean territory.
  • Does your market have natural adjacencies? Some markets start small but can expand into larger ones. If your Blue Ocean doesn’t naturally lead into a Red Ocean with more opportunity, you may be stuck and should reconsider.

Misdiagnosis

Not all Blue Ocean strategies are bad. Some companies do create entirely new markets—Airbnb, Uber, and Tesla are examples of companies that initially seemed to be chasing obscure opportunities. However, the difference is that these companies had:

  1. A clear, pent-up demand for their product. They weren’t just innovating for innovation’s sake; they were solving real problems that customers desperately wanted addressed.
    Further, they were presenting entirely new product that disrupted or out-innovated customers. Tesla built and electric car – a car that at the time didn’t have much demand, but cars in general represent a huge opportunity. AirBnB scaled an existing product (home rentals) to compete with hotels.
  2. Mass-market potential. They weren’t building niche solutions; they were creating new behaviors in industries worth billions. Their products seemed niche at first, but took over the red ocean market eventually.
  3. The ability to convert skeptics. A common mistake in Blue Ocean thinking is assuming that people will adopt a new behavior without friction. Market creators must have a strategy to drive mainstream adoption.

Refactored Solutions

If you suspect your startup is Chasing Blue Oceans, consider these adjustments:

  1. Validate Before Building. Before you commit years of effort, validate that real customers will pay for your product. Conduct interviews, run pilot programs, and test demand in measurable ways.
  2. Start in a Red Ocean and Expand. Many successful companies start in competitive markets, prove their value, and then carve out their own niche. Competition often means there’s real demand.
  3. Assess Market Timing. Some ideas are great but too early. If infrastructure, regulation, or customer behavior isn’t ready for your solution, you may need to rethink the business model. A pivot might be necessary. Time is money and your company burns cash everyday. You can’t wait forever for the market to mature.
  4. Focus on Real Urgencies, Not Hypothetical Ones. Entrepreneurs often get excited about theoretical problems that “should” exist but don’t actually cause enough pain for customers to pay to solve them.
  5. Look for Big Adjacencies. If your initial market is small, make sure it has clear expansion opportunities into larger, profitable segments. If that is the case it might make sense to invest in a small blue ocean market, with the clear understanding that this is a temporary workaround that will lead to a big opportunity.

When It Could Help

Despite the risks, Blue Ocean thinking can work when applied strategically. It may be worthwhile when:

  • Your solution is truly revolutionary. If you have a breakthrough technology or business model, like OpenAI with generative AI, a Blue Ocean may be justifiable. This is especially true if you have new, foundational technology, that changes the target market.
  • You can create network effects. Some markets start small but explode once they reach a critical mass (e.g., social networks, marketplaces, and platforms). If you have clear conviction and data that the Blue Ocean opportunity will become massive (not based on a wild bet) than being the first to open up this opportunity might be a smart thing to do.
  • You have the capital to educate the market. If you have the runway to build awareness and shape customer behavior, you might be able to create demand over time. Unfortunately, most startups don’t.

Conclusion

The dream of an uncontested market is enticing, but the reality is often less glamorous. Chasing Blue Oceans without validating market size, demand, and urgency can lead startups down a costly and frustrating path.

Instead of prioritizing open space, founders should prioritize solving big, well-defined problems for real customers. Remember, most successful startups don’t win by avoiding competition—they win by executing better, moving faster, and solving problems that matter at scale.

The unfortunate reality and VC pattern recognition is that disrupting and innovating in a well understood, at scale, market. A red ocean. If often and easier path to building successful scalable startups. Those markets have clear demand and a new technology addressing the existing large market in a completely new way can hit the ground running right away, instead of waiting for the market to show developer, if ever.

Startup Anti-Pattern #6: Chasing the Competition

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First, a story.

In 2012, Twitter launched Vine, a platform that allowed users to create and share six-second looping videos.

Vine quickly gained popularity, becoming a cultural phenomenon and amassing a substantial user base. At its peak in December 2015, Vine had over 200 million active users(!).

However, as competitors like Instagram and Snapchat introduced their own short-form video features, Vine struggled to keep up. Instead of innovating and focusing on its unique strengths, Vine attempted to mimic its competitors’ features. This reactive approach diluted its brand identity and the company lost traction with it’s users. By 2017, Vine was discontinued, serving as a cautionary tale of the perils of chasing the competition.

What It Is

“Chasing the competition” is the anti-pattern where startups reactively adjust their strategy, product, or business model based on competitors’ moves rather than their own well-defined vision, Roadmap, and understanding of customer needs.

Instead of focusing on their unique strengths and market positioning, these companies play an endless game of catch-up, constantly shifting their approach in response to external moves.

Startups fall into this trap for several reasons:

  • Fear of Missing Out (FOMO): Seeing competitors gain traction with a feature or model creates panic that they’ll be left behind.
  • Investor Pressure: Stakeholders often push startups to replicate competitors’ successes, assuming they must be doing something right.
  • Lack of Confidence in Original Strategy: When a startup is unsure of its own value proposition, it looks outward for validation rather than inward or into it’s customer base for conviction.
  • Media and Market Hype: Tech media amplifies competitor successes, making founders feel like they must follow suit.

