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 #4: if you build it, they will come

Leave a reply

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”

Leave a reply

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

Leave a reply

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

Leave a reply

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.

Startup anti-pattern #3: elephant hunting

1 Reply

First, two stories that highlight two different sides of elephant hunting.

In 2005, Meridio was guaranteed to win a deal worth $15m+. Meridio was a small electronic documents and records management (EDRM) startup whose software ran inside some of the world’s most secure organizations: from banks to oil & gas companies to branches of government and the military. One of its happy customers, the UK Ministry of Defense (MoD), was looking to modernize its infrastructure in a massive IT procurement worth billions. Each of the two integrator consortia shortlisted for the deal had designed Meridio into the solution. It was the largest secure SharePoint deployment in the world at the time: a great proof point of the quality and scalability of Meridio’s software. The future looked bright.

Meridio did win the deal and get the money in the end, but the process nearly killed the company:

  • The product roadmap and development prioritization became more complicated.
  • Supporting the two fiercely competitive integrator consortia required staffing up teams with semi-duplicated responsibilities: a significant distraction and increase in burn far ahead of revenue.
  • Once the MoD deal was awarded to one of the consortia, Meridio had many employees it couldn’t put to productive use quickly. The resulting layoffs impacted culture.

The UK MoD deal was important for Meridio — it influenced the 2007 sale of the company to Autonomy, now part of OpenText — but it was less impactful from a valuation standpoint than the company imagined it’d be. Winning the deal came at the expense of distraction and operational inefficiency, both of which affected growth in other areas of the business. Also, there never was another deal like it.

And now for story #2. In 2014 Life360 hit gold. After 18 months of lengthy negotiations, Life360 landed a $50m investment deal from ADT, the global leader in Home Security, coupled with a strategic joint product development opportunity that could net the company tens of millions of dollars in revenue. The team was dancing on rooftops!

In 2019, long after the commercial deal was dead in the water, Life360 decided to go public early (compared to its peers), and one of the considerations was ADT’s significant position as an investor in the company. Further, after years of development that sucked, at times, half of our engineering team’s bandwidth, the product we launched was discontinued and made no contribution to our business. When the company struck the deal employees were initially very excited. They believed that the organization they were working with would be as devoted to the strategic deal’s success as their small startup was. Three management team changes later, it became clear that the deal, which was one of the highest priority items on Life360 plate, was a pretty low priority for ADT. New execs at the company didn’t feel a real commitment to it, and a Private Equity acquisition coupled with organizational changes didn’t help much either.

Everything is easier in hindsight, but Life360 could have avoided this. Luckily, the deal didn’t end up being a company killer and the other parts of the business helped Life360 cement a great spot as a public company. It’s probably fair to say Life360’s success happened despite the ADT deal, not because of it.

What it is?

“Elephant Hunting” is a buzz term describing the practice of targeting deals with very large customers. For example, hunting an elephant in the context of a startup could be a seed-stage company targeting the likes of Google or AT&T as a customer in a million-dollar deal. These customers can provide large contracts, but they can be hard to catch and require large teams to tackle. With business-to-business (B2B) startups, there’s almost nothing more exciting (or seductive) than hunting and bagging an elephant-sized deal. It can produce huge revenue growth, provide you with highly leverageable customer references, and it’ll excite investors. Once you hunt down an elephant, it can feed many mouths (and egos) at the company for a long time. What could be better?

Be warned: the pursuit of elephants can be a dangerous game. If you fail to “kill the elephant” it might well be the one killing you. Unlike young and dynamic startups, elephants are organizational dinosaurs and striking a deal with an elephant will require your entire team — from sales to engineering — to engage with the elephant at different levels of the organization. This engagement happens over months, sometimes years. Even if you succeed in getting an elephant, you may get less benefit than you expected, as the cases of both Meridio and Life360 demonstrate.

Why it matters?

