Category Archives: Venture Capital

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

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. 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.

Investing in AI companies? Think Data first, AI second

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,,, 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.

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

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?

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

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.

The convergence of SaaS and Consumer

Silicon Valley loves talking about the next big trend and how it impacts the world, so it should come as no surprise that the convergence of SaaS and Consumer technologies (or “Consumerization of the Enterprise”) has been on the radar for a while now.

But there are less discussions about what it takes to win in the “new age” of SaaS companies, nor about the shift in mindset and skillset that startup investors and founders have to undergo to succeed.

To be successful in the “new age” of SaaS Founders and early employees need to have a mix of SaaS and Consumer DNA. Vertical Market Networks, B2B2C companies, and software solutions serving Small and Medium Sized businesses (SMBs), are scaling quickly because of consumer-like characteristics.

Vertical Market Network (read more here) are scaling faster than ever because they are creating virality among businesses. Honeybook (which dubbed the concept of Vertical Market Networks) connects SMBs in the event space, bringing together wedding planners, photographers, and florists, among others, to serve a customer for their project. One service provider usually takes the initiative and starts inviting others, virally growing the reach of the platform. A virtuous cycle begins, similarly to what you would expect in a Social Network, but in this case a business professional network.

B2B2C companies are not a new thing. In the past B2B2C companies were mainly focused on their primary customer – businesses. If businesses were happy the company was successful. But what has been an fairly easy task is becoming harder and harder. Feedback channels from consumer to businesses are prolific and effective and low quality B2B2C products instantly reflect poorly on the brand. Gone are the days where you can have a crappy mobile app and get away with it.

The quality bar required to meet consumer demands, especially in Mobile and IoT, is ridiculously high. Millions of apps flood the app stores and tech startups are going after any connected appliance you could put in your home. Consumer expectations are insanely high and users have little patience for error or quality issues. Everything needs to have a premium feel. If on the web the cost of an error would result in 1x consumer confidence loss, an error on mobile would lead to 10x loss. Even consumer companies have a hard time doing mobile right. One great quote from Facebook: “When Facebook made the move to mobile, it had to ditch its “break a few eggs to make an omelette” mentality, a big change in the company’s core values.” (read more about it here)”. For B2B2C companies to succeed they have to put both the Business and the End-user first. Almost mission impossible.

Last but not least, Businesses themselves are changing rapidly. The United States labor market has been undergoing a substantial shift toward small-scale entrepreneurship. The number of proprietors – owners of businesses – who are not wage and salary employees, has skyrocketed.

Building solutions for SMBs isn’t significantly different than building products for consumers, and requires a shift in focus. The line between work and personal is blurring away, and business users have no patience for systems that don’t meet their demands as a user. Companies serving Small Businesses need Product Development professionals who understand how to build products that have world class User Experience and breathtaking design. Economies of scale is key and Product Growth professional help solutions scale as fast as it takes to serve an online ad.

One example of a company that nailed it is MileIQ. MileIQ publishes a Mobile App that automatically logs all rides and lets you easily deduct or expense miles with total peace of mind. Most of their users are sole proprietors or professionals using MileIQ for businesses. However, the company has been built from the ground up with a consumer mindset. MileIQ invested early in hiring Mobile Growth specialists and being ahead of the curve in mobile acquisition. The focus enabled the company to scale the number of paying users in a very short time period.

That is why I particularly like supporting SaaS founders that have a mixed background of Consumer and Enterprise. The team should first and foremost excel in building a product businesses love and achieving success by scaling Sales and Marketing.
However, founders will stay ahead of the pack by baking “consumer-like” characteristics into their product, make it viral, a pleasure to use, and a product businesses and their users will rave about. The companies who embrace that will shape the next wave of innovation in business productivity.

Bots are great for the Enterprise, not just for consumers

2016 was already declared the year of bots. While potentially being slightly over-hyped, it seems that many consumer companies have been putting a lot of meat behind their conversational UI efforts.

Facebook is banking on its messaging apps to get back into becoming a leading platform again. They are already allowing users to chat with businesses for customer service and have integrated with Uber to allow people to call an Uber through Messenger. Up-and-comers like Kik are thinking about “importing” WeChat’s success in China to the US.

If indeed there is a broader shift away from traditional point-and-click apps to chat-based user interfaces that is a shift not just for consumer tech but also for the Enterprise. The same fatigue that consumer have with apps is also true for prosumers occupying a work station at work. They get several software solutions for HR, a few more for communication and social networking inside the organization, Many more to sharing content, and so on and so forth.

