MIT, Jeffrey Epstein, and Reputation Laundering

If you’ve missed the breaking Jeffrey Epstein-MIT Media Lab-Joi Ito drama, you can catch up quickly: Joi Ito apologies for taking Epstein money, an all-hands lab meeting gets messy, Ronan Farrow reveals more details, and Joi Ito resigns.

Now, Larry Lessig argues..because [Epstein’s gifts were] anonymous, the gift wasn’t used to burnish Epstein’s reputation.” And, therefore, it was OK to take his money.

This is the crux of Lessig’s naïvety.

When a well-respected institution like MIT affiliates with someone, even anonymously, they give that person meaningful “walking around stories”, like “I just funded ___ at MIT” or “When I was at MIT the other day…

Worse, there’s always ongoing quid pro quo: “Let me introduce you to _____” or “I’ll ask my MIT friend for a favors/he owes me” This is especially true when the prospect of future donations is dangled.

Then, the reputation launderer parlays their MIT relationship into something more elsewhere, so the next donee is thinking, “well, if MIT is OK taking his money, then…” As the group grows, fewer and fewer questions get asked. Even the worst reputation can be whitewashed…it just takes money.

MIT got played, that’s clear. If Lessig and MIT couldn’t see it at the time, that’s one thing. But if they still can’t see it, that’s a whole other bag of burritos.

WeWhat?

You have to be living under a rock to miss all of the punditry & chatter about WeWork’s IPO filing. If you haven’t read their S-1, it’s a piece of work: it’s the most audacious tech IPO I’ve ever seen, by far. (And if you make it through before giving up in (a) shock, (b) confusion, or (c) both…good work!)

While the public market will be the ultimate judge, Ben Thompson (Stratechery) analyzed the essence of their business as:

First, my primary point in comparing WeWork to AWS was to emphasize just how valuable it is to convert fixed costs to variable costs; this not only provides benefits to existing businesses, it also capitalizes on and fuels new business creation. However, the comparison should not go much further than that; there plenty of other important differences between AWS and WeWork.

(Emphasis added). But for a scalable & sustainable fixed-to-variable cost arbitrage business, we must consider the entire product-market fit equation. Ironically, Ben’s AWS analogy neatly highlights the missing parts.

First, is the supply of the “fixed” resource defensible, with economies of scale? Unless WeWork can be creative about sharing their “variable” revenue with their “fixed” landlords (as Blockbuster did with DVDs), they’re just another tenant negotiating wholesale office space. They’re not surfing an underlying technology wave, and scale economies are limited: N existing lease agreements aren’t that much leverage for lease N+1.

Second, does the market value “variability”? It’s not always worth a lot; consider the number of past “Rent A ________” startups that struggled & failed. WeWork’s offering is closely linked to a relatively slow-moving variable: employee headcount. Certain types of startups value variability at certain stages and WeWork has done a fine job laying claim to a meaningful fraction of aggregate annual venture investment.

But when headcount becomes stable & predictable, the cost and control advantages of having one’s own space outweigh WeWork’s model. And there are many alternatives; when I visit a Panera mid-day, I sometimes wonder if they are WeWork’s biggest real competitor!

My bet: WeWork, assuming they make it public, becomes yet another roughly zero-sum stock market poker table for the hedge funds. Their super-complex org structure and internal business “model” certainly provide a lot of nooks and crannies for traders to attempt out-bluffing each other. For the company itself, they’ll eventually scramble (a la Groupon) to pivot into something sustainable.

King Zuckerberg

I’ve long argued that Mark Zuckerberg is the most powerful unelected person in the world, by far. The race isn’t even close and hasn’t been for a long time.