Elephant hunting can bring your company down on its knees. Here are some perils to be aware of:

  • No repeatability. Elephants are hard to catch and often there aren’t enough of them. Meridio never found another UK MoD. Life360 never found another ADT.
  • Heavy operational burden. When you pursue and, later, land an elephant, it’s tempting to put all your resources into serving them. But this can lead to neglecting other clients and missing out on potential opportunities. Both Meridio and Life360 suffered operationally while selling and, later, servicing their respective elephants. Elephants may demand extended payment terms or lower prices, which can put a strain on a startup’s finances. It’s important to carefully consider the financial implications of taking on an elephant client.
  • Missed learning opportunities. When you and your team are laser-focused on one client you might be missing the forest from the trees. As a startup, you seek scalable solutions that matter to most potential customers you want to serve. More feedback is better, and getting feedback from just one elephant makes it harder to identify the scalable, repeatable, products that your target audience needs.
  • Overpromising and underdelivering. In the rush to impress an elephant, startups may make unrealistic promises they can’t keep. This can damage their reputation and lead to the loss of the elephant and future clients. Elephants have tall expectations for products and services delivered, as well as a web of requirements across legal, compliance, cybersecurity, etc. that smaller companies may be incapable of servicing well.
  • Compromising your identity. When a startup lands an elephant, it’s easy to become absorbed in their world and lose sight of your own identity and values. This can lead to compromises that go against your startup’s mission and culture. Note, for example, how many big tech companies have had to compromise to do business in China.
  • Losing control. Elephants may have their own demands and expectations that clash with a startup’s way of doing things. This can lead to a loss of control and autonomy, as the startup becomes beholden to the elephant’s whims. On the partner/channel side, this relates to the platform risk anti-pattern. In conclusion, while landing an elephant can be a huge boost for a startup, it’s important to be aware of the perils that come with it. By maintaining a balance, staying true to your values, and carefully considering the operating implications, startups can avoid the dangers of elephant hunting and build sustainable growth.

Diagnosis

Diagnosis is relatively straightforward. Here are a few signals that you might be spending too much time elephant hunting or are getting sucked into the Savannah:

  • Are you and your sales team spending most of your time focused on one deal with a big enterprise client? Has this been going on for an extended period?
  • Are you increasing spend ahead of revenue more than what you’d normally do for just one or two deals?
  • Is a significant chunk of your engineering team’s bandwidth focused on building custom features for one big customer? Does it feel like this customer is essentially dictating your roadmap for the foreseeable future? Do you find yourself having to promise steep SLAs and help desk hours that you know your existing team can’t support now or in the near future? Startups often do need to stretch to deliver, but if your team feels that servicing the elephants will consume the entire company, they’re probably right.

Misdiagnosis

A common misdiagnosis stems from not fully understand or realizing the scope and bandwidth consumption of Elephants. Often, it’s easy for the team to get excited about big deals and they tend to look the other way. Developing and delivering products to Elephants comes with significant overhead, longer sales cycles, lower win rates, and, often, requirements and standards that don’t make a positive impact on the joint outcome, but suck a lot of time and energy from everybody in the room.

Put together KPIs and tools to help you measure the impact elephant hunting has on your Sales and Engineering teams and make data-based decisions.

If your startup is investor-backed, remember that your job is to grow equity value. Revenue, profits and growth are pieces of how equity value is determined. Ask yourself whether the pursuit or even the winning of an elephant will have a meaningful positive impact on equity value given all the positive and negative externalities.

Refactored solutions

Once diagnosed, the refactoring of this anti-pattern very much depends on the set of challenges and opportunities your company has at hand. A few ideas on how to make the most out of Enterprise customers without consuming your entire (small) organization in the process:

  • Try to strike a smaller, multi-phase, deal with the Elephant. That would help both sides build confidence and capabilities to better serve each other.
  • (Artificially) Limit the resources devoted to elephant hunting. Be ruthless about this with your sales and bizdev folks. They’re likely to gravitate towards elephant hunting — these deals tend to be very exciting.
  • Continuously measure and analyze how much your team spends on custom work (especially non-repeatable deals and non-productizable work). It might put a strain on your relationship with the Elephant customer, but good Sales and Customer success teams can help strike a balance and set expectations.
  • Do you have enough slack to sign a deal with an Elephant? One good rule of thumb is assuming that deal will require twice as much resource and time compared to your original expectations. If that’s the case, would you still execute on the deal?

When it could help?

Does this mean you should never try to hunt elephants? No, but it does mean you should think very carefully about it, and be prepared to answer a few questions: 

  1. Where does elephant hunting fit in your sales and growth strategy; near vs. longer term; lower-hanging fruit vs. higher up your sales tree?
  2. How many elephants are there for you to hunt? Is that a real market niche for your business?
  3. Do you have the human resources to hunt and satisfy elephant-sized customers?
  4. Do your sales, engineering and customer success people have the skillsets and experience to satisfy this species of customer? 
  5. Does your CEO have the bandwidth and skill to take down the elephant? This strategy often demands an inordinate amount of the CEO’s time. Which of the CEO’s other responsibilities might suffer?
  6. Does your company have the financial resources to survive and thrive in the face of typically slow decision and purchase cycles? Will investors give you (relatively) cheap cash so that you can wait for the revenue?

