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Sadly for these engineers, their view of technology is a fiction. Technologies are not just neutral tools to be applied (or misapplied) by their users. They are artefacts built by people. And these people direct and design their inventions according to their own preferences.
Our phones are designed to fit in men’s hands rather than women’s. Many medicines are less effective on Black and Asian people, because the pharmaceutical industry often develops its treatments for white customers. When we build technology, we might make these systems of power more durable – by encoding them into infrastructure that is more inscrutable and less accountable than humans are.
Over the next 60 years, AI research progressed slowly. The field had many false starts – with seemingly important breakthroughs fostering overinflated expectations, which in turn led to failure and despondency.
But also within that were a series of S curves. Speech Recognition, chess mastery, path finding, OCR, computer vision, automated mapping, knowledge graphs, all graduated from AI to become a computer science tool.
These wide-ranging inventions are known as ‘general purpose technologies’. They may displace other technologies and create the opportunity for a wide variety of complementary products – products and services that can only exist because of this one invention. Throughout history, general purpose technologies (GPTs) have transformed society beyond recognition. Electricity drastically altered the way factories work, and revolutionised our domestic lives. The printing press, which played a key role in the European Reformation and the scientific revolution, was much more than a set of pressure
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As engineers build a product, they come to understand what it takes to build it better. They figure out a more elegant way to connect two components. Or they combine a set of different elements into a single component. Workers figure out shortcuts that make them more efficient. In other words, they learn by doing. As engineers finesse the process, a small innovation here and another innovation there drives rapid increases in efficiency.
This is a distinct concept from the notion of economies of scale – the idea that efficiencies come from having bigger operations, or getting better prices from suppliers. Rather, Wright’s emphasis is on the relationship between demand and skill. As demand for a product grows, the people producing it have to make more of it. And that means more opportunities to learn by doing. As they put what they have learnt into practice, costs get driven down further and further.
the rise of a global market for products is one of the great changes of the last 50 years. The volume of world trade increased 60-fold, from $318 billion to $19,468 billion by 2020.
It would be monumentally stupid for a nation to cut itself off voluntarily from that trade boom.
They are, in short, interoperable. And we all benefit hugely from that interoperability. Standardisation of this kind makes the world more efficient – and allows different innovations to combine. When technologies adhere to standard forms, they can be applied to a wider range of industries. Standard technologies become like Lego blocks that can be selected and assembled into an eclectic array of services. This combination and recombination catalyses further innovation.
On the one hand, there are technologies that develop at an exponential pace – and the companies, institutions and communities that adapt to or harness them. On the other, there are the ideas and norms of the old world. The companies, institutions and communities that can only adapt at an incremental pace. These get left behind – and fast.
What would an exponential government look like? And would a French allow-list, a UK block-list or a USA light-touch approach to regulation keep up best?
In general, if an organisation needs to do something that uses computation, and that task is too expensive today, it probably won’t be too expensive in a couple of years. For companies, this realisation has deep significance. Firms that figured out that the effective price of computation was declining, even if the notional price of what they were buying was staying the same (or even rising), could plan, practise and experiment with the near future in mind. Even if those futuristic activities were expensive now, they would become affordable soon enough. Organisations that understood this
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The bosses at Tesla understood that the prices of electric vehicles might decline on an exponential curve, and launched the electric vehicle revolution. The founders of Impossible Foods understood how the expensive process of precision fermentation (which involves genetically modified microorganisms) would get cheaper and cheaper.
This is how disruption and start-ups should work - let reduced costs be your path to profitability. Network effects are great but fragile in the face of profits and public image.
Stability is an important force within institutions. In fact, it’s built into them.
take the nature of work. When new technologies allow firms and workers to bid on short-term tasks through gig-working platforms, it creates a vibrant market for small jobs – but potentially at the cost of more secure, dependable employment. When workers compete for work on task-sharing platforms, by bidding via mobile apps, what is their employment status? Are they employees, contractors or something else entirely? What rights do they have? Does this process empower them or dehumanise them? Nobody is quite sure: our approach to work was developed in the nineteenth and twentieth centuries. What
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Albert Bartlett, a physicist from the University of Colorado, began lecturing on the limits of population and energy use in September 1969. He observed that ‘the greatest shortcoming of the human race is our inability to understand the exponential function’.
Psychologists who study how people save for the future have identified the ‘exponential growth bias’, which makes us underestimate the future size of something growing at a compounded rate.11 Research in this area shows how people are consistently befuddled by the compound growth of savings, loans and pension plans. If you started investing in your pension a little late, you – like many of us – may have suffered from a persistent bout of exponential growth bias.
Every self-driving car company has missed its targets. It turns out that the problem is much harder, from a purely technical perspective, than the teams building the technologies were willing to acknowledge.
