Disrupt the Disruption — The Tech Oligopoly Part 2

In the second part of this series, we analyze and shed light on the tools and tactics that tighten the oligopoly’s control on the AI ecosystem

Hobbes’ Leviathan- The Political State is in retreat as a new Leviathan emerges

Cornered in Clouds — Understanding our present tech landscape

In the valley that prides in birthing king slayers, what if the kings can no longer be slain?

“How do you compete with a giant that doesn’t want to make money?” — Mickey Drexler

Sitting at the top of the tech food chain, the five most valuable companies in the world: Apple, Amazon, Alphabet, Microsoft, and Facebook, have come to form the Tech Oligopoly. The members of this tech oligopoly are the largest lobbyists and political donors in Washington. They have more financial resources than several nation states. Their products and services have transformed our society. They have network effects that provide them with an enormous competitive edge and allow them to operate at a scale that startups cannot. They are actively improving their strategic advantages through research and acquisitions (DeepMind, LinkedIn, Siri, Github, Instagram, YouTube, Whatsapp, Android). If they are unable to buy a startup, they copy all of its innovations. They have venture capital arms through which they secure equity in many successful startups. Consider Amazon’s strategy to reinvest in its business and sacrifice profits for the long-term, fundamentally reinventing the game as a loss leader.

Their anti-competitive behaviors have brought them billions in fines and as Peter Thiel would remark,

There are exactly 2 kinds of businesses in this world. Businesses that are perfectly competitive and businesses that are monopolies.

As the world becomes increasingly digital, members of the tech oligopoly have built an ecosystem that is becoming increasingly crucial for other companies: server clouds, ad networks, app stores, operating systems, social platforms, etc. Thanks to this “necessary” ecosystem, made possible primarily by the rise of cloud computing, almost all successful startups pay some member of the tech oligopoly just for existing.

The success of “dependent” companies that rely on this ecosystem adds to the dominance of the Tech Oligopoly members.

Capturing and extracting value through such an ecosystem means that the technological success of other companies adds to the dominance of the tech oligopoly.

For example, Snap Inc., the tech company behind Snapchat, signed a contract of $2 Billion in 2017 with Google to use its cloud services — in this time it will also be competing with Google and the other giants for digital ad revenue. Similarly, Netflix hosts all of its content on Amazon Web Services while it competes against Amazon in creating new content. When it comes to apps, Apple and Google take a 30% commission. This commission generated $11.5 Billion in revenue for Apple in 2017.

As we transition to the AI economy, these permutations and inter-relationships are going to become increasingly complex and as Joshua Cooper Ramo argues in The Seventh Sense, our connection to these services is going to fundamentally impact our identity:

“What is true for the machines all around us now is true for us too: We are what we are connected to. And mastery of that connection turns out to be the modern version of Napoleon’s coup d’oeil, the essential skill of the age.- Joshua Cooper Ramo

The AI-First Future

“Computing is evolving again. . . . In an AI-first world, we are rethinking all our products and applying machine learning and AI to solve user problems.” — Sundar Pichai

Artificial intelligence will be the crown jewel of the ecosystem being created by the oligopoly, and will further increase the dependency of other companies on the big five.

According to a PwC study, “45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand.” Their research shows that artificial intelligence will lead to a greater variety of products, which will have increased personalization, attractiveness and affordability over time.

A careful examination of the recent initiatives, acquisitions, and policies of the oligopoly reveals a directed push toward an “AI-First Future.” The tech giants have already begun to add AI services to their cloud-based offerings, and have started AI-related initiatives across a wide range of industries.

Industries where the Tech Oligopoly is building an AI-first Future

In the Amazon Era, growth is all that matters. With the rise of artificial intelligence, the rent-extracting necessary ecosystem will only add more dependents for the giants to feed upon. As Satya Nadella, the CEO of Microsoft put it: “It’s going to be A.I. at the edge, A.I. in the cloud, A.I. as part of SaaS applications, A.I. as part of in fact even infrastructure.”

As the members of the tech oligopoly promise to democratize AI, create an AI-First future, and launch a partnership on AI to benefit society, we have to question whether they can prioritize societal benefit over their penultimate value of increasing shareholder value.

