We worked hard this year to help tease apart what was hype/ what was real when it came to the buzz and noise around tech trends in the news this year (!) on our podcast show “16 Minutes.” To do so, we went beyond the headlines, shared first-person analysis directly from experts, and also often shared frameworks for putting tech trends in context—and all in 16ish minutes or so (along with occasional 2x explainer episodes;).
But the show also goes well beyond the news cycle into the longer arc of various tech trends, since innovation plays out in phases: Discussing which are leaps, and which were incremental advances, is a big part of what we discuss on the show. And this year, we covered a veritable ABCs of tech trends beyond the coronavirus pandemic—from algorithms and AI in bio to CRISPR and digital health; from gaming and GPUs (and semiconductors) to healthcare rules and identity online to section 230 and oh so much more.
So, consider this list of top 16 episodes of 16 Minutes your curated summary of 2020 tech trends-in-the-news, explained. But also consider it your primer for understanding the future: where we’ve been, where we are, and where we’re going… And if you haven’t already, be sure to subscribe to “16 Minutes from a16z” wherever you get your podcasts!
While much of the news covered the political dance between the various players around divesting the popular video-sharing app TikTok in the U.S. (given security concerns and China), we covered the key algorithm that powers the app — as well as the trends of “creativity network effects”, “algorithm friendly design”, and where we are on the future of video.
This summer, Open AI released private access to its API, which included some of the technical achievements behind GPT-3, the pre-trained machine-learning model optimized for a variety of natural language processing tasks. The viral sharing of resulting screenshots was soon described as “TikTok videos for nerds”. So why the excitement? How do we know how good it is or isn’t; is it ready for practical use; and how do we know the difference between “looks like” a toy and “is” a toy (especially given that many innovations may start out so). And where are we, really, with artificial general intelligence here?
There’s no question that there was a “pandemic effect” when it came to gaming this year, but people who hadn’t played games before (or were coming back to games after years) is part of what contributed to the online multiplayer game Among Us becoming a viral phenomenon. But that alone does not news make; a major politician livestream-played it, too, further mainstreaming such gaming… and livestreaming itself contributed to the who, how, and why now for a game that suddenly became very popular but has actually been around since 2018. What does it mean for the future of social?
The Centers for Medicare & Medicaid Services (CMS) issued their final rules to help increase price transparency in (what’s been described by the U.S. Department of Health and Human Services’ secretary as) a “shadowy system where prices are hidden”. So we covered the specifics of, and the impact of, the rules on consumers and on various industry players — hospitals, group health plans, health insurance issuers — as well as the gap (and opportunities) between mandate and implementation. The rules go into effect January 2021… but the deadlines go through 2024. Will we finally have price transparency, and be able to compare and “shop” for healthcare?
Going beyond the headlines of the potential merger, we focused on the longer history of computing innovation — and deeper industry questions and tech trends — behind Nvidia’s (maker of GPUs among other things) announcement of its intent to acquire Arm (provider of silicon IP for system-on-chips inside billions of devices). Questions such as: “what about channel conflict” and where is the value in the hardware/software/firmware stack? Wherefore semiconductors, Intel, and Apple — especially given trends such as cloud-native, mobile-first, “ML inside”, and edge computing?
The news of this merger was considered a big deal not just because of the public markets dollars involved, but because it’s aimed at creating “the first true health tech giant“, an end-to-end digital health platform at scale. However, the scale and opportunity in healthcare is so massive that this is just a drop in the bucket of what’s out there. So where are we, really, when it comes to the broader category here; what’s the taxonomy of key trends and shifts involved such as telehealth/ telemedicine, remote patient monitoring, and virtual care?
This year, the FDA approved the first-ever videogame to be legally marketed and prescribed as a medicine — specifically, for 8-12 year olds with Attention Deficit Hyperactivity Disorder or ADHD. So where does that fit in the broader category of “digital therapeutics” — which have proven to be effective as therapeutics — and what IS a digital therapeutic, really? What are the implications for value-based pricing, regulation, and where does real-world evidence come in? [see also related episode on Journal Club for more on the clinical trial data here: “Therepeutic Videogame on Trial“]
Messaging games built on HTML5 and “mini programs” or apps-within-apps merge the key trends of mobile, social, and cloud gaming on phones. Could Snapchat therefore be a serious contender in the “cloud gaming” wars, coming at it from the low end of the market, as in classic disruption theory? From social-first to identity and expression to AR filters and more, how could Snap Minis and the like extend gaming modes and monetization?