For many startups, the transition to spending more time on Elephant hunting is part of the startup journey from childhood to adolescence. If you have good answers to the above questions, a more mature product that is ready to scale, you and your team might be ready to make the move, but tread carefully so you don’t end up being yet another victim on the plains of the Serengeti.

Co-authored with Simeon Simeonov. More startup anti-patterns here – https://blog.simeonov.com/startup-anti-patterns/ 

Intro to Startup anti-pattern Series

Leave a reply

Co-authored with Simeon Simeonov

An anti-pattern is a commonly used process, structure, or pattern of action that, despite initially appearing to be an appropriate and effective response to a problem, has more bad consequences than good ones.

Simeon Simeonov first wrote an introduction to the value of startup anti-patterns back in 2013. To sum it up, it’s hard to pinpoint the exact set of reasons startups succeed, but experienced entrepreneurs and investors have a good sense of what drives startups’ failures.

Startup anti-patterns are all about that — patterns that increase the risks associated with startups (hey, it’s a risky business to begin with). Pursuing an anti-pattern doesn’t mean that your company will die tomorrow or in the next year, but each anti-pattern adds-up and could lead to clouding your focus and hampering your ability to execute.

Together with Itamar Novick from Recursive Ventures, Simeon Simeonov is bringing the Startup anti-pattern series to life. Stay tuned for more in this series as we work through each anti-pattern with tangible examples from our experiences as founders and investors in 100+ startups, and the experiences of guest founders from our portfolio.

Startup Anti-Patterns full list (work in progress…)

Studying repeatable patterns of startup failure (startup anti-patterns) is more useful than studying non-repeatable strategies for startup success.

Top Startup Anti-Patterns:

  1. Elephant hunting
  2. Ignorance
  3. Platform risk
  4. Analysis paralysis
  5. Arrogance
  6. Attribution risk
  7. Bad revenue
  8. Bleeding on the edge
  9. Boiling the ocean
  10. Bridge to nowhere
  11. Changing strategy instead of execution
  12. Chasing the competition
  13. Confirmation bias
  14. Confusing activity with results
  15. Consulting to product
  16. Death by pivot
  17. Deathmarch
  18. Delayed scaling
  19. Demand generation
  20. Design by committee
  21. Designing for investors
  22. Drag
  23. Escalation of commitment
  24. Escape to the familiar
  25. Escapism
  26. Featuritis
  27. Forward thinking
  28. Founderitis
  29. Groupthink
  30. Hail Mary
  31. If you build it, they will come
  32. Ivory tower
  33. Lack of focus
  34. Lagging indicators
  35. Learned helplessness
  36. Long feedback cycles
  37. Lying to investors
  38. Magic salesperson
  39. Mentor whiplash
  40. Missing your exit
  41. Myopic bootstrapping
  42. Next round only
  43. Not knowing your investors
  44. One-off customization
  45. Oooh, shiny!
  46. Overengineering
  47. Overselling
  48. Oversteering
  49. Platform trap
  50. Premature optimization
  51. Premature scaling
  52. Promiscuity
  53. Proof by anecdote
  54. Pushing a rope
  55. Raising too little
  56. Random founders
  57. Scapegoat
  58. Second class citizens
  59. Seed extensions
  60. Secrecy
  61. Silver bullet
  62. Spreadsheet Bingo
  63. Stovepipes
  64. The one idea entrepreneur
  65. Top-down planning
  66. Uber pivot
  67. Underqualifying
  68. Unicorn hunting
  69. Unrealistic expectations
  70. Warm bodies
  71. Weak board
  72. Yes man
  73. Zombie
  74. Outsourcing your architecture (via Alan Neveu)

Note: the list is not “drawn to scale.” Some anti-patterns occur more frequently than others and some are more likely to cause a startup to fail than others. 

How does Early-Stage Venture Capital Perform in a Recession? (or: It’s Time to Build & Invest!)

Leave a reply

Co-authored with Tom White from Stonks and Ram Ben Ishay from the VC Podcast

Venture Capital has existed as a discrete asset class for over seventy years now.

