too long to tweet


DLD13 Keynote

This is the approximate text of the keynote I gave at DLD13 today in Munich. I felt my delivery of this (admittedly, relatively dense) material was not the best, but the content is crucially important. To that end, I am posting the notes here. They were edited for grammar beyond the basics, so you will have to forgive the occasional fifth-grade prose.


Many of today’s big data companies are trying to tackle problems that just aren’t nearly big enough. 

Most are focused on marginally improving existing, digital businesses, but I believe the next big wave of opportunities exists in centralized processing of data gathered from primarily analog systems.

At PayPal, where I was the CTO, we succeeded because we gained deep understanding of the immense quantities of behavioral data that we captured in processing millions of transactions per day. We learned so much about our customers, that we could predict their intentions, and prevent vast majority of intentional fraud. 

At HVF, the project I began in 2011, we seek to create businesses that improve the analog, real world through deep understanding of data. I will tell you more about that in a bit, but first let me expand on what I mean by “digital sensors over analog data.”

Collaborative consumption is a huge current trend. To me, it started to make sense with Über — idling black limo cars put to better use. Über (where I am now an investor) created a simple piece of software to send the idle ones to price-insensitive consumers in need of a cab with a bit of flair. 

The world of real things is very inefficient: slack resources are abundant, so are the companies trying to rationalize their use. Über, AirBnB, Exec, GetAround, PostMates, ZipCar, Cherry, Housefed, Skyara, ToolSpinner, Snapgoods, Vayable, Swifto…it’s an explosion! What enabled this? Why now? It’s not like we suddenly have a larger surplus of black cars than ever before.

Examine the DNA of these businesses: resource availability and demand requests — highly analog, as this is about cars, drivers, and passengers — is captured at the edge, automatically where possible, then transmitted and stored, then processed centrally. Requests are queued at the smart center, and a marketplace/auction is used to allocate them, matches are made and feedback is given in real time. 

Utilization per dollar goes up, because there is simply less idle time! So efficient is this new approach in fact that limo companies started hiring drivers to drive for Über only.  

I am ignoring some key details, like the need for mutual trust between participants that is at least initially enabled by the presence of a trusted third party, and the feedback loop from consumers of the service, but at its core, these business all look very similar.

A key revolutionary insight here is not that the market-based distribution of resources is a great idea — it is the digitalization of analog data, and its management in a centralized queue to create amazing new efficiencies.

Consider the cab calling experience vs Über. When dialing up a cab, you are managing your own spot in the queue. If you hang up in anger, you go back to the end. If you stay on, listening to the on-call tune, you may be an infinite loop, as there is no feedback! 

And even if you are willing to pay a hundred times more than everyone else waiting ahead of you in line to speak to dispatch, you never get to express that demand. The data exists in an analog-only format, and it moves at analog-only speeds.

With Über, the queue can be managed centrally (because the information is converted to a digital format at the edge) with nearly complete transparency to you — you know when the resources you want become available, you know how long you have to wait for them, and most importantly, you are generally assured through feedback that you have not been forgotten or ignored.

So why is all this now? Cheap digital sensors over analog resources (cars, houses, humans, etc) — AT&T pays for your GPS… so you’d consume more data of course. Mobile broadband, of course. Even more key is the critical mass of pretty smart devices that are clients for real-time participation in queue management with feedback. It’s the smart-enough terminal model. 

As an aside, consumers want sexy sensors that give visual feedback to motivate action (and more data) — like the Nike Fuel band. Machines, on the other hand, just want cheap sensors to get more data from.

I sometimes imagine the low-use troughs of sinusoidal curves utilization of all these analog resources being pulled up, filling up with happy digital usage. 

Private jets spend 1h per day on average in the air. BlackJet promises to make that number closer to the commercial average — 10h/day. And not everyone in a suburban neighborhood needs their own lawn mower — they need an app to schedule one of the local kids to come by and take care of their overgrown grass. 

The defensibility of these businesses lies in their ability to build a network effect — a network effect of data. Once a business understands substantially more than any one of the resources managed in their queue, it’s effectively impossible to compete with them on price — they can always see more of the usage as it happens, and price it more efficiently, pushing any competition out. 

So what other businesses can we expect to emerge in analog-data-driven, central-intelligence queue marketplace businesses? Some interesting ones are probably already being built: a market for private neighborhood security (off-duty cops)? An auction for short-term patent licenses (litigator included)? Technology already enables efficient redistribution for your spare change: it’s Kickstarter and AngelList. We will definitely see dynamically-priced queues for confession-taking priests, and therapists!

How about dynamic pricing for brain cycles? We have been maximizing utilization of very high-value, very low-frequency specialists — today you can already rent the brain of a data-mining genius via Kaggle by the hour, tomorrow by brain-hour. Just like the SETI@Home screensaver “steals” CPU cycles to sift through cosmic radio noise for alien voices, your brain plug firmware will earn you a little extra cash while you sleep, by being remotely programmed to solve hard problems, like factoring products of large primes.

There is also a neat symmetry to this analog-to-digtail transformation — enabling centralization of unique analog capacities. As soon as the general public is ready for it, many things handled by a human at the edge of consumption will be controlled by the best currently available human at the center of the system, real time sensors bringing the necessary data to them in real time. The freshest, smartest pilot, most familiar with the particular complicated airport will land your plane — via remote control.   

So what’s after that? This is where it gets really interesting. These new modes of operation — remote controlled cars and planes flown by pilots you can’t see, rides in quasi-cabs with people you have never met, legal advice from lawyers whose license you cannot really check. This is going to add a huge amount of new kinds of risks. 

But as a species, we simply must take these risks, to continue advancing, to use all available resources to their maximum. Yet these risks are real, and they cannot be ignored.

The way to deal with risk is of course some form of insurance. Modeling loss from observed past events is hardly news, but dynamically changing the price of the service to reflect individual risk is a big deal. My expectation is that next decade we will see an explosion of insurance and insurance-like products and services — leveraging those very same network effects of data, providing truly dynamic resource pricing and allocation. 

This is the purpose of my new project, HVF — to bring these new products to those that will benefit from them the most. We see data-driven understanding and pricing of risk as the great opportunity to improve lives. The reason the notion of analog is so important here is because it ultimately also means “human.”

Understanding the changing risk profile of a person can deliver to them amazing opportunities they wouldn’t have in today’s world: inferring that a particular college grad is financially responsible by looking at their tweets could allow them to buy their first house on credit, at 21, without any history, and looking at someone’s heart rate monitor data could make their cardiovascular healthcare cost-free. 

These are not non-controversial topics — privacy, unfair discrimination, built-in biases are all possible, and we must be thoughtful and diligent in how we go about bringing this future. But I believe that what we can enable with data insights greatly outweighs the downsides. 

Here is what I mean, by way of a simple example.

On a Sat morning, I load my two toddlers into their respective child seats, and my car’s in-wheel strain gauges detect the weight difference and reports that the kids are with me in a moving vehicle to my insurance via a secure message through my iPhone. The insurance company duly increases today’s premium by a few dollars. 

My keepHonest app sees this too and immediately offers me up as a customer to a few competing insurance companies in the background, but nobody is willing to charge me less right now, and the phone chirps sadly to let me know I’m now paying a higher premium. Safer, but more expensive. 

But In a few hours, my car’s GPS duly reports to my insurer that I only drove two miles to the park, never sped and, and observed all traffic signs. My phone now chirps happily: not only has my rate been discounted, several companies are offering me a deal on insurance!

So to conclude: I believe that in the next decades we will see huge number of inherently analog processes captured digitally. Opportunities to build businesses that process this data and improve lives will abound.