App Clips — small, lightweight, fast, parts of a full app that can quickly execute just one specific action for users in context, when and where they need them — and App Clip Codes — stickers that encode a URL and incorporate an NFC tag so the code can be scanned by camera, much like QR codes — are part of a growing trend. Besides Apple’s announcement this summer, other examples include Snap Minis, announced at Snap’s recent Partner Summit [above]; Google’s Instant Apps (2018); and We Chat’s Mini Programs in China (2017). Such mini-apps are sort of like bookmarks or shortcuts to digital destinations dropped all over our physical world, connecting online to offline through smartphone. So what are the use cases for businesses and brands big and small here — and what are the broader implications for discovery, super apps, and the future of context-aware computing?
The 2020 Nobel Prize in Chemistry was awarded to scientists Emmanuelle Charpentier and Jennifer Doudna (also an a16z co-founder, of Scribe Therapeutics), for the development of the CRISPR/Cas9 method for genome editing. Described by them as a technology that’s “had a revolutionary impact on the life sciences, contributing to new cancer therapies, and may make the dream of curing inherited diseases come true”, it all happened in less than a decade from discovery to practice… how? And while many describe this technology as “genetic scissors” — one of the sharpest tools — is that analogy too limited for describing the true power and potential of CRISPR as a gene-editing platform?
There’s already plenty of analysis out there on performance, benchmarks, and more of Apple Silicon and devices, so we covered the big picture behind the company moving away from Intel chips to their own chips (that run on the Arm instruction set). We take a look backwards, and forwards, from this point in time on the long arc of innovation: Beginning first with a quick history of Apple chips spanning decades, and then going into the implications for consumers, developers, future device form-factors, and the industry as a whole. Is it the end of a long story… or the beginning of a new one?
What’s hype/ what’s real beyond the headlines and beyond the press releases, when it came to the first of many vaccine progress announcements — in this case from Pfizer and BioNTech — that their vaccine candidate was found to be more than 90% effective in preventing COVID-19? What’s the significance of the readout and case numbers? How do we put this (and related approaches, like Moderna’s) in context of all the other programs in development? We broke down the math, the science, and the practical considerations… as well as the past and present future of vaccines.
The rhetoric behind Section 230 of the 1996 Communications Decency Act (1996) has been in the news a lot in 2020. So to make sense of the technology and policy aspects of it — and where the First Amendment, content moderation, and more come in — we covered precisely what it does and doesn’t do; the role of agencies like the FCC; and specific nuances and exceptions. And how do we tease apart fact from fiction in common rhetorical arguments such as “platform vs. publisher”, “like a utility/ phone company”, “public forum/square” and others?
Is always-on, (relatively) low-cost, passive monitoring for fitness wearables for consumers really, finally the wedge into data for clinical applications as well? What features — cost, efficacy, battery power, convenience, data, business model — do and don’t matter when it comes to filling in the gaps between the doctor’s office and our mobile selves, families, and home care? We take an, ahem, “pulse check” on where we are when it comes to the idea of the “doctor’s office on a wrist” given latest news around Apple watch, and the overall trends of wearables… including data and privacy considerations.
Earlier this year, the company — which faced unprecedented growth during the pandemic — had to address several security issues and concerns, from home-grown encryption to having engineers in China. How worried should/ shouldn’t we be given that everything from cycling classes to children’s classes are now online through such remote communication tools? And what does it mean for related tech trends in bottom-up SaaS — from ease of use and user onboarding to pricing & packaging to open source, multimedia, and cloud security?
The AlphaFold deep learning system for predicting the 3-D structure of proteins outperformed 100 teams across 20 countries in the 14th Community Wide Assessment on the CASP (Critical Assessment of Structure Prediction) challenge this year. The biennial challenge tracks progress, key metrics, and state-of-the-art on predictive techniques for protein folding, but it isn’t just an academic exercise: Because proteins define and power ALL life functions, figuring out the shapes that proteins assemble into is important in helping determine their functions and therefore potential applications (drug discovery, among other things). So is the grand protein folding problem really solved? Will it really revolutionize drug discovery? What are other applications — and what are the implications for molecular biologists, computer scientists; for big companies, startups; for open science?