The 2021 Crystal Ball for Emerging Tech

Five trends experts think will define the year ahead
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Francis Scialabba

· 8 min read

In 2020, emerging tech stepped up to the plate in a whole host of ways: further enabling online shopping, bolstering warehouse automation, improving last-mile delivery of supplies, tracing viral infection...the list goes on.

As we eagerly—no, frantically—draw the curtains on this year, we’re looking ahead to 2021. And after chatting with economists, experts at career search platforms, venture capitalists, and market analysts, we zeroed in on the top emerging tech predictions they’re watching for next year and beyond.

“The year of the self-driving SPAC”

For electric vehicles, the past year has brought not only increased buzz, but also sweeping decisions: GM upped its commitment to an all-electric future, Toyota announced its first all-electric vehicle, and thanks to Nikola, QuantumScape, and Arrival—among others—we all learned what a special purpose acquisition company (SPAC) is.

Asad Hussain, PitchBook’s lead mobility analyst, says battery electric growth won’t stop anytime soon—but he believes that 2021 will be “the year of the self-driving SPAC.”

  • SPACs are an attractive option for the AV sector for the same reasons as the EV sector: Capital-intensive startups without much (if any) revenue typically need cash quickly, and SPACs provide that.

Early signs include: Major enthusiasm around lidar startups. Innoviz recently announced a $1.4 billion SPAC merger. Velodyne’s stock jumped after its smallest sensor won a Silicon Valley Robotics award. And Luminar’s stock price has been “stratospheric,” says Hussain.

Digital health = golden goose

One of 2020’s biggest emerging tech bright spots is digital health. It’s shaping up to be the largest-ever funding year for the sector, thanks in part to an influx of high-dollar deals. Chances are that’ll continue well into 2021, according to Burke.

Big $$: For the global enterprise health/wellness tech industry, the median VC deal size through Q3 2020 is $39 million—up from $25 million in 2019 and $18.9 million in 2018, signaling “industry maturity,” according to Kaia Colban, an emerging tech analyst at PitchBook. Q3 was also the largest quarterly deal value in the past five years.

It’s been a breakout year for AI in healthcare in particular: The pandemic drove AI-assisted disease research and drug discovery, as companies focused on studying Covid-19 or helping to develop a vaccine.

  • Clinical decision support—the use of AI in diagnosing diseases—was the most active category of AI (in terms of number of deals) in Q3 2020, according to PitchBook data.
  • “We think that the success that AI algorithms are having in diagnosing certain diseases is starting to make the adoption of those technologies feasible, which hasn’t been the case historically,” says Brendan Burke, a senior emerging technology analyst at PitchBook.

On Upwork this year, Mike Paylor, the platform’s VP of engineering and product, says he’s seen a lot more demand for healthcare apps and, in particular, AI for healthcare.

But these are high-stakes tools, many of which exhibit concerning biases. And it's important to hire diverse teams of people who can identify biases and think critically about how to address them.

  • Demand will likely rise for “equity engineers,” or people who can work with AI tools to help remove biases and make the systems “much more representative of the world around them,” says Peter Barrett, founder and general partner at venture fund Playground Global.

+ While we’re here: Another big win that should help propel the sector forward in 2021? DeepMind’s breakthrough in protein folding, a 50-year-old computational biology mystery with big implications for disease research.

Natural language processing will reach new heights

In the AI sector, natural language processing (NLP)—which helps computers understand human language—rose from the least-funded sub-category in 2018 to the third-highest-funded in 2019.

Some ways the sector made the headlines this year? Top AI ethics researcher Timnit Gebru was recently fired by Google over her research on potential bias in large NLP models. This past summer, OpenAI released GPT-3—considered the most advanced language model ever created—and Microsoft licensed it. And a serious boost in funding helped NLP startups turn text prediction and processing tasks (think: email composition, chatbots, and code completion) into business models.

Now, NLP is set up to potentially become the highest-funded category in 2021, according to PitchBook data.

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The jobs angle: Paylor has seen a rise in demand on Upwork for Python skills, a coding language that’s often associated with AI and data pipelines (and, therefore, NLP).

“Natural language processing has so many different use cases for businesses, from chatbots to analyzing communications to even things like automated responses or automated article creation,” says Paylor. “Businesses are going to look to try to leverage those services...across many areas.”

Last-mile delivery tech will surge

Whether you're a Luddite or an early adopter, chances are you're doing more of your shopping online these days. That shift has broadened the market in a big way.

  • Delivery companies are growing quickly, M&A is up, and, controversially, “the regulatory overhang [for] these stocks has been lessened by Prop 22,” says Hussain.
  • California’s Proposition 22—which passed in November after extensive lobbying from Uber, Lyft, and DoorDash—exempts gig economy companies from providing employee benefits to workers.

Some of 2020’s biggest headlines: Uber officially acquired Postmates earlier this month, DoorDash went public last week, and Instacart’s IPO could come as soon as Q1 2021. Virtually all of the space’s leaders have moved beyond solely food delivery and into areas like convenience and retail.

That's led to an even hotter market for last-mile delivery tech: This year, electric vehicle startups Rivian and Arrival partnered with Amazon and UPS, respectively, on future fleets of electric delivery vans. Amazon and Walmart’s delivery drone battle entered a new phase. And shipping giants like FedEx are rolling out autonomous same-day delivery bots.

Bottom line: The uptick in investment—and in demand—will likely continue into 2021 and spark new competition, increased hiring, and more bets on delivery tech like fleet management software and autonomous delivery robots.

AI and ML tools will become more democratized

In 2020, businesses have access to more data than ever on their customers, competitors, and the market as a whole. AI tools are the obvious choice for sorting through it all, but how are companies handling integration of the tech?

  • A recent McKinsey survey found that half of organizations worldwide have adopted AI in at least one function.
  • But another global survey found that less than half of adopters say they’re highly skilled at integrating AI into their existing environments.

What will bridge the divide? A combination of more hiring for specialist roles and the creation of more standardized, user-friendly AI tools.

In 2021, experts told us, we can expect demand for data engineers and others who can help integrate AI and ML tools into a business’s existing infrastructure.

  • “Small- and medium-sized businesses alike need to bring on the right skilled professionals to help integrate the right tools and systems [for AI],” says Paylor. “There are great ways to use that, ‘out-of-the-box,’ but you need to know how to leverage it.”

And when it comes to more user-friendly AI tools, the fast-growing field of ML operations (MLOps) is a good case study. To most businesses using AI, MLOps tools are as essential as Wi-Fi is to you while working from home. They’re typically the bread and butter of training, deploying, and running AI models.

Right now, those services are offered by “hyper-scalers” like Amazon, Google, and Microsoft, which offer MLOps tools that integrate with their existing cloud environments—but they’re typically confusing for non-specialists. So startups like DataRobot and H2O.ai created a straightforward way for non-specialists and citizen data scientists to build ML models.

  • If hyper-scalers want to compete, they’ll “either need to release new features aimed at citizen developers or acquire startups that have developed a competitive user experience,” says Burke.

Bottom line: “If a certain kind of tool is one that can only be used by a very, very small, elite group of ‘grandmaster’...craftspeople,” says Guy Berger, PhD, principal economist at LinkedIn, “those tools can have very limited capacity to change anything.”

That’s why we’re seeing these technologies shift to be more “standardized” and “out-of-the-box,” he says—so that non-specialists can potentially start to use them. That, he says, is “what really matters here.”

Stay up to date on emerging tech

Drones, automation, AI, and more. The technologies that will shape the future of business, all in one newsletter.