The transition to bots and conversational interfaces could represent a major point of disruption in the interface paradigm, leading to a slew of incumbent startups going after traditional Enterprise players. There are so many options to explore. What about a conversational analytic platform? How about search and information queries inside the org. run by an bot talking to multiple folks? Maybe a friendly HR bot can help you out with employee benefits? and believe it or not there is already a conversation lawyer out there called Ross ( courtesy of IBM Watson.

But what about the distribution of those services? Companies like Slack are looking at chat-as-platform as a major next step and that could be one entry. Another simple and under the radar channel is email. Plain old email, requiring no apps to install and barely any configuration to hustle with.

Case in point is Clara. I love my Clara. She might be dumb as hell sometimes, but that is when the human kicks-in and corrects course. Hopefully there is some machine learning going on when that happens as the service seems to improve all the time. I’ve recently surveyed folks who have engaged with Clara only to find out that 90% had no idea they are talking to a machine, with the 10% that did know being Silicon Valley folks who just happened to hear about Clara.

And off course there is Siri and the now Alexa from Amazon. The other I came back home and my three years old toddler has totally lost interest in his previous hobby, the iPad. He spent the entire afternoon busy bossing Alexa around, cracking up whenever she replied to his commands.

Although Alexa currently just resides inside Echo, a consumer product mostly occupying kitchens, I’ve actually started using Alexa for more and more semi work related chores. For example, she is excellent at figuring out what my next meeting is an how traffic is looking (“Bay Bridge traffic is awful today. Thanks for asking”) I can see a natural evolution to engaging with a “personal assistant” – Alexa for business – making every employee a tad more efficient.

All in all it’s exciting development, making technology more accessible and helping us humans become more efficient at whatever we set out to do, including business.

What’s wrong with the Smart Home?

For a while now we have had a slew of companies big and small promise us that the age of the Smart Home is finally here. The industry has seen a rash of early products that have raised soaring expectations and contributed to an expanding universe of ideas – it has become one of the hottest topics in IoT over the last two years (see my post about CES earlier this year –

The “Smart Home” is an idea representing the culmination of many consumer-focused technologies, resulting in a magical residence equipped with lighting, heating, electronic devices, information, entertainment and other home components interacting together seamlessly and controlled remotely. This concept has fueled the imagination of entrepreneurs and tech savvy homeowners. Even my wife got used to having August the Smart lock gracefully unlock the door for her when she comes back home. Yes, she is loving it, thanks for asking.

However, Despite the “future is now” proclamations by industry observers, Smart Home technologies have been in the works for years and even though the first wave of Smart Home products is out there in the market I think we are very early in this cycle.

There are too many blockers and products are not working as well as they should. Here are a few challenges that have been plaguing this market.

Products not yet ready for mass market
While people are interested in the technology, they also aren’t ready to buy it. And I don’t blame them. Many products feel like a V1 or even a beta, with a disregard for usability and an incoherent story about what the product can do for people. This mean that anyone interested in buying a connected product quickly encounters a cautionary tale that makes them think twice about spending $200 on a connected door lock.

Connectivity standards
WIFI and Bluetooth are just not a good fit for connected home devices. Even Bluetooth Low Energy (BLE), which is a newer standard is not a great fit. It’s nobody’s fault, but the devices and consumers who buy them end up paying the price. These standards haven’t been designed with IoT devices in mind and don’t support use cases such as super low latency with consistent very reliable connection.

ZigBee and Z-Wave have been touted for years but market adoption of both is still anemic. Case in point – have you ever seen a smartphone that has any of these new IoT standards?

End point solutions vs. Platforms
So far, with the exception of SmartThings and a few others, the big promise of having connected devices talk to each other has not been delivered. The solutions that are selling well are actually all point solutions – Dropcam, Nest Thermostat, Canary, etc.
Non of these devices are “Platform” today. Unfortunately, the few platforms that are out there are not playing nicely with the leading point solutions leading to a frustrating user experience.

I think 2016 or 2017 will be a wonderful year for Home Automation filled with breakthroughs. Now all we need to do is fast forward to that time… I can’t wait for my fridge to start talking back to me when I forget to throw out my out of date eggs.

The Israeli VC market gap

The fact that there is currently a sizable gap in the Israeli VC market is not new, but lately it seems that the situation is slowly improving. Also, US based venture firms have noticed this market gap and are making a move to benefit from it.

