THE LULL IN BIG TECH IDE
We wanted flying cars, instead we got 140 characters” - Peter Thiel
This may seem like an odd time to question the ingenuity of the technology sector.
With news of artificial intelligence (AI), automation and machine-learning swirling around, the technology space looks like a bright spot in a dull economy. Mega venture funds and frothy startup valuations seem to bear out this optimism.
Yet, fifty years ago we imagined a very different future. Stanley Kubrick’s classic 2001: A Space Odyssey released during the heyday of the space programme assumed that by the early part of this millennium, space travel would be routine; machines would be terrifyingly intelligent; and computers and humans would have intelligent conversation.
By that yardstick, the present is decidedly underwhelming. Our phones have become smarter, interfaces slicker, and communication faster. But other predictions haven’t come to pass.
Most space programmes are limited to unmanned expeditions. After the lull of the past few decades, space travel is in the news again.
But even Elon Musk, the eternally confident founder of Space X, expects to send a manned expedition to the moon only in 2023, the first lunar journey by humans since 1972.
Driverless vehicles are not ready to replace humans yet. The vagaries of human traffic are just too much for these ordered systems. Robots, which are used extensively in manufacturing and distribution, haven’t been able to adapt to routine human tasks.
Automated assistants such as Siri are great for one-off tasks, but it’s near impossible to hold a conversation, especially with a thick accent.
Has the tech sector fallen short or were our collective expectations unrealistic?
Dog walking, not cars flying
There are around 290 unicorns, or unlisted startups valued at more than $1 billion, across the world. In theory, these unicorns represent the best of our ideas. Investors seem to agree, ploughing over $980 billion into these companies.
But an analysis of the companies shows startups working on truly innovative problems constitute less than one-tenth of these companies ( see chart 1).
Most of the funds (and noise) is soaked up by startups engaged in one of two things. The first, on-demand and e-commerce platforms, find new ways to order things without leaving your couch.
The other class of startups, social media and entertainment firms are focused on designing content that keeps you in that couch for as long as possible.
Take on-demand firms. Uber has improved the taxi-hailing experience; Wework has made renting office space easy; and with Swiggy, it’s nice not to have to walk to the restaurant for food.
But we are still riding in the same taxi, Big ideas are becoming harder to find. Since more years are being invested into finding the next big idea, the average age of a new entrepreneur is shifting. 20-34 35-44 45-54 55-64 working in the same office, and eating from the same restaurant as before.
The order-to-fulfilment process has been streamlined and the supply more closely matches demand. But this isn’t innovation that has fundamentally altered how we live, work, or play. It’s just made it incrementally better.
The desire to disrupt any activity with on-demand or online substitutes can sometimes veer into the absurd. Early last year, Softbank’s Vision Fund invested $300 million into Wag, a startup that lets you book a dog walker. (The Vision Fund’s stated goal is to “invest in businesses and foundational platforms that will enable the next age of innovation”.)
With social media companies, the innovation argument is even weaker. Facebook and Google evangelized the cult of connectivity. But after initial productivity gains from improved communication, watching YouTube videos, sharing Google photos or browsing Facebook are not growth-accelerating. On the contrary, the fatigue induced by aimless scrolling on Twitter or Whatsapp are a drain on productivity.
For an industry that fetishizes disruption and innovation, this lack of creativity is disappointing. One reason for this state of affairs could be financial. The computer scientist Jaron Lanier has argued that the monetization model driving the internet has created perverse incentives for tech firms.
From the early days of the internet, we assumed most content and services should be free. But the desire to democratize access to information came at a cost —the advertising-supported business model. The largest tech companies today—facebook and Google—rely on advertising for a majority of their revenue. Even Amazon has now got into the game, by selling pixels on its site to eager sellers. In the push to generate more advertising revenue, these companies need to continually find ways to keep users addicted (or engaged) to their platform. And as users have become smarter, the algorithms have become even more intricate. Today, some of the largest employers of AI and machinelearning talent are social media platforms. ByteDance, the creator of Tik Tok and the most valuable startup on the planet, employs legions of AI and machinelearning engineers. Tik Tok does a great job of curating and customizing content, based on what’s most likely to appeal to a user. But peel away the glamour and the product is still a social network for amateur music videos. A generation of our best engineers are spending their productive output on building behavioural nudges that manipulate