Exponentiality often has unexpected effects. Take chewing gum. In the 10 years from 2007, American chewing gum sales fell 15 per cent – just as 220 million American adults bought their first smartphone. This was no coincidence. When people got into a shop queue, they would once have spent the time browsing the goodies for sale at the counter – and gum was the obvious choice. Now they were spending that time playing with their phones. So gum sales plummeted.
Our inability to make accurate predictions about exponentiality reached its zenith in 2020. When the Covid-19 pandemic got underway, most of us consistently underestimated how rapidly it would spread.
The way fast technology runs ahead of our slow institutions is nothing new. This is arguably one of the key, inevitable consequences of innovation. In the nineteenth century, breakthroughs in industrial machinery catapulted the British economy into a position of global dominance. But there was a hitch. There was a 50-year period where British GDP expanded rapidly but workers’ wages remained the same – something the economic historian Robert Allen calls ‘Engels’ pause’. Those with capital to invest in new machinery did well initially, because it was technology that was driving the growth. It
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One way to make sense of the social problems brought by industrialisation is as a gap – between the speed of technological and social change and the speed of institutional and political adaptation. The state’s failure to regulate working practices during the industrial revolution reflected the preoccupations of an agrarian and aristocratic elite; Britain had a modern economy, but a distinctly pre-modern political order.
Superstar firms get bigger and bigger, dominating one market, then the next.10 Many superstar firms are household names – Apple, Google, Uber, Facebook. They transform the markets they inhabit, turning them into fertile ground for themselves and parched deserts for their competitors.
But once Microsoft had established dominance in the operating system market, it became increasingly hard to take on. There was a powerful network effect: once everybody else was using Windows, and exchanging Word documents and Excel spreadsheets, it became much easier for you to use them too.
Also helped, of course, by the issues WordPerfect had on Windows, which Word didn't have. And the odd compatibility issues that meant that networks couldn't bridge.
Imagine if Standard Oil had merged with Ford, and both used leaded fuel, so Ford cars could only use Standard Oil, and Ford cars ran smoother because of that secret.
Microsoft’s success allowed it to spread inexorably from one market area to the next – first operating systems, then word processors, then spreadsheets.
Which is why monopoly law now considers using monopoly in one market to capture another is uncompetitive behaviour.
Unburdened from the sweaty, crowded, noisy atmosphere of large physical marketplaces, these companies can grow to extraordinary sizes: eBay brings together 185 million active buyers per year;18 Alibaba, 779 million users.
AI is the ultimate intangible asset, because it takes on its own momentum – the algorithms give you more and more value without you having to do very much. The cycle looks like this: You feed data into an AI and it becomes more effective – tailoring a product to your needs, perhaps recommending news stories you want to read or songs you want to listen to. This improved service becomes more desirable, and so more of us use it. As more of us use it, we generate more data about our tastes and preferences. That data can then be fed into the AI, and the product improves.
Take a single, reasonably representative year, 2018–2019. The Chinese tech giant Tencent grew its revenues by 18 per cent; Amazon by 20 per cent; Facebook by 27 per cent – all far quicker than the rate of growth of the global economy.
Netflix has followed a similar pattern. In 2010, Netflix had sales of $2.1 billion and 2,100 employees. Each one of them could account for slightly less than $1 million in revenue. A decade later, the firm had grown more than 10 times in size, with revenues approaching $25 billion. Its 9,400 employees counted, now, for nearly $2.7 million in revenue each.
Of course, the writers, actors and other crew aren't Netflix employees. Like Uber and Amazon, the platform business model passes both costs and risk to buyers and sellers, but keeps the profits.
‘managing becomes redefined as a series of quests for the next technological winner … the Next Big Thing.’
Entrepreneurs who understand blitzscaling, he said, approach their companies very differently to the older titans of industry. Companies that blitzscale emphasise growth over efficiency. Practically, this means throwing out the traditional manager’s rule book of optimising spending. Instead, aim for growth, even if it is expensive to do so. Peter Thiel, one of Hoffman’s co-founders at PayPal, also has an ideology that focuses on growth. In Thiel’s view, ‘competition is for losers’.34 He recommends instead that founders identify markets where they are substantially better than any existing
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The cancer model of business.
What it misses, of course, is that cancer cannot outlive its host. If you're draining resources from society, there won't be any buyers or sellers left.
The Universal Paperclip is not just an AI problem.
Amazon, a retailer, has moved from selling books to peddling virtually every type of tchotchke. It has a logistics wing, the world’s largest cloud computing business, and – like Apple – a media business.
Amazon doesn't have a logistics wing, it has a logistics backbone. The entire ethos of reducing customer friction made that inevitable. Everything else on the retail side depends on that backbone.
Take chip making. For most of the history of the personal computer, chips were made by one specialist firm, the computer by another and its operating system by yet another.
(looks at IBM, Samsung, Motorola). Vertical non-integration was a glitch.