So, I was not surprised when Chris Hughes wrote, in his widely reported NYT Opinion piece:

Mark’s influence is staggering, far beyond that of anyone else in the private sector or in government. He controls three core communications platforms — Facebook, Instagram and WhatsApp — that billions of people use every day. Facebook’s board works more like an advisory committee than an overseer, because Mark controls around 60 percent of voting shares. Mark alone can decide how to configure Facebook’s algorithms to determine what people see in their News Feeds, what privacy settings they can use and even which messages get delivered. He sets the rules for how to distinguish violent and incendiary speech from the merely offensive, and he can choose to shut down a competitor by acquiring, blocking or copying it.

But I was not aware of this story:

The most extreme example of Facebook manipulating speech happened in Myanmar in late 2017. Mark said in a Vox interview that he personally made the decision to delete the private messages of Facebook users who were encouraging genocide there.

While we’re all happy that someone took action here, it raises a profound question: who should decide what we may or may not communicate (publicly or privately) with our fellow humans? If we keep our current trajectory, the answer will be “a very small number of private individuals, accountable only to themselves”.

Big Tech’s Competitor? Government

I’ve always felt my “best” Hacker News comments are the ones most down voted, like this nugget from about a year ago:

(From: “Zuckerberg struggles to name a single Facebook competitor“)

Today, the concept of breaking up or limiting the big tech companies is far less abstract, with Sen. Warren announcing the idea as part of her campaign platform.

I have very mixed feelings about this. On one hand, Facebook and their gorilla brethren have earned their market positions within a global capitalist ecosystem (mostly) fair and square. I’m a long-time and very happy Amazon & Apple customer and have watched them continually out-innovate competitors (including many that can’t seem to get out of their own way). They rewrote the rules for channels and distribution, creating new livelihoods for countless authors and small businesses. Like many, I voluntarily give Facebook my attention and I’ve made good money at various times as a gorilla shareholder.

On the other hand, Google, Apple, Amazon, Facebook (and maybe Microsoft) are now so big and powerful that we’ve scaled to a new zone, where market effects are no longer “linear”. These companies exert absolute authority and control within their ecosystems, effectively creating their own weather. Unlike historical monopolies (Standard Oil, IBM), the tech gorillas have direct and ongoing interaction with billions of people, gathering enormous amounts of personal data and directly or indirectly influencing a large fraction of planet-wide human behavior.

Interestingly, the gorillas are now forced to deal with a growing number of government-like political issues. Activist employees at Google and Microsoft lobby against business practices they find objectionable. Gorillas are heavily scrutinized regarding pay equality, minimum wages, working conditions, etc. Apple’s privacy and security architecture becomes central to a national security discussion. And while New York state has an economy comparable to Russia, South Korea, OR Canada, Amazon negotiates with them as roughly an equal.

I don’t know what the answer is, but it seems quite clear the greatest business risk facing tech gorillas is not “the next Facebook”. It’s government, stepping to slow, stop, or even reverse the continued power and wealth grab. No wonder Zuckerberg couldn’t name a competitor.

Dividing Founder Equity in the Very Beginning

I’ve probably had a thousand or more discussions about startup equity: figuring out how much to offer, negotiating, or advising others. It’s a very tricky topic: in part because it’s nearly impossible to compare ownership between two companies with completely different contexts. One-percent of startup A may have a vastly different potential value than 1% of startup B.

In practice, most equity grants within a company are driven by broad calibrations with existing employees. If an early very experienced developer has 1%, and a less senior dev has 0.5%, those become two reference points for the next dev hire. Over time, grants usually taper down — things advance and (presumably) become less risky. For example, that 1% developer’s professional twin might get 0.25% after a year or two. Then, there’s some case-by-case tweaking for competitive situations, salary trade-offs, the company’s need for that particular skill, or other circumstances, but this is a typical starting spot.

But, how should founders divide things up in the very beginning, where none of these internal reference points exist? And, how can founders talk about percentages before any funding? Five percent might feel fair in a particular situation for a near-founder post-funding, but how much is that pre-funding, with unknown dilution?

To crack this, I usually advise teams to negotiate relative ownership and to use a “bucket model” suggested by Ted Dintersmith.