A quick recap of the he Israeli VC industry history shows that 2010 has been the most difficult year for Israeli VC funds since it’s inception in 1992. Despite improvement in macro economical factors in 2010, Israeli VC funds were not able to attract new capital during 2010 (Yes, that’s right, they have raised $0). 2009 wasn’t that good either, with only $234 million raised by Israeli VC funds and $200 million of that amount raised by just one fund – Sequoia Israel.
Local Funds still hold approx. $1.2B and are able to continue investing in 2011. Accordingly, Hi-Tech investment in Q1 2011 have gone up by over 100% compared to Q1 2010, with 140 Israeli high-tech companies raising $479 million from venture investors, both from local (69%) and foreign firms (31%).
However, the future doesn’t look as promising and the ability of Israeli VC firms to raise follow-on funds in 2011 and 2012 will have a strong impact on the future of Israel’s high-tech sector.

Meanwhile, a few US firms are moving to close the market gap. This week, two firms have expanded their Israeli activity – Innovation Endeavors and Greylock Capital.
Eric Schmidt’s innovation endeavors, Founded a year ago, does not manage a predetermined amount of capital, but locates and invests in early-stage start-ups. Innovation endeavors appointed Doron Alter to head its local team and the fund’s managing partner, Dror Berman, will closely supervise the Israeli operations.
Greylock Capital has just announced a new $160 million fund aimed at internet technology companies, deployed between Europe and Israel. This is Greylock Capital’s second fund, with he first one investing in Israel since 2006.

With new and improved dynamics in the US IPO market and Israeli internet startup firms such as Conduit and Wix doing well the IVC’s (Israeli Venture Capital research center) outlook for capital raising is cautiously optimistic. Furthermore, Israel’s Ministry of Finance has recently announced an incentive program for Israeli Limited Partners to invest in Israeli funds and the program is expected to increase investment by $220 million in 2011-2012.
Hopefully the improved dynamic coupled with renewed US interest will help keep Israeli innovation on track and further boost the local Hi-Tech industry, which is the Israel’s most notable economy’s growth engine.

A reminder about what platform play is all about

The action on the Twitter platform last week with UberMedia reminded me what platform play is all about.

Talking to colleges, I was surprised to hear that some people think that UberMedia can “beat the platform” and create an off-Twitter network. Knowing first hand some of the developer policies that platforms like Facebook and Twitter put in place, it’s pretty easy to see that any of those platforms can shut down any service they want, whenever they want, without potentially having a reason. Off course, that can be a highly unpopular move that will result in bad PR and upset users, but if it’s “self defense” they can do it with a blink of an eye and might actually get away with it. Furthermore, even if the consequence of shutting down a company that is positioning itself as a competitor is some loss of users or a slight regression in adoption rate, that might be a small price to pay compared to the alternative.

Personally, I think Twitter made the right move, they got worried that UberMedia controls such a large percentage of twitter’s user base, so they made UberMedia behaves sooner rather than later, before they have an even greater user reach. Twitter could have looked away and ignored it, but they decided to stop this madness right now, and make sure UberMedia remembers who controls this platform. As a nice side benefit, twitter also enjoyed two days of increased user adoption for their mobile and web tools because the redirected users that wanted to learn more about the outage to their own applications.

Another recent example is an automatic algorithm used by Facebook to block application according to some criteria. Developer’s claim that Facebook is blocking application algorithmically that are growing “too fast”, regardless of whether they abuse Facebook’s developers policy. You can follow the conversation onQuora here.

At the end of the day, companies building layers of services on top of those platforms need to understand the rules of the game, and realize that as long as everybody gets along it all good, but if something goes bad, the platform can always pull the trigger on them, at any time.

Update(3/11/2011): Twitter have formerly announced that third-party developers should stop doing Twitter clients. Here is updated they have just made to their API terms of service:
“Developers have told us that they’d like more guidance from us about the best opportunities to build on Twitter. More specifically, developers ask us if they should build client apps that mimic or reproduce the mainstream Twitter consumer client experience. The answer is no,” Sarver writes very matter-of-factly.

“If you are an existing developer of client apps, you can continue to serve your user base, but we will be holding you to high standards to ensure you do not violate users’ privacy, that you provide consistency in the user experience, and that you rigorously adhere to all areas of our Terms of Service. We have spoken with the major client applications in the Twitter ecosystem about these needs on an ongoing basis, and will continue to ensure a high bar is maintained,”