The problem? By the time a startup reacts, the competition has often moved on. Worse, the startup risks alienating its existing customers by failing to deliver what originally made it valuable.

Why It Matters

Chasing the competition can be disastrous for startups. Here’s why:

  • Loss of Identity – Startups that constantly shift their strategy can lose their unique value proposition. Customers don’t know what they stand for, leading to weak brand positioning.
  • Strategic Drift – The company’s roadmap becomes dictated by external forces rather than internal conviction. This can lead to wasted development cycles and diluted focus.
  • Poor Product-Market Fit – Features copied from competitors may not align with the startup’s core user base. This can result in low adoption rates, increased churn, and a confused user experience.
  • Inefficient Resource Allocation – Constantly reacting to the market means frequent shifts in development priorities, marketing strategies, and sales approaches. This unpredictability increases operational inefficiencies and burn rate.
  • Employee Disillusionment – A startup that continuously pivots in response to competitors can create internal instability. Teams lose confidence in leadership’s vision, and morale declines.

Diagnosis

To determine if your startup is falling into the “chasing the competition” trap, ask yourself:

  • Are our product decisions driven by competitor announcements rather than customer feedback?
  • Have we pivoted our strategy multiple times and often in response to external moves rather than internal validation?
  • Are we sacrificing long-term vision for short-term trends? If so, to what degree?
  • Are our employees confused about what our core mission and differentiators are?
  • Do we spend more time analyzing competitors than engaging with our own customers?

If the answer is “yes” to multiple questions, your company might be suffering from this anti-pattern.

Misdiagnosis

Not every competitor-inspired move is a mistake. There are valid reasons to take cues from the market, such as:

  • Customer-Driven Demand: If your users are clamoring for a feature competitors offer, it probably worth considering.
  • Evolving Industry Standards: Some market shifts, such as mobile-first interfaces or AI-driven personalization, become table stakes over time.
  • Competitive Benchmarking: Keeping an eye on the competition is healthy, as long as it informs rather than dictates strategy.

The key difference is proactive innovation versus reactive imitation. Are you making changes because they align with your vision, or because you’re afraid of being left behind?

Refactored Solutions

Once diagnosed, here’s how to break free from the competition-chasing cycle:

  1. Double Down on Customer Insights – Focus on your users’ needs rather than your competitors’ moves. Conduct regular user research, feedback sessions, and data analysis to validate your direction.
  2. Define and Stick to Your North Star – Have a clear long-term vision and resist knee-jerk reactions to market shifts. Your strategy should be based on core principles, not fleeting trends.
  3. Develop a Clear Product Roadmap – Plan features and business moves based on long-term value rather than short-term competitive reactions. Make sure each update serves a clear strategic purpose.
  4. Limit Competitive Benchmarking – Monitor competitors, but don’t obsess over them. Use them as data points, not roadmaps. Ignore PR and the Media. They often don’t focus on what matters (and sometimes avoid fact checking) and are also naturally gravitate to Selection Bias
  5. Empower Your Team to Innovate – Encourage internal ideation rather than external imitation. The best startups continuously create market demand and innovate, rather than just react to market forces.
  6. Educate Investors and Stakeholders – If investors push for feature parity with competitors, educate them on why differentiation could be more valuable. Feature parity can eventually lead to a race to the bottom. In started you are actually looking for the opposite, clear differentiation and higher value leading to higher prices and hopefully better margins.
  7. Be OK with Not Competing on Every Front – Not every battle is worth fighting. Choose the ones that align with your strengths and ignore the rest. Focus is key.

    Here’s one point many early stage founders miss – Startups rarely die because competitors are better or because they run out of money. They die because they lose focus trying to be better at everything instead of being the best at something.

Your biggest threat isn’t your competitor’s bank account. It’s losing sight of who you really serve.

When It Could Help

Are there cases where “chasing the competition” is beneficial? Occasionally, yes.

  • When a competitor’s move validates an idea you were already considering. If their success provides proof of concept, it may accelerate your own plans.
  • When industry shifts make competitive parity a necessity. If a fundamental technology (e.g., cloud computing, AI, mobile-first) becomes standard, failing to adapt can be risky.
  • When entering a mature market where feature expectations are well-defined. In highly competitive industries, some level of parity is expected, though differentiation remains key.

Final Thoughts

Startups succeed by playing their own game, not by reacting to someone else’s. While it’s important to stay informed about the competitive landscape, true success comes from focusing on your vision, your customers, and your strengths—not just keeping up with the latest feature war.

If you’re chasing the competition, stop and ask: What do we stand for? What makes us different? If you can answer those questions with clarity and confidence, you’re on the right path.

Startup anti-pattern #5: Bad Revenue

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First, a blast from the past which some of us might remember.

