Consumer surplus is the delta between what consumers expect to pay or are willing to pay for an item and what they actually have to pay given market dynamics. A good example of where we are generating a lot of consumer surplus is technology. I would be happy to pay for my email (and do) but I can get it for free from Gmail. A 49″ smart TV sells for about $300 on Amazon. A Samsung Chromebook is $200 on Amazon.
I like to think of all of this “found money” that consumers are getting from technology as the dividend we are getting from the technology revolution. It is also true that technology takes jobs out of the market, and adds them too, and that it may be a zero sum game or worse.
But the truth is many things have gotten a LOT less expensive over the last twenty years and that has made managing the household budget a fair bit easier.
My colleague Nick sent me this chart yesterday. I don’t know where he got it so I can’t identify the source.
What you see from the chart is that wages have increased about 70% over the last twenty years and many things, including housing, food, clothing, and most dramatically technology, have increased less, or have actually gone down in price, creating room/surplus in the household budget.
But not everything has gone down. Health care and education, most notably have increased dramatically.
So it is time to take aim at those sectors. We can do the same with education that we have done with other services. And we will. I feel that healthcare will be a harder lift, but I do think it can be tackled too.
The one that I am personally most excited about is what Naomi calls “change of venue” and within that I like the “virtual primary care” model.
A pure virtual primary model eliminates fixed costs associated with brick and mortar expansion and is able to focus resources on reaching more patients, recruiting more doctors to their platform, and improving the experience for current patients. Payments on a subscription basis allow doctors to get paid more consistently rather than waiting for insurance companies to process claims and paying overhead costs to negotiate reimbursements with their billing offices.
We have portfolio companies executing this model like Nurx and Modern Fertility and we hope to add more.
I value the doctor/patient relationship, but I think there is a lot technology can do to make that relationship less expensive, more engaging, and more convenient (for both parties). And generational changes in doctors and patients are catalyzing and facilitating this transition.
One of the themes we are deeply bought into at USV in our approach to healthcare investing is the opportunity in women’s health care, particularly providing care to young and healthy women. Our portfolio companies Nurx, Clue, and Modern Fertility are all doing that.
This podcast, which I listened to earlier this week, is a discussion of exactly that opportunity and features Hans Gangeskar, CEO of Nurx, and Carolyn Witte, CEO of AskTia.
these observations seem to indicate some unbundling of the existing large, monolithic systems in healthcare towards a more open, local, independent and transparent model, with control residing with individual users. And ultimately, this could change the way healthcare is delivered to consumers.
“A more open, local, independent and transparent model, with control residing with the individual users” sounds exactly what we like to invest in at USV so expect to see more investing in health care from us and more posts from Naomi. You can find her posts on USV.com and/or follow Naomi here.
In it, he explores whether machines are going to replace radiologists, dermatologists, etc or help them do their jobs better.
He concludes with this observation:
The word “diagnosis,” he reminded me, comes from the Greek for “knowing apart.” Machine-learning algorithms will only become better at such knowing apart—at partitioning, at distinguishing moles from melanomas. But knowing, in all its dimensions, transcends those task-focussed algorithms. In the realm of medicine, perhaps the ultimate rewards come from knowing together.
We are very excited about the possibilities of using machine learning to help diagnose medical conditions early when they can be treated successfully. We have made a number of investments in this area and I expect we will make many more.
I believe that this is the future of medicine and the sooner we get to it the better off everyone, including the practitioners, will be.