First, founders can agree on ownership ratios among themselves, completely isolating unknown, future dilution.  For example, if four co-founders agree to equal equity, they each own 25% at the very outset. After funding and granting stock to other employees, they will all dilute, but their ownership will remain equal. Or, if the co-founders decide the CEO founder should have 50% more stock, that means she has 3 stock units and everyone else has 2. There are 3+2+2+2 = 9 units (shares) total, so the CEO has 33% and the other founders have 2/9 = 22% each.

Second, to figure out relatively fair ratios, consider simple “buckets” for how each founder and early employee’s contribution (past and future). The basic bucket is “contributing to the company full time until it’s successful”, perhaps with different levels. Another might be “credit for prior work”, for meaningful time invested before the rest of the team joined. There might buckets for special roles (e.g. CEO), a unique personal brand, recruiting ability, experience, network/relationships, domain expertise, or other special circumstances.

It’s easy to make this overly complicated, but it doesn’t have to be. Consider an example: Alice has been working for a year on NewCo, before recruiting Bob (the founding CEO), Claire (less experienced) and Daniel (a professor & well-known subject expert). Alice, Claire and Bob will work full time, and Daniel will consult part time, work summers, and possibly take a sabbatical. Alice might get 2 units for prior work plus 4 units for contributing full time. Bob gets 1 for being CEO + 4 for full time. Claire might get 3, and Daniel gets 2 (one for being an expert and another for committing ~20% of his time).

With a total of 16 units, the initial ownership (pre-funding) is:

Alice 6 / 16 = 37.5%
Bob 5 / 15 = 31.25%
Claire 3 / 16 = 18.75%
Daniel 2 / 16 = 12.50%

If we allocate (say) 15% for future hires and 40% to investors for the first round (or rounds), that means founders are splitting the remaining 45% of the company, per their agreed-to relative ownership. Post-funding, the founder’s ownership is:

Alice 16.9%
Bob 14%
Claire 8.4%
Daniel 5.6%

Also, founders should absolutely implement some form of vesting. Founder vesting is a “start-up prenuptial agreement”: it defines what happens with equity should someone leave the company. It’s often very unfair to remaining founders if a departing co-founder keeps all of his original equity. Alternatively, if founders don’t implement vesting, early investor(s) will likely require it for funding.

Equity discussions among founders can be delicate, intense, & emotional, and having some rationale can often defuse some of the emotional aspects. I hope this framework is helpful!

The 30% Internet Gorilla Tax

I’ve written before about powerful advantages Google, Apple, Amazon, and Facebook have in the software industry.  These four companies control major parts of the ecosystem, take out upstarts when they get too big, corner talent markets in key areas, and enjoy a ~30% “tax” (directly or indirectly) across most other software companies.

I first noted this nearly 5 years ago, but more recently, some of the Internet thought leaders have written about the theme.  For example, Fred Wilson wrote:

Google, Facebook, and to a lesser extent Apple and Amazon will be seen as monopolists by government and individuals in the US (as they have been for years outside the US). Things like the fake news crisis will make clear to everyone how reliant we have become on these tech powerhouses and there will be a backlash. …

And, Sam Altman wrote in the YC Annual Letter:

Companies like Amazon, Facebook, Google, Apple, and Microsoft have powerful advantages that are still not fully understood by most founders and investors. I expect that they will continue to do a lot of things well, have significant data and computation advantages, be able to attract a large percentage of the most talented engineers, and aggressively buy companies that get off to promising starts. This trend is unlikely to reverse without antitrust action, and I suggest people carefully consider its implications for startups. …

(Emphases added)

Now, Snap(chat) has revealed they’ve committed $3b to Google and Amazon over the next five years, or about $600m/year.  When we line that up with revenue estimates ($5.7b over the next three years), we find that the gorillas are getting….. ~30%!