As a kid growing up in the 80s, I was a movie junkie and rented movies from Blockbuster several times a week. I’d binge-watch the movies I loved (I’ve probably seen the entire James Bond series 20 times). Back in the 80s, Blockbuster was somewhat okay—they had policies around late returns, but those weren’t draconian, and the late fees were manageable. I could pay a reasonable amount of money if I wanted to keep a VHS tape and watch it on repeat.

At some point around the turn of the decade, Blockbuster decided to change its policies. I’m wildly speculating that someone at Blockbuster’s Corporate recognized an opportunity to make a few more bucks from a (probably significant) subset of customers who were late in returning video tapes. Blockbuster decided to charge exorbitant late fees for no real reason.

It was frustrating and kept customers on their toes. The brand image deteriorated. Employee discretion policies regarding charging late fees became increasingly aggressive (the “my dog ate my VHS tape” excuse quickly became frowned upon), not surprisingly, given the incremental revenue lift late returns delivered to the chain. Blockbuster became the one shop on my street I was always trying to avoid.

Unfortunately for Blockbuster, they made the wrong move. Their choice to create significant friction with customers left them completely open to alternatives. We all know how the story ended. Netflix came about and was happy to lend customers DVDs for as long as they wanted to binge. That simple tweak, putting customers first, made a big difference and eventually (among other things, such as streaming and online content piracy) led to Blockbuster’s ultimate demise.

What is it?

It might seem odd, particularly in what feels like a Tech recession (or potentially a broader recession), to talk about the concept of “good vs. bad revenue”. Some of you may be thinking that “Revenue is revenue; all revenue is good!” Unfortunately, that is not always the case.

“Bad revenue” is revenue that comes at the expense of building the long term viability of the business. Below are a few examples of “Bad Revenue”:

  • Comes at the expense of the relationship with customers – For instance, a company that hinders their clients’ ability to cancel when they want to or sells something overpriced, taking advantage of the customer’s needs. This could lead to Churn, low NPS scores, and angry clients which can hurt your organization’s reputation.
  • Negative or very low contribution margin – The deal with the specific customer is either unprofitable or the margins are significantly lower than those with “good revenue.” The opportunity ends up negatively impacting the company’s financial resources.
  • Outside of target audience – Revenue opportunities outside the company’s customer target are more difficult to acquire, and win rates are significantly lower. Even if we win the customers, the ability to create a delighted customer is at risk because the company and product are not set up to win the relationship. Further, those opportunities don’t help your organization learn and evolve.
  • “Customer from hell” – Customers who are over-demanding or just plain rude can lead to frustration and churn among the company’s employee base. These customers are unlikely to become an advocate for the business and won’t be good references either.

In contrast, “good revenue” typically generates strong profitability because it is from deals that we know we can support well. The customer is delighted and the product helps them achieve their goals. The customer scores high on NPS scores and is proud to share their delight, serving as an advocate and reference. Engaging with the customer strengthens our collective understanding of our customer’s needs and helps cement our position in the market. It enables us to more easily find and pursue additional deals and revenue.

Why it matters?

Bad revenue can drain the company’s resources and divert management and employee focus toward less productive venues. Good Revenue informs your next decision. Bad Revenue distracts you from it.

Focusing on good revenue could mean that the company probably won’t grow as fast as some of our competitors. However, it does mean that customers will genuinely love the company and want to work with us over a longer period. In the short term, you might miss your goals, but it will be a win in the long term.

Furthermore, bad revenue often comes at the expense of good revenue creating a high opportunity cost. Instead of focusing on the right set of customers, the company’s (often scarce) resources are continuously diverted to energy-draining customer relationships.

Almost every professional has had the unfortunate experience of being stuck in a relationship with a customer who they can’t possibly satisfy. The ongoing frustration could eventually lead to employee dissatisfaction and churn.

Diagnosis

Sadly, too many salespeople are directed by their management to grow revenue at all costs and don’t pay attention to finding opportunities that create “good revenue”. They end up casting a wide net in their prospecting to hit short-term revenue goals, at the expense of the long-term health of the business.

There are several ways to diagnose “Bad revenue” in your organization:

  • Profitability is declining, while pricing remains the same – This can be a signal that 1) the company is becoming less efficient, or 2) The company is taking on bad revenue.
  • Employ customer profitability analysis – Knowing your average customer profitability and conducting a customer level profitability analysis can help detect relationships which are not making a positive impact on the business. In more mature organizations, this type of analysis should be made on an ongoing basis, or at the minimum at renewals. There are better tools these days to assist with this analysis, such as Lumopath AI (apologizes for the shameless plug to a Recursive Ventures portfolio company).
  • NPS scores dropping – Aggregate NPS scores are a great high-level diagnostic tool. Once you spot a negative trend, the trick is to go beyond the aggregate, analyze deviations, and stack-rank customer satisfaction on a per-customer basis. It can be helpful to continuously stack-ranking individual customers and analyzing the bottom quartile of responses. Beyond just better understanding the challenges involving your target market, the exercise can also help understand which types of customers the organization should avoid moving forward.

