My favorite book of 2020: The Future is Faster Than You Think.
What happens as AI, robotics, virtual reality, digital biology, and sensors crash into 3D printing, blockchain, quantum computing, and global gigabit networks? How will these convergences transform today’s legacy industries? What will happen to the way we raise our kids, govern our nations, and care for our planet?
Technologies are not only exponentially accelerating, they’re indeed converging and this multiplying effect transforms businesses, industries and our lives (from the Future of Shopping, Advertising to FS, Food, Education and Healthcare). Written right before Covid, it’s packed with 76 pages of references 😅 and a perfect way to project ourselves into a new decade (which is complicated with our local and linear brains).
“The Six Ds of Exponentials” introduced in the book BOLD by the same authors were the growth cycle of exponential technologies: Digitalisation, Deception, Disruption, Demonetization, Dematerialisation and Democratization.
In “The Acceleration of Acceleration” chapter, out of 7 forces, the most interesting one to me was Force #6 New Business Models: The Crowd Economy, The Free/Data Economy, The smartness Economy, Closed-Loop Economies, Decentralised Autonomous Organisations, Multiple Worlds Models, Transformation Economy.
The Future of Advertising chapter spells the end of brands which we feel already when we just re-order our usual groceries on Redmart or Amazon. The algorithm and the metadata are influencing our purchase more than brands who find it more and more difficult to reach us. The scenario described here is “Shopping JARVIS”:
” Imagine a future when you simply say: “Hey JARVIS, buy me some toothpaste.” Does JARVIS watch TV? Did he happen to catch those late-night ads filled with bright-white smiles? Of course not. In a nanosecond, JARVIS considers the molecular formulations of all available options, their cost, the research that supports their teeth-whitening claims, published client-satisfaction reports, and evaluates your genome to determine the flavor formulation most likely to tingle your taste buds.
Then it makes a purchase.
Taking it a step further, in the future, you’ll never actually have to order toothpaste. JARVIS will be monitoring your supply of regularly consumed items—from coffee, tea, and almond milk to toothpaste, deodorant, and all the rest—and will order supplies before you realize what needs restocking.
[…] We’re heading toward a future where AI will make the majority of our buying decisions, continually surprising us with products or services we didn’t even know we wanted. Or, if surprise isn’t your thing, just turn that feature off and opt for boring and staid. Either way, it’s a shift that threatens traditional advertisers, while offering considerable benefits to the customer.”
The Future of Entertainment chapter spells the end of “passive media”. Active means the information flows both ways. Think AI “personas” of real people. Immersion marks the 3rd shift in content: “when it comes to attention, active trumps passive, and immersive trumps active.” Examples are:
“[…] your AI also inserts a few videos from your phone – movies of your wife laughing together. Happy memories remind you of what’s actually important. And the mash-up works perfectly. By the time you’ve finished your drink, that bad mood has lifted.” […] Most of this technology is already here, […] “affective computing”, or the science of teaching machines to understand and simulate human emotions.
Brain-computer interfaces (or BCIs) […] a direct computer-to-cortex connection.
As technologies continue to converge, the scale of disruptions will only increase. The smartphone video camera was an uprising, allowing the masses to become makers. Then platforms like YouTube give those makers a playground, and a way to get paid. But there are now app-based services like Bambuster that help anyone make their own live-streaming broadcast network, a development that allows creators to take aim at entire entertainment ecosystems. Blockchain will amplify this process. By allowing artists to create unchangeable digital records of their work (making piracy impossible), and because its transaction costs are negligibly low or nonexistent, blockchain is bringing us to that fabled land of content creation: micropayments. […] Direct to the fan, no middlemen.
The Future of Education highlights that multi-sensory learning trumps other forms. Technology allows us to create an infinite variety of immersive high quality teaching environments. “Education could be VR’s killer app. More likely, it will be a combination of VR and AI.”
The Future of Insurance chapter taught me where the term “underwriting” comes from. I love this story! Edward Lloyd opened a coffee house in London and to earn patrons loyalty, he provided reliable and accurate shipping news to his customers thanks to a network of correspondents in ports across Europe. When he expanded his business, he moved to 16 Lombard Street:
“This newer, larger venue was tricked out with wall-to-wall blackboards and a central pulpit. […] The bankers who frequented Lloyds were willing to collect premiums in exchange for taking shipping risks. They dubbed this process “underwriting”, as bankers would literally write their names on the blackboard under the name of the ship and a list of the trip’s details: its cargo, crew, weather, and destination. “
The rest of the chapter covers the trends in insurance: “[… driven by] an upsurge in information and collaboration the insurance industry is again about to be completely transformed. […] First, by shifting the risk from the consumer to the service provider, entire categories of insurance are being eliminated. Next, crowdsurance is replacing traditional categories of health and life insurance. Finally, the rise of networks, sensors, and AI are rewriting the ways in which insurance is priced and sold, remaking the very nature of the industry.”
“[…] with car-as-a-service, […] the car insurance market could shrink by an astounding 60% by 2040.”
“[Autonomous vehicles like Waymo’s] trips were data-gathering missions, with the resulting information being used to train Waymo’s AI. The big deal here is both safety and a nearly unassailable market advantage. All that data puts Waymo far ahead of the competition. […] Already traditional insurance companies are years behind the curve.”
“Decentralised, peer-to-peer insurance, or what’s become known as “crowdsurance”. […] Lemonade, arguably the best funded of today’s crowdsurance startups.”
Dynamic pricing: “price auto insurance according to driving habits rather than driving history.” Think sensors, car usage rates, good driving trends, low-risk driving times. Same in home insurance: “this kind of AI-driven, sensor-laden insurance is “pay-as-you-live” morphing the traditional “detect and repair” role of an insurance company into “predict and prevent”.