A great deal has been written about what constitutes Venture Capital and what does not (including by Tom White) in these seventy years. Though many different things to many different people, Justice Potter’s famous dictum in Jacobellis v. Ohio works for our purposes: “I shall not today attempt further to define the kinds of material I understand to be embraced within that shorthand description; and perhaps I could never succeed in intelligibly doing so. But I know it when I see it.”

Despite these various definitions, one consistent feature has been written, spoken, read, and heard about Venture Capital: its tremendous outperformance over the last twenty years.

The data is irrefutable regardless of timeframe: over the last five, ten, fifteen, and twenty-five years, top quartile U.S. Venture Funds have generated the highest returns of any major, institutionally-backed asset classes

Because of this, very many podcasts and blog posts have sung Venture’s praises. This post will not pile on here.

Interestingly (and perhaps conveniently, mind you), they all elide over a rather inconvenient truth: a great deal of this outperformance has coincided with arguably the greatest bull run in US history.

Given iffy economic forecasts, turbulent markets, and the looming threat of a recession, this begs two questions. Phrased generally: Did this rising tide lift all boats? More specifically: How does Venture Capital perform in periods of economic decline?

Show Me the Money Data!

Unfortunately, measuring the performance of Venture Capital is a bit more challenging than conducting a similar analysis for public equities. For instance, although some data can be traced back to 1946, the Venture Capital of Georges Doriot and Don Valentine looks very different from that of Marc Andreessen and Mary Meeker. Not to mention, the nearly thirty-five-year period from 1946 to 1979 resembles mere creek when compared with the twenty-first century’s inundation of capital.

Because of this, a middling amount of data exists from that period. Similarly (and unfortunately), data regarding Venture Capital performance from the 1980s is shockingly spotty. In order to maintain data integrity and sound analysis, the below analysis focuses solely on the Global Financial Crisis of 2007-2009. No matter, we’re making lemonade with these lemons. Let’s get into it.

Recessions Make It Rain

Put simply, the Global Financial Crisis produced some of the best Venture Capital vintage years in modern history. A great deal of this value accrued to those GPs and LPs who deployed capital during the crisis’ nadir.

Per the table to the right, Venture Capital performance leading up to the recession was lukewarm. Both during and after the recession, however, performance really 🔥heats up 🔥

Though perhaps initially counterintuitive, upon reflection, some of the strongest performing companies of our generation—Uber, Lyft, Airbnb, Pinterest, Snowflake, Slack, Square, Cloudera, Yammer, and many more – emerged and received funding during this downturn.

Looking at company formation data, the creation of these eventual winners equally spans a lengthy timeline. In fact, it is distributed almost equally before, during, and after the crisis. This supports the notion that good companies materialize through all sorts of financial climates, both good and bad.

Interestingly, the same conclusion does not hold true for the 2000 dot-com bubble. During this downturn, Venture Capital firms had some of the worst returns in history. Even top-quartile funds had single-digit IRR for their 2000-2002 vintages.

So much for a clean through line. What gives?!

Why Do Some Startups Born in Recessions Perform so Well?

Before answering, story time!

When Itamar started his venture career in late 2010 as a junior investment professional, the entrepreneurial ecosystem was still in shock after the 2008 crisis. Despite this, both time and luck were on his side; he was fortunate to have a front-row seat to the funding of some massive VC outcomes during this period. SentinelOne, Honeybook, and LendingClub are just a few that come to mind.

Though companies founded during recessions have a much harder time raising capital, they do benefit from three key advantages:

1) A Focus on Financial and Operational Discipline

Unlike the companies formed in the last three to five years, recession-born businesses must be more customer- and revenue-focused in order to survive. In these environments, founders sprint to build a self-sustaining business because they cannot rely on the safety net of Venture Capital to rescue them. Many aim to become default alive as soon as possible. Those that do not—or cannot due to poor unit economics or lack of product-market fit—seldom make it out of these periods alive.

This transcends the quantitative.

Good companies formed during bad times seem to instill a uniform “tighten-the-belt” ethos when it comes to controlling expenses and cash flow, building sustainable unit economics with quick cash recovery cycles in order to stay default alive. When executed correctly, these upstarts will grow into more efficient, robust businesses.

2) Less Competition

In times of crisis, fewer mavericks take the entrepreneurial leap and bigger companies retreat from new vertical expansion. These complementary forces drastically shrink the competitive landscape.