Misdiagnoses

One common misdiagnosis occurs when companies don’t properly consider “land and expand” opportunities, thus undervaluing the customer relationship. Another form of this is short-term or miscalculations of lifetime value (LTV) of a customer. Instead of considering the long-term potential of the relationship, the company doesn’t properly assess the opportunity and doesn’t end up making the right long-term choice for the business because they think it’s “bad revenue”, but in practice the customer has much more to offer longer term.


Refactored solutions

Once diagnosed, the refactoring of this anti-pattern requires changing the organization’s mindset and approach to sales and customer success. A few ideas on how to refactor best:

  • “Firing” customer(s) – If your organization is indeed facing a ‘customer from hell’ or bleeding cash servicing a customer, the easiest solution is to part ways.
  • “Improving” Prices – Improving pricing can go both ways. If the customer engagement is not profitable, you can raise prices to achieve the organization’s target profitability goal. Alternatively, if the pricing isn’t sufficiently correlated to the value delivered to the customer, and the customer feels that they are significantly over-paying for the solutions, you could lower the price to help retain the customer for the long term.
  • Institute performance-based compensation plans that reflect the overall “health” of a deal – salespeople often get most of their compensation with commissions. Building a scheme that accounts for profitability, Lifetime value, and customer satisfaction goals pushes sales and customer success to prioritize good revenue over “bad” revenue
  • Build organizational values seeking “win-win” situations with customers – Win-win situations help build a healthy customer relationship. Obviously, the company needs to strike the right balance and capture value, but avoiding “harvesting” or short-term thinking tactics lead to happy customers, retained over longer periods, and producing higher NPS scores and LTV.
  • Embrace Market Research and go-to-market best practices – Start by conducting comprehensive market research to identify your target audience. If possible, avoid diverging from your target audience to focus on the customer you can deliver the most value to first. The company will eventually have to go outside of initial target customer “comfort zone,” but it’s best to avoid doing that early if you have sufficient “runway” with your core target audience.


When it could help?

Sometimes, organizations need revenue badly, and have to accept bad revenue. However, companies should do it with eyes wide open, understanding what they are doing, why they are doing it, and what risks they are taking on.

A few examples in which bad revenue could make a positive, albeit short term, impact:

  • Bad revenue could be a necessary evil when the organization needs to fundraise soon (and need to show revenue momentum to investors).
  • Related to the previous point, Bad revenue could help contribute to M&A outcomes, with buyer paying a premium for growth or a wider set of customers.
  • New market penetration – A company endeavoring into new customer segments and markets might experience a stretch of bad revenue as part of their learning process. This bad revenue could be a necessary evil required to learn the new target market, fine-tune the product offering, or discover the right pricing scheme. If the bad revenue is a means to an end – better understand the market and eventually reaching a good revenue state – it could be an justifiable investment.

Startup anti-pattern #4: if you build it, they will come

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As part of the continued series on startup anti-patterns, we look at the battle between conviction and validation.

First, a story. In 2000, Intech technology, a fledgling startup out of Israel, was building a new type of billing software for property managers. Intech had one potential customer- the Israeli government – that shared the founders’ vision of software which could split bills across multiple tenants in a customizable fashion. For example, using this “killer” feature, the property manager could decide that one tenant pays 70% of the gardening bill while another pays the rest.

The excitement at Intech technologies was at its peak. The founders automatically assumed that if they had the vision and one customer wanted it, many others would. Eighteen months and several layoffs later, the truth was unveiled: end-users didn’t really care about the “killer” feature. Other prospective customers showed no interest in the product’s advanced bill-splitting capabilities. They opted for simpler and cheaper systems that generated invoices and connected to building meters.

After building a product that ended up being an overkill, the company shut down. The founders (Itamar was one of them) learned a hard lesson.

What it is

“If you build it, they will come” is the anti-pattern where startups make decisions based on their vision of how a solution should look, ignoring or underemphasizing customer needs and neglecting to collect sufficient product validation from prospective customers.

The origin of this anti-pattern is the allure of “a great idea”. Entrepreneurs, driven by their passion and conviction, tend to assume that their product’s brilliance alone will captivate customers and guarantee success.

Unfortunately, the mere existence of a product doesn’t automatically translate into customers flocking to buy it. The “if you build it, they will come” mentality often leads to a lack of product-market fit, a leading cause of early stage startup failure.

When combined with confirmation bias, another anti-pattern, this problem becomes even more acute. As with ignorance, it’s usually deadly when combined with a big dose of arrogance.

Why it matters

“If you build it, they will come” mentality can kill your company. It results in redundant product development and misalignment, a significant waste of resources, increased technical debt, and challenges in go-to-market. Hoping that a product will resonate with customers is often a recipe for disaster.