All this confirms that the superstar company is not a fleeting trend but a wholly new economic paradigm. The corporate giants of the future will take increasingly dominant market positions, both within and across sectors. But is this necessarily a problem?
Omni Consumer Products is your friend. Trust OCP.
The first problem is that, instead of exploiting consumers, modern monopolistic businesses might exploit smaller-scale producers.
*might*? I would suggest Cory Doctorow has enough evidence that this is basically inevitable from a platform company.
voiced concerns over Google’s vertical integration in the advertising market, noting that the business was rife with conflicts of interest – and the strong risks of fee increases, given the absence of effective competition.41 Again, however, it’s not the average Google user who loses out – it’s the small business that needs to buy ads.
At their most audacious, tech companies even end up diverting scientists’ attention away from big-picture scientific research. In the 2010s, AI skills were in such demand that the tech giants started to poach university professors to help with their corporate priorities.
there’s a final problem with the tendency towards monopoly. Bork doesn’t mention it much, but companies are not just important to consumers – they are also part of the wider functioning of society. Most obviously, they are supposed to pay tax. And for the first giants of the Exponential Age, our tax codes have been generous. The bulk of the assets for Exponential Age firms reside in intangibles, and intangibles are prone to sliding across borders – and easier to slip past the taxman. According to The Economist, in the years to 2020, the largest American tech firms paid a tax rate of around 16
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For example, one surprisingly popular paper by the legal scholar Lina Khan argues for an alternative antitrust framework – one that accounts for the implications of Amazon’s dominance of infrastructure and logistics.53 Instead of focusing on Amazon’s implications for consumers, we might look to the conflicts of interest that arise from massive scale and infrastructure power.
antitrust bodies need to be more confident about blocking big companies from acquiring smaller ones.
Google paid peanuts for the mobile system Android in 2005. It was a time when the phone market was dominated by a battle between BlackBerry and Nokia – what harm could Google buying a start-up do?
If I posted a short video of my weights routine on Instagram, my friends on LinkedIn should be able to admire it and send me digital plaudits. Interoperability preserves the positive benefits of the network effect: even if I leave one particular social network or other service, I can still access users on the previous service. But it also means that the network effect on any one service – leading a single company inexorably towards monopoly – is curtailed.
we can limit the power of gargantuan Exponential Age firms by treating them less like ordinary companies and more like utilities – the essential services that we can’t avoid, like water, or the electricity network,
General Motors workers in Lordstown, Ohio, set fire to assembly-line controls, forcing production to halt. They were protesting against increasing automation, which they claimed was resulting in layoffs and an inhumane increase in what managers expected of workers.
The snag with the ‘rise of the robots’ narrative is not that it is merely wrong, however. It is also a distraction. We are living through one of the greatest transitions in the history of work. Technology will revolutionise how we all work, and the relationships between employers and employees. And this transition, if handled badly, has the potential to cause a new era of workplace exploitation.
All this means that we’re left with a slightly different picture of our supposedly jobless future. The more that superstar firms like Amazon and Netflix automate, the bigger they grow; the bigger they grow, the more people they employ. There’s an exponential process here, but it doesn’t lead us to employee-free corporations. Where workers do lose their jobs due to automation, it’s not because they themselves are replaced by some piece of software. It’s often because the firms they work for fail.
But sometimes it's the small firms with quality jobs that fail whilst Amazon and its crying booths demand more people on the payroll.
Across an economy, automation might still lead to job losses – albeit with firms like Amazon and Netflix employing increasing numbers of people.
But are the quality of jobs required by Amazon equivalent to the jobs they are replacing or are those jobs themselves mechanistic?
In 2018, the World Economic Forum predicted automation would create 113 million new jobs over the next several years – albeit at the cost of destroying 75 million.
Again, what about the quality of the jobs? Are people happier in corporate or SME jobs? Are people happier in lower autonomy or higher autonomy work?
With the easy tasks that comprised most of his workday now being done by code, that allowed Sid to turn to more complex, and important, tasks – many of which had previously been going unattended.
This is the automation of tedium, but the exponential growth is eating up the "creative jobs" because the consumers of the new tech don't understand what they're consuming, much like the meat eaters who are willfully ignorant of factory farms.
43 In the end, across an economy automation leads to more jobs, not less. Crucially, this dynamic only emerges in the longue durée. The historical record is unambiguous.
Is this necessarily true? Our global population has grown, increasing demand. Without the increase in wages and workers to propel demand, how can we say that the jobs required for supply will increase?
But new technologies tend to put people out of work in the short run, and what economists regard as the short run can be many years.’
And how to governments keep those people fed, housed and available to work in the meantime?
Not a single one of these drivers worked for Uber. They were gig workers;
Again, no discussion on the quality of work and on the precariousness and stress of the gig economy.