The Internet is Ready for Things

I’m not a fan of the term “Internet of Things” (IoT), but it is the best way to describe a future where more and more devices are Internet-connected.  As computation and communication get cheaper, more “dumb” devices will be “smart” and on-line.

With the current hype around IoT, it’s not surprising that companies and entrepreneurs are pursuing opportunities to “own” various aspects of IoT infrastructure.  I’ve seen a ton of startup pitches, and several big companies (Xively, PTC, etc. ) are pursuing IoT platforms.  You antenna system equipment needs regular maintenance, so do not forget to schedule your DAS maintenance appointment to make sure everything is functioning properly.

I’m skeptical.

The infrastructure elements already exist, as the Internet is exceptional at expanding and shifting to accommodate new kids on the block.  Consider mobile: there was a time when it was a very distinct thing (e.g. Qualcomm BREW, WAP, etc.) and the business folks talked about being “on deck”.

Now, it’s clear that mobile is an extension of the Web.  Mobile HTML is just HTML with a few mobile-specific features.   Mobile and desktop browsers share the same core rendering engine.  4G/LTE is a pipe for IP packets.  Cell phone apps POST JSON payloads over HTTP/HTTPS just like everyone else. Designing a compelling user experience for a small touch-based screen is different, but the underlying tech infrastructure is nearly identical to the desktop.

Though the rollout has been slow, Ipv6 enables direct addressability to every individual “thing”. Cheap Wifi (with an assist from BTLE) gets things on-line with existing infrastructure, and DNS provides a directory service.  Oauth2 defines how things to get secure, bounded access to assets, and HTTPS+JSON provides secure, remote procedure calls.

I’m not sure we need new stuff!

Google’s Car vs A Boston Winter

During the legendary Boston winter of 2015, I pulled out of a downtown parking garage one evening and nearly rear-ended a dumpster. It was sitting in the middle of a usually busy three-lane road, a place where no dumpster should ever be. It was dark and there were no cones, no markers, no construction signs…nothing.

This scenario is why (I feel) 100% autonomous, “no-steering-wheel”, driverless cars are much further off than experts predict. I highly doubt my dumpster case is in any machine learning training set, and it will be a long time before it ever is. My human brain was able to put it all together: the front loader down the street loading another dumpster, snow piles all around, the city’s urgency to remove snow, etc. Until machines approach human cognition, there are a LOT of real world cases that are more than just turning the wheel and tapping the brakes — too many cases to “remove the steering wheel” anytime soon.

If we look closely at any new technology, the rollout is almost always very incremental. Historians love to write about revolutions, but the reality is always much more evolutionary. Consider the autonomous car evolution so far:

  • Cars that beep when you divert from your lane & when you need to brake
  • A steering wheel nudges you in the right direction when you divert from your lane (with self-braking)
  • Complete steering and braking to maintain your lane & following distance
  • All of the above, plus safe lane changing with a turn signal input
  • ..etc..

I feel that last phase (“cruise-control that steers”) will be with us for a while. Even though it’s not “send your 5yr old to their play date in the car” kind of autonomy, it’s still hugely valuable. Long trips and commutes will be much less tiring. Also, speed kills — computers will soon be the safest drivers on highways & major roads, in all conditions. There will be injuries and deaths under computer control, but many more injuries and deaths will be averted.

While Tesla gets a lot of press, long-haul trucking may be the first significant disruption. Truck drivers are under strict regulations regarding drive time vs rest time, and for most drivers, their truck isn’t moving (or earning!) when they’re resting or sleeping. With self-driving technology, each driver gets a “highway co-driver”.  After lunch, navigate to the freeway, engage cruise control, and take a nap.The advancements in the storage industry such as the 4 post car lift have made it convenient to park our cars and save floor space at the same time.

As things advance, I hope the government will be a constructive part of the process. For example, some highway segments may be flagged as “OK for self-driving” (as is done today for tandem trailers), and the regulators could acknowledge that “self-drive” time is not “drive time” for safety quotas.