Building a product based on conviction as opposed to market validation can harm your startup in multiple ways:

  • Increased adoption friction. Instead of iterating and improving the product based on customer feedback, startups who fall into this anti-pattern often lack the features customers want. They find themselves trapped in a vicious cycle of slow growth, small capital raises, financial strain and, ultimately, the demise of the startup.
  • Slower product development. Development teams should aim to build what’s most valuable for the business as quickly as possible. Building on conviction without validation is risky because unnecessary features slow down development without creating sufficient business value. Solution complexity and the likelihood of incurring more technical debt, slowing down future development, and shortening a startup’s runway.
  • Low morale. Discovering post-launch that a product isn’t well-received can demoralize a team that worked hard on its development. Before then, team members who know that development is happening with insufficient validation may be demoralized by the company’s approach.

Building “in a vacuum” increases the risk of achieving product-market fit. This misalignment can manifest in various ways. The product might solve a problem that customers don’t care enough about or may fail to meet customers’ expectations or needs. Without product-market fit, it’s harder for a startup to build the right brand, launch effective marketing campaigns, and build the right sales playbook.

Diagnosis

Diagnosis requires honest self-reflection. Look at how the company makes product decisions that commit it to significant expenditures of time and money:

  • Are you aware of all important decision points? Lack of awareness leads to implicit decision making. System 1 thinking, skewed by cognitive biases, dominates implicit decisions. Make decisions that commit the company to significant resource use explicitly.
  • When making important decisions, how much weight do you give to conviction (vision, gut feeling) vs. anecdotal evidence (hearsay, one or few data points collected by an ad hoc process) vs. sufficient evidence collected by a thoughtfully designed validation process? Making big decisions without a responsible amount of evidence is risky.
  • Does the evidence supporting decisions come from a sufficiently diverse range of stakeholders, both internal and external ones? Making decisions based on limited/skewed information is risky, especially when decision-makers aren’t aware of the bias and/or variability of the data.

When attempting to diagnose this anti-pattern, make an honest assessment of the extent to which conviction stems from fear. Sim knew a brilliant technical founder who’d rather spend 100 hours writing code than have a validation conversation with a stranger. He thought his product was going to be awesome. It was the only rational way to avoid talking to people who may give him negative feedback.

Fear often deters teams from engaging in validation processes due to a variety of psychological, organizational, and market factors:

  • Fear of being wrong. People often intertwine their ideas with their personal identity. They may perceive being wrong as a personal failure. Cognitive dissonance pushes individuals to avoid situations that might challenge their pre-existing beliefs. Confirmation bias pushes them to unconsciously ignore unfavorable feedback.
  • Fear of the unknown. If validation feedback suggests that significant changes are necessary, this can lead to an overwhelming feeling of uncertainty. The path forward might not be clear, which can be daunting. Even founders, who typically are comfortable with massive amounts of uncertainty, can fall prey to this.
  • Fear of authority. In some hierarchical organizations, when a person of authority has conviction, people lower down in the organization may avoid validation. They fear repercussions if it contradicts the authority figure’s conviction.
  • Fear of disclosure. Some entrepreneurs feel their intellectual property (IP) is so valuable that they fear validation processes might leak some of that IP. In his VC days, Sim met with several founders unwilling to talk about the details of their technology before a term sheet. You can imagine how these pitches went.
  • Fear of being late. Some teams may skip validation to hasten delivery. They may fear that competitors may beat them to market or feel pressure from stakeholders to deliver by a specific deadline. Discussing time pressure trade-offs honestly and explicitly is good. Replacing validation with conviction implicitly, for fear of being late, is a problem.
  • Fear of wasting an investment. Once a team has invested time and money in a particular direction, they might feel that continuing forward is the only option. This is known as the sunk cost fallacy. For fear of creating waste, they will ignore negative evidence. Humans often exhibit loss aversion, where the pain of losing is psychologically about twice as powerful as the pleasure of gaining.

Arrogance and confirmation bias are the most common anti-patterns that make the diagnosis of “if you build it, they will come” difficult.

Misdiagnosis

Misdiagnosis occurs when companies set an unreasonably high bar for the validation required to make product decisions. It may lead to analysis paralysis in organizations, another anti-pattern, reducing the company’s competitiveness in the market and its ability to launch new products in a timely manner.

What is a reasonable, let alone optimal, split between conviction and validation when making decisions? There is no right answer. Context matters. Marissa Mayer famously asked a team at Google to test 41 different shades of blue for the toolbar on Google pages. Was that too much? It’s hardly excessive when considering Google’s scale and Google’s resources. A 41-way test may not have been that much more difficult to execute than a 2-way test. However, the request to test 41 options applied to a product with limited usage would be ridiculous. It’d take too long for the test to produce a valid result.