Other financial services trends and example are “technically a mobile wallet, Good Money lives on your phone and holds both regular and crypto currencies. […] you get equity share in return. […] Good Money is targeting people who prefer value-driven companies [and] the unbanked. “ M-Pesa in Kenya, bKash in Bangladesh, Alipay in China are other examples. R3 and Ripple in the developing world are using blockchain to replace the SWIFT network.
Fun fact: “by matching customers who have, say, used pesos they want to turn into dollars with customers who want to change dollars into pesos, TransferWise is using a modified dating app. “
Prosper, FundingCircle, LendingTree are known examples of “crowdlending”. SmartFinanceGroup created in 2013 to serve China’s massive unbanked and underbanked population is another example. They “comb a user’s personal data […] to generate a reliable credit score. “
Robo-advisors like Wealthfront and Betterment are bringing wealth management to the masses.
Towards the end of the book, I was glad to read that telepathy is now possible! “Back in 2014, a team of Harvard researchers sent words from mind to mind via the internet. Technically known as “brain-to-brain communication,” this was an example of the long-distance version – with one subject in France, the other in India. The researchers used a wireless, internet-connected EEG headset as their transceiver and a transcranial magnetic stimulator – which sends weak magnetic pulses into the brain – as a receiver. The subjects didn’t exactly get thoughts, but rather could accurately read flashes of light that corresponded to the message.”
The last words are just perfect to start 2021: “Take a deep breath and don’t blink because, ready or not, here comes tomorrow.”
I didn’t anticipate that the chapter on the Future of Insurance would be so fertile. And it makes total sense to connect this with HBR’s Exponential View’s episode From insurance giant to tech platform: the story of Ping An. A fantastic interview with Jonathan Larsen, Chief Innovation Officer. It was illuminating to hear how having a 5-year horizon is really enabling innovation (not driven by innovation people but by the leadership, a mindset).
All these emerging and converging technologies made me think of this TED Talk by Polina Anikeeva – Why You Shouldn’t Upload Your Brain To A Computer. It is a challenge of biology, chemistry, material science and how to bridge the brain-machine interface as our generalist brain is soft, squishy, 3D human computing hardware and the electronics powering artificial computing and AI hardware so far are hard, rigid, sharp and flat specialised machine.
The future is a brain-inspired interface that matches the resolution of individual synapses and can communicate with neurons across all the natural languages, neurotransmitters, electric fields, mechanical forces, changes in pH and temperatures and can communicate back to the artificial hardware. Her lab is working on developing nano transducers, mixed in IV solution and injected into our brain like a drug, where they can receive signals from outside and convert them into one of neuronal languages (eg. heat). This is limited to few thousand neurons (far from trillions of synapses). The opportunity is to take advantage of AI as a specialist plug-in into our generalist brain to make it a diverse generalist. It requires a paradigm shift from machine inspired electronics to biology driven design of new materials and architectures.
No wonder that 10 years after Software is eating the world, A16Z bet is on biology – “bio today is where information technology was 50 years ago: on the precipice of touching all of our lives. Just like software—and because of it—biology will one day become part of every industry.” Their manifesto as of 2019 is Biology is eating the world.
Another revealing TED Talk about our brains, given how much is spent on emotion-detection systems: You aren’t at the mercy of your emotions — your brain creates them | Lisa Feldman Barrett
And guess what… examining every brain imaging study on emotion published in the past 20 years, Lisa Feldman Barrett discovered that our brains are not hardwired with emotion circuits. Emotions are “guesses that your brain constructs in the moment where billions of brain cells are working together” and “physical movements have no intrinsic emotional meaning. We have to make them meaningful. A human or something else has to connect them to the context and that makes them meaningful.”
And taking about emotions being made, not hardwired, I finally took this test I heard about ealier this year on Eat, Sleep, Work, Repeat podcast as it supposedly test “social intelligence”. I scored 29 / 36 which is equal or better than 74% of all participants. I’m doubtful about how the research team assigned 1 emotion to the 36 pair of eyes that were shown with 4 plausible “correct” emotions tagged to them. Most of the time I either thought the emotion was something completely different from the 4 options or made a guess by elimination. And I can’t believe it is universal, we project or read emotions differently. Here’s what the post-test details about the test had to say:
- This test was developed in Great Britain and the images you saw were taken from British magazines in 1990’s. Unsurprisingly, the test doesn’t work perfectly for people who are not native speakers of English or for people who come from cultures that are very different from Britain’s.
- Typical results. In the original experiments with this test, the average score for British adults was 26. The average result for students was 28. However, individual results ranged from 17 to 35 as many factors may affect performance: the lighting, the quality of your screen, your emotional state, fatigue, not to mention knowledge of the English language.
- Social intelligence and team-based problem solving. Recent research published in Science in 2010 demonstrated that there is a link between how well team members perform on this test and how well the team performs on complex problem solving tasks. In fact, the overall “social intelligence” (or “collective intelligence” as it is referred to in the paper) was more than five times more important to the team success than the average IQ of the team members!
- Besides this test, there were two other factors that were found to be important for team success: how equally team members contributed to the conversations (teams where one person dominated the conversation performed less well than those where all members contributed roughly the same), and the number of women on the team (yes, the more women, the better the team did! Sorry guys…).
- If you want to hear this from the horse’s mouth, here is the paper: Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science, 330(6004), 686-688.
- Reading the Mind in the Eyes and Autism. This test was originally developed by prof. Simon Baron-Cohen at the University of Cambridge as part of his and his team’s research on autism. Adults with Asperger Syndrome or high-functioning autism scored 22 on average on this test. Again, large individual differences were observed. There is big overlap between the results of typical adults and adults with Asperger Syndrome.