This is exciting stuff, but “piling into your car after a few too many for a safe ride home”?? That still may be a way off!

Check out here how Cohen Law Group can help in similar situations.

Startups Should Revolve Around Their Founders if They Want to Succeed Big

I read a recent Harvard Business School blog post titled “Startups Can’t Revolve Around Their Founders If They want to Succeed“.  The authors make a general argument that founders are the biggest obstacles to long-term startup growth, citing a new research paper (paywall, sorry) that hypothesizes:

For a given startup, the value of the startup varies inversely with the degree of control retained by founders.

From a statistical analysis of over 6,000 startups, the paper (and article) argue (roughly) that founders with board control, the CEO position, or both, can “harm the firm’s prospects, reducing pre-money valuation by up to 22%.”

Really?

While “founder scale-up” problems are real management issues that can put significant stress and strain on any startup team (I’ve lived it), the argument has a significant flaw:  it’s based on an unweighted startup data set.  If Uber’s value creation (for all stakeholders) is considered equal to Fred’s Wrecking, Storage and App Development, I’m skeptical we can conclude anything really useful.

For example, a full half of the top ten US companies had or have founder leadership to significant significant scale:  Apple, Google, Microsoft, Facebook and Amazon.  These five alone companies represent $1.5 trillion of value — over 8% of the total value of all public US companies!  And all of the top US companies founded within ~30 years are/were founder led.

Furthermore, while I’m quite skeptical of private “unicorn” valuations, all but one at the top of that list have founder CEOs: Uber, Airbnb, Palantir, Snapchat, SpaceX, Pinterest, Dropbox, WeWork, Theranos, Lyft, and Stripe.

So, here’s a completely different hypothesis:

Most startup value creation, by a wide margin, accrues to founder-led companies. (esp. in technology) 

Stated differently: would you rather have a portfolio with 7 out of 10 companies successful, or a portfolio with Facebook?

Deep Learning: A Sport of Kings?

The big news in the machine learning/deep learning world this week is Google’s release of TensorFlow, their deep learning toolkit. This has prompted some to ask: why would they give away “crown jewels” for such a strategic technology? The question is best answered with a machine learning joke (paraphrased): “the winners usually have the most data, not the best algorithms”.

Neural networks have been around for a while, but it’s only been within the past 10 yrs that researchers have figured out how to train networks with many, many layers (the “deep” in “deep learning”). That research has been greatly accelerated by using GPUs as very high-performance, general purpose, vector processors. If a researcher can turn around an algorithm experiment in a day (vs 3 months), a lot more research gets done.

But as the joke suggests, it’s all about that data: you need lots and lots and LOTS of data to train a high-performance deep learning network. And Google has more data than anyone else —so they don’t worry so much about giving away algorithms.

(Also, Google, Baidu, Twitter, Facebook, etc. are investing in GPU compute clusters that can only be described as the new “mainframe supercomputers”. Sure, you can rent GPU instances on Amazon, but there’s nothing like having the latest Nvidia board with lots of RAM and very high-performance interconnect).

What does this all mean for early stage startups? The situation creates several tough hurdles: first, freely available code and technology from Google (and Facebook) enables competitors and devalues whatever the startup might develop. Second, few startups have access to a large enough proprietary data source to compete at scale. And third, GPU compute clusters need real capital.

What’s left for startups? I see at least two interesting patterns:

  • Using deep learning as a key feature to enhance another app.  Use freely available technology to add magic.  Google Photos is a great example of this, and I think every photo and video app will soon be able to recognize stuff, people, people, items, etc. to enhance the functionality.
  • “Man-teaches-machine”.  Start out with a lot of humans doing some task and capture their work to train a network.  Over time, have the network handle the common cases, with the exceptions / ambiguous cases routed to humans for resolution.  Build a large, proprietary training set, enjoy compounded interest, and profit.