Refactored solutions

Once diagnosed, the refactoring of this anti-pattern requires changing the organization’s mindset and approach to product development:

  • Empower people to make data-driven decisions. Instrument products for data collection with good security and privacy controls. It should be easy to implement A/B and multivariate tests. Clean, machine-readable metadata should be available for data enhancement. Manage data consistently in a unified platform. Give key stakeholders self-service access to the analytics that matter. Operational dashboards that answer known questions aren’t enough: optimize for ad hoc analytics aimed at answering new questions quickly and precisely. Distribute organizational authority, responsibility, and accountability for making decisions based on data.
  • Embrace market research and customer feedback. Starting at the top, foster a culture of listening to markets by implementing methodologies such as customer development. Engage with potential customers through surveys, interviews, or beta testing to gather valuable feedback that shapes the product roadmap and the entire company. Pay special attention to statistical validity.
  • Share the voice of customers. Broadly distribute customer and market feedback within the organization. Spend time in all-hands and other company-wide communication channels to highlight customers. Empower your customer support/success team to work more closely with product teams and rotating engineers and product managers to support duty.

Your ability to make well-validated product decisions is like a muscle: the more you exercise it, the stronger it gets. Getting good at validation isn’t easy. It requires significant investments in culture, systems, and processes. It also requires overcoming fears.

To overcome fear and foster an environment that encourages validation, organizations and teams can foster a culture of learning and experimentation; encourage collaboration and open communication; and incorporate iterative processes with smart feedback cycles. By addressing fear, organizations can improve the likelihood of developing products that meet market and customer needs, ultimately enhancing their chances of success.

When it could help

This anti-pattern can help in two cases: when an excess of conviction can be useful and when the expected value of validation is low.

As with ignorance, an excess of conviction can be useful in very special circumstances:

  • Entrepreneurs vary wildly in their ability to predict the future. On average, they’re very wrong, but there are outliers. If you have solid evidence, without ego-boosting revisionist history, that you are such an outlier, it may be smart to put relatively more weight on your convictions.
  • If resources and timeframes are very tight, there truly may be no room for doubt or validation, and it may be worth taking on significant validation risk. It’s time for a Hail Mary pass. Startups often live or die by these decisions.
  • There’s a saying in venture capital that a little bit of data is a dangerous thing. Sometimes the presence of data that isn’t great is worse than having no data at all. This is especially true in tough fundraising climates, when investors who are slowing down their investment pace are looking for even more reasons to reject deals. Since hiding bad data is unethical, entrepreneurs sometimes make the decision to avoid or reduce validation instead of risking having to disclose unfavorable data. However, the absence of validation data may lead to fundraising failure.

All these strategies follow the strategy paradox: while they can be extremely successful, they can also lead to extreme failure. Even Steve Jobs, the quintessential product visionary, came up with Macintosh Portable and the Newton.

There are some cases where market validation has lower expected value because it produces fuzzy and/or biased results:

  • Highly disruptive products. One example is Uber/Lyft in the early days. When surveyed, early prospective customers were concerned about getting a ride with an unknown, unlicensed driver. However, after consumers got used to the convenience and cost efficiencies with ride-hailing, they became comfortable with it. Strong network effects compound this early on and it is difficult to imagine the value at scale.
  • Groundbreaking technology. It’s sometimes hard to articulate technology that works like magic to customers. When Steve Jobs introduced the iPhone, many didn’t understand why touch screens would matter so much. Previous smartphones had keyboards and regular touchscreens, and it wasn’t immediately apparent that capacitive touchscreens would change the world.
  • Category creation. In blue ocean scenarios, there are no (or almost no) prospective customers to talk with. The market or category doesn’t exist yet and will only unfold in the (hopefully near-term) future. For example, when Life360 first launched, investors, advisors, and even parents consistently said they don’t believe kids will have smartphones. Smartphones back then were business tools, not replacements for cell phones, and the general audience didn’t think kids would need them. They were clearly wrong (easily said in hindsight).

Some ideas are much harder to validate than others. Smart startups focus a lot of effort on validation to reduce the risk of achieving product-market fit.

Co-authored with Simeon Simeonov. More startup anti-patterns here.

The Power of Proprietary Data and creating an “AI Moat”

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In the fast-evolving landscape of AI Data has emerged as the new currency (alongside access to Nvidia H100 GPU). Data serves as the fuel that drives AI.

AI systems solving complex problems require an immense amount of data to deliver high quality services. This is especially true in a use cases that don’t have a human-in-the-loop (e.g. Level 5 autonomous driving), use cases delivering partial pr full automation with a high degree of trust and accuracy in a consumer facing scenario (e.g. tier 1 customer support chatbots), or systems automatically executing transactional API calls to other services.

Proprietary data is not a technical topic but a business one. Proprietary data serves as a moat that helps companies differentiate and justify the (often significant) investments associated with building product based on AI models. By training AI models on proprietary data, companies can develop unique capabilities which others can’t develop (simply because others don’t have the data), deliver high quality predictions (typically measured in performance metrics like recall – the percentage of data samples correctly identified as belonging to a class of interest out of the total samples for that class), or leverage a foundation AI model doing a better job fine-tune these model for a given set of use-cases and verticals.