In fact, during the most recent tech bubble (i.e. 2018-2021), there seemed to be far too much money chasing far too few quality businesses. 

Like weeds, bad, buzzy ideas proliferated and sucked precious capital from good businesses. Driven by FOMO and behavioral contagion, new, sexy ideas attracted the attention of entrepreneurs and VCs alike. This is the barren soil that competition produces; namely, an environment that makes it extremely difficult to produce a singularly-dominant, fast-growing, healthily-operated company. 

3) Talent Acquisition Becomes Easier

During a recession, companies recruit a great deal less. Look no further than recent headlines from Meta, Google, and other giants about current/forthcoming hiring freezes.

Such an environment makes it far easier for David (i.e. startups) to hire talent both from Goliath (i.e. established tech companies) and Fellow Davids (i.e. other startups). 

The below table showcases the cyclical oscillation of job creation by comparing younger and older firms over the last twenty years. During the Great Financial Crisis, job creation slowed by over 50%.

What’s Venture Capital Have to Do with It?

Venture Capital’s beauty derives from its focus on not what is, but rather what could be. As Sebastian Mallaby wrote in Power Law: Venture Capital and the Making of the New Future

Of course, investing in what is categorically impossible is a waste of resources. But the more common error, the more human one, is to invest too timidly: to back obvious ideas that others can copy and from which, consequently, it will be hard to extract profits.

As such, VC is an asset class focused on the long term—more interested in generational paradigm shifts than instantaneous profit extraction.

This is not a bug, but an invaluable feature. Because of this, at its earliest stages – Pre-seed, Seed, and Series A – VC is inherently anti-cyclical. Just think: companies into which Pre-seed investors deploy capital will reap potentially-mammoth rewards not in five to ten months, but in the subsequent economic cycle, five to ten years from now.

It’s worth mentioning that Later-Stage VCs tend to be much more sensitive to economic cycles due to their focus on sufficient capitalization for survival or near-term exit opportunities for liquidity. In recessions, exit timelines become muddled and can be shorter than the time horizon at which late-stage and pre-IPO funds tend to invest.

That said, our focus is on VC’s earliest stages. During recessions, early-stage investors benefit from:

Lower valuations

In recessions, deals become far less competitive. Because of this, it becomes easier to win allocation into high-quality deals (i.e. those that can return a fund and lead to venture-sized outcomes).

Less Market Competition 

As dealflow declines, so too does the pace of capital deployment. This forces VCs to focus more on proper deal due diligence before writing sizable checks. As is said, slow is smooth and smooth is fast.

Though not in the interest of predicting the future, August 2022 is rife with trends that seem to portend recession.

For example, in Q2 2022 Angel & Pre-seed deal activity has fallen ~30% QoQ:

Caveat Emptor and do with that information what you will.

History shows some of the best opportunities come in bad times, not good ones. Though we’re not in the business of prediction here at Recursive Ventures and Stonks, in the words of Lord Byron: “The best prophet of the future is the past.”

Put simply, if public/crypto market turmoil persists, early-stage valuations may well drop ~30% or lower (i.e.below levels seen merely a year ago!).

If that happens, don’t run out of the store when there’s a discount. You would do well to heed Warren Buffet’s advice to be greedy when others are fearful.

Who knows? Such greediness may provide a generational opportunity to invest in quality businesses at bargain prices.

Come along for the ride — we hope to see you on the moon. 🚀

N.B. Investing is a risky endeavor. Investing in startups is particularly risky. Nothing herein constitutes financial advice.

What are “Solo Capitalists” and why are they winning?

Leave a reply

Traditionally, Venture Capital has been driven by the firm. 

The fundamental idea was based on VC Partnerships, with the sum of all partners and investments professionals adding up to more than the individuals themselves. The traditional VC firm with multiple partners has the ability to come across more investment opportunities and be able to better diligence those opportunities. The firm model was seen as lower risk, with more checks and balances in place before cutting a big check.

With the pace of startups right now, though, the firm model is sometimes being left behind. Startups are being built and scale faster than ever. Companies like Deel are reaching unicorn status, scaling from $1m in ARR to $100m, in less than 2 years. Information and deals flow more efficiently, and referrals and trust building happens online and, to a certain degree, in much shorter cycles. VC firms see many more deals, but often they can’t move fast enough.