Most people think about proprietary data simply as a unique, exclusive information, collected or generated. Often that is indeed the case, but there are other types of “proprietary” advantages and data strategies that can deliver a significant moat. Here are a few more examples to consider:

  • Leveraging customers’ data sources – Some companies excel at accessing their customers proprietary datasets and obtain rights from their customers to leverage data derivatives for machine learning purposes. This helps both the vendor and the customer by delivering higher quality services. One example is Cherre, which helps customers connect all your real estate data (1st party and 3rd party) and better understand data quality.
  • Partnerships and data consortiums – Business Development partnerships can aid with obtaining and scaling proprietary data sources. This is a method that has been used extensively in online advertising, transactional data, and Location datasets. Other companies deploy data consortiums in which every additional partner benefits from a network effect. Deduce is one example of a data consortium that helps derive more signals from a network of participants, benefitting of all participants. Another great example is Placer, which has an exclusive data acquisition agreement with Life360, locking out significant part of the market
  • Customer led labeling – Many AI solutions sit at the intersection of Human-Machine interface. Collecting customer feedback through the actual use of the system in continuous and smart ways can help can generate data to “debug” models and better understand underdamping, data distribution issues, and mislabeling. Designing the right user experience can lead to customers (including experts in those companies) doing quite a bit of labeling heavy lifting, in turn resulting in higher quality labeled data.
  • Intelligent expert labeling – Having raw data is the first step, but labeling data for training purposes could range from a simple repetitive task to an herculean one requiring specialists and experts. Some companies build tools to leverage experts very efficiently or have tools that leverage limited expert labeled data with various deep learning and transfer learning methods to build models. Watcful.io is an example of a company that helps other companies with expert labeling techniques
  • Unique data mapping – Products built to serve specific verticals (e.g. Law, CyberSecurity) can benefit from mapping data inputs and model outputs to specialty built Data Models (typically built and maintained by humans)or leveraging Knowledge graphs as a way to transform and include relevant tokens into a prompt into an LLM. In specific verticals, this can help minimize model hallucination by adding context and producing model outputs that are more inline with customer expectations
  • Data collection through devices and Hardware – Some companies deploy hardware devices to collect real world data, or are given access to such datasets derived from devices others deploy. Any connected device can help facilitate “real world” data that would be proprietary, including IoT devices, Sensors, Smartphones, etc,

To summarize, possessing proprietary data serves as a business moat, offering protection against rivals and fostering long-term sustainability. Proprietary data and proprietary labeled data sets can comes in various shapes and forms.

A key question to consider is whether a company has a hard to replicate approach to obtaining data, at scale, or labeling it in a way that would make it harder for a new entrant (or even a incumbent that has existing data) to enter the market and deliver AI systems that perform as well. At Recursive Ventures we call this “AI Moat” and it’s inherent to how think about long term value creation in the budding AI eco-system.

AI winners and the race for the ultimate prompt UI/UX

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In the rapidly evolving world of AI, prompt engineering has become a critical discipline. Learning and adopting prompt engineering has already been recognized as the future of jobs in the age of ChatGPT.

But first, what is prompt engineering? Prompt engineering, a concept in natural language processing, involves embedding the task description in the input itself. Prompt engineering enables precise instructions or queries to guide AI models towards desired outputs. It allows humans to effectively interact with AI systems, leveraging their capabilities to accomplish complex tasks with accuracy.

Learning prompt engineer might help you unlock future job opportunities, but helping users succeed with prompt engineering is a key differentiator for the success of a AI-based products.

The success of prompt engineering relies not only on algorithms and models but also on the user interface (UI) and user experience (UX) that enable seamless interaction with AI systems. At Recursive Ventures, we believe that prompt UI/UX excellence is a key pillar for AI startup success.

Similar to the Web and Mobile eras. In the AI era, companies that develop the right set of UI/UX paradigms to help their end-users leverage AI systems will emerge as winners. Creating a product with accessible and usable UI/UX enhances its value to customers, facilitates word-of-mouth, increases willingness to pay, and fosters user stickiness.

How can AI products help customer with a better UI/UX? Here are a few ideas:

Streamlined and contextual guidance

Next-generation UI/UX for prompt engineering should provide a clear and concise interface for formulating prompts by offering smart suggestions, and providing real-time feedback on the expected outputs. Instead of having the user put in a prompt, wait a few seconds (or frustratingly, minutes) to get a response, and then get to the next prompt, streamlining the prompt design in real time can save the user time and overhead.

Effective UI/UX should assist users in composing prompts by offering contextual guidance. This can include features such as auto-completion, natural language suggestions, or interactive tooltips that provide insights into the capabilities and limitations of the AI model. It can help users get to their desired output faster and deliver a higher quality (more accurate, on point) response.

One pretty impressive examples is the work that Adobe has done with various tools and toggles in the Adobe FireFly product, seamlessly integrating text and tool-tips to help users accomplish the designs they envision.

Iterative Refinement

UI/UX tools for prompt engineering should enable iterative refinement of prompts and facilitate experimentation with different inputs. This allows users to fine-tune queries, evaluate generated outputs, and iteratively improve the performance of AI systems. A well-designed UI/UX supports this iterative process, making it easier for users to iterate, learn, and adapt their prompt engineering strategies.

Naturally, having a prompt that enables iterative motions and builds up on the context from previous prompts (similar to ChatGPT) is prerequisite for iterative refinement. Having the ability to also walk back to better understand the iteration path that led to a certain output can also be valuable. One rough analogue would a bread-crumb trail in web browsing. It helps users understand how the model got to a certain result and would be valuable as users increasingly demand model explainability.

Collaboration and Community

UI/UX platforms can foster collaboration among prompt engineers by providing features for sharing, discussing, and co-creating prompts. Creating a vibrant community of prompt engineers encourages knowledge exchange and collective improvement. This collaborative aspect of UI/UX enhances the effectiveness and efficiency of prompt engineering efforts.

One of Recursive’s portfolio companies, Storytell.ai, has done essentially that with their prompt marketplace. It’s a great way to help users get up and running with powerful prompt templates and accelerate their path to getting effective responses out of AI system.

To summarize, the next set of winners in AI will likely master prompt UI/UX. By offering streamlined interaction, contextual guidance, iterative refinement, and collaboration features, AI first companies can help customers adopt prompt engineers to effectively utilize AI models. Prioritizing innovative UI/UX solutions gives startups a competitive edge, enabling them to stand out in the rapidly evolving AI landscape, and fend off competitors.

Investing in AI companies? Think Data first, AI second

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By now, with ChatGPT and the doomsday media hype around it, almost everybody got the memo that AI has the potential to revolutionize industries, reshape business models, and potentially destroy humankind in the process (e.g. Choas-GPT).

As an investor in AI (seems like these days everybody is), it’s crucial to understand the key factors that contribute to the success of AI companies. In this blog post, we will delve into Recursive Venture’s underlying investment thesis in the future of AI – the importance of having proprietary data that sets a business apart and creates a robust moat around it. We call this the “AI Moat”.

Without deviating too much from the main topic (data!), having a moat is crucial for generating significant startup returns for investors. A moat establishes a sustainable competitive advantage and protects against competition. Data from a study conducted by CB Insights revealed that startups with a moat in place, such as proprietary data, were 2.2 times more likely to achieve successful exits.

Back to AI. In AI, data is the fuel that powers the various models. In a crowded AI landscape, where algorithms can be replicated and foundation models are becoming a commodity, having proprietary data becomes a game-changer (Google says that both Google and OpenAI have no moat).

The availability of quality and relevant data is crucial for training AI models, but access to vast amounts of data alone is not enough to gain a competitive edge in the AI market. The real differentiator lies in possessing proprietary data, which is either unique, exclusive, or not easily replicable by competitors (naturally, having all of the above is ideal). Proprietary data can come from various sources, such as customers, partnerships, user-generated data, or specialized data collection processes.

Exclusive data creates a long-term moat by enabling:

  1. Enhanced Accuracy and Performance
    One of the biggest issues today with AI (and even more so with Generative AI) is accuracy and reliability.

    Having access to proprietary data enables AI models to be more accurate and perform better than those relying solely on public or generic data sources. By training algorithms on unique datasets, companies can fine-tune their models to specific use cases and improve predictive capabilities. This heightened accuracy translates into better outcomes, increased customer satisfaction, and deliver stronger model performance.

  2. Deliver custom solutions to customers at scale
    In today’s era of hyper-personalization (for consumer solutions) and customization (for B2B solutions), startups can tailor their AI solutions to individual customer needs.

    Proprietary customer data allows AI companies to create customized experiences, recommendations, and solutions that resonate with the needs of the business or with individuals. This personalized approach enhances customer loyalty, drives adoption, and fortifies the company’s market position.

  3. Barrier to Entry
    Proprietary data acts as a formidable barrier to entry for potential competitors. Building a comprehensive and unique dataset takes time, resources, and domain expertise.

    As AI companies amass and refine their proprietary data, it becomes increasingly challenging for new entrants to replicate their success. Since obtaining similar datasets is challenging or even impossible, it becomes difficult for rivals to replicate the offering. This helps companies establish market dominance and defend against new entrants.

Back to investing in AI. Our thesis is that to identify promising AI investments, investors should evaluate the depth, uniqueness, and relevance of a company’s proprietary data – Assess the company’s “AI Moat”. Multiple companies in the Recursive portfolio, such as Placer.ai, Cherre.ai, Tomato.ai, Wevo, and CultureScience harness this unfair advantage and deliver higher quality models and services due their access to proprietary data.

Discovering depth and uniqueness are fairly easy to investigate, but that isn’t enough. The proprietary data also need to be one that the company can use to improve its AI models. Specifically, investors should assess the company’s ability to leverage the proprietary data for continuous model quality and performance improvements. Often the data needs significant work, labeling or other techniques to actually be effective in creating an “AI Moat”.

The AI revolution is driven by data, and the companies with the most valuable and exclusive data will be tomorrow’s winners, as long as they can leverage the data to create a virtuous cycle and continuously improve their models and services.