Funding for startups has also changed, especially at the earliest stages. Good ideas and founders get funded faster than ever, sometimes in days instead of months. Many startups are continuously fundraising. The lines between early stage investment stages (Pre-Seed, Seed) blur together into a series of continued SAFE investment. VC investments builds momentum alongside the company’s business momentum. The notion of waiting for a “formal” big party round is replaced for many founders with several funding chunks providing them with the liquidity they need to continue to scale their businesses.

With the profound changes in startup building velocity and funding ecosystem, it shouldn’t be a surprise that new types of VCs are disrupting the traditional model. The firm model, with its bureaucracy, can’t always keep pace with startup growth. The Solo Capitalists is one model that emerged.

New models are often considered reckless by the old VC guard. But, Solo Capitalists are outperforming many of the traditional funds.

Before we dive into why, let’s first cover the definition of “Solo Capitalists”. Solo Capitalists are typically:

  1. The sole investment decision maker in their fund (and the only General Partner)
  2. Run a lean shop. They often don’t have an Office or Staff (other than Ops and Backoffice, often run by the likes of AngelList and Carta)
  3. Writing larger checks than Angels and competing directly with VCs, raising $50m+ funds and investing $1m+ into funding rounds
  4. Increasingly support their portfolio company’s beyond the early investment stages, leading or investing alongside other VC firms in subsequent rounds all the way to an IPO


Leading “solo capitalists” manage more money than many funds. Oren Zeev manages more than $1.5 billion without additional investment support. Elad Gil, Josh Buckley, and Lachy Groom manage funds in the hundreds of millions with similarly lean structures.

So why does the Solo Capitalist model seem to work so well?

Ultimately, it comes down to the customer.

Entrepreneurs are the customers for VCs (I tend to think about LPs more as partners on the journey). Do founders need VC partnerships? Often they don’t. What founders crave is a single decision maker they can build a trusted relationship with. A partner for the journey who can be hands-on when necessary and understands the business and its evolution.

Founders want a single decision maker who can move as quickly as their business needs (no more “let’s talk again after our next Monday morning meeting”), is unhindered by firm politics or status (no “unfortunately the Senior Partner decided that your business is not one we can continue to support”), and can make informed decisions with full understanding of the business (Why would another partner in the firm investing in Enterprise Software weigh-in on a Consumer investment?).

Further, Solo Capitalists benefit from freeing up the most valuable asset VCs have: time. Without significant operational and partnership overhead, the Solo Capitalist’s time is dedicated to making investments decisions quickly. Solo Capitalists win through speed, empathy, and expertise. Many are current or previous operators, giving them empathy for founders’ journeys. Some bring unique expertise to the table, too.

Naturally, there are potential pit-falls to Solo Capitalists. A single person can’t be an expert in more than a few areas, and having a team of smart folks around the table to weigh-in can help investors avoid missing obvious (and less obvious) hard questions that should get asked ahead of investing. Solo Capitalists often mitigate some of those concerns by seeking help from advisors and experts.

Established institutional LPs are aware of this trend and are increasingly backing Solo Capitalists. University endowments and other institutional investors have funded several top managers. In doing so, these LPs seem to have made peace with the risk of having a solo GP. A side benefit for LPs is that Solo Capitalists are typically more efficient and may generate higher returns due to lower management fees.

Increasingly so, the Solo Capitalist model is a win-win-win to all sides of the Startup funding table — Founders, General Partners, and Limited Partners.

Recursive Ventures top 100 Global seed investor

Leave a reply

Humbled to share that I’ve been chosen #17 on the inaugural Business Insider Seed 100 – The best early-stage investors – list of Global seed investors made by Business Insider and Tribe Capital. Very thankful for the opportunity to support amazing startup founders. Read more about my investments and strategy at Recursive Ventures.

https://archive.is/8M4fu

Berkeley SkyDeck accelerator – application now open

Leave a reply

I’ve recently joined Berkeley Skydeck as an ambassador. It’s a wonderful program and a great fit for startups who are interested in building their business in the U.S. and more specifically in Silicon Valley.

On top of investing $100k, SkyDeck offers a lovely shared office space in Berkeley, and has resources lined up for founders working on deeper technical challenges in Life Sciences, Robotics, and AI. By tapping into the broader UC Berkeley research community the program offers access to faculty as well as cutting edge research labs.

2020 cohort application is now open. Startups can apply here. I’ve recently chatted with Israeli media on the topic (in English, and Hebrew).