Gatekeeping Open Source

“The second you try to take Android and do something that Google doesn’t approve of, it will bring the world crashing down upon you.” — Google’s Iron Grip on Android

As we discussed in the first part of this series, developers and researchers form the backbone of AI. The Achilles heel of big tech firms is the shortage of appropriately educated and trained talent in this domain.

In this context, open source becomes one more tool for big tech to establish hegemony over developers’ minds. Matt Assay, the Head of Developer Ecosystem at Adobe, sees Google’s decision to open source TensorFlow as a strategic decision to create sources of future revenue. Google’s strategy, according to another analyst, is to build familiarity with machine learning and then encourage developers to run their projects on Google Cloud. Tencent’s recent foray into this space is not accidental either.

The software open-sourced by tech titans tends to be open source but closed in terms of strategy and design and development process. The open source code is provided freely to developers, which is a valuable service. However, unlike classic open source software such as Linux, the vast majority of corporate open source AI software is not developed via a transparent and participatory process. Rather it is periodically emitted to the community, via a top-down control and information dynamic. Community comments and requests may be solicited and in some cases attended, but only insofar as agrees with corporate strategy and tactics.

Tech giants are able to imbue their open source software with certain merits not so common in the old style participatory open source world: beautiful graphical visualizers, easy installation and deployment, great documentation and tutorials, optimization for efficient operation on various hardware platforms. These potentials come along with big development budgets and they serve to ease the path of new talent from beginner to intermediate status, and the path from researcher and hacker use to wide commercial adoption. However, they also serve to squelch the development of alternative toolsets that have community-driven development processes but lack comparable funding to add the bells and whistles.

The Theano deep learning library, developed for years by the lab of deep learning pioneer Yoshua Bengio, has been mothballed because Bengio’s team of PhD students and enthusiasts could not keep up with big tech. Bengio’s team excels in algorithmic expertise and conceptual mastery, but for user interfaces and multi-GPU optimization and whizzy tutorial videos, they could not viably keep up with companies worth hundreds of billions of dollars. — Dr. Ben Goertzel, CEO SingularityNET

The result of this rapid infusion of a vast amount of dollars into open source AI software is that, especially in areas like deep learning with well known immediate large-scale commercial applications, the whole sphere becomes dominated by top-down-controlled corporate AI projects. Control passes from the broader community to corporate decision-makers. New developers are naturally motivated to use the open source tools that have the most capabilities and the fewest barriers to entry, and thus as they enter the AI field, they also become trained in the specific tools used by big companies — and channeled toward the particular AI application areas that big companies currently find most commercially valuable.

The open source tools for computer vision are tremendous these days because there is so much commercial value to big tech in having the PhD students and hackers of the world create and refine and test vision algorithms. Open source tools for, say, diagnosing crop disease or doing mathematical theorem proving or computational chemistry — these exist and are powerful, but they’re old-style open source code, more laborious to install, and monitored using log files inspected on the command line, rather than using fancy user interfaces. These domains are highly valuable to humanity and to the progress of AI and other sciences, but nobody with tens of billions to spare stands to rapidly earn yet more billions in these areas. So the tooling remains cruder, which slows down progress for users in some aspects, yet also encourages creativity, as it’s possible for developers on a low budget to effectively compete for users.

While there are certainly benefits to the tech giants’ interest in open source software, it is unlikely they will ever fully embrace the historical ideals of the open-source movement. Those who run corporations with a prime directive to maximize shareholder value are prohibited by their fiduciary responsibilities from working toward other conflicting goals.

When everything aligns just right, maximization of shareholder value can align with more broadly beneficial goals like decreasing global wealth inequality, increasing access to technological tools and services, or creating ethical AGI. But such alignment never seems to happen consistently, and when a conflict occurs, in a conventional corporate context, shareholder value has to win.

Five Hungry Ghosts

“We believe that [cloud market] share will continue to consolidate around the largest platforms into an oligopolistic market structure.” — Goldman Sachs

The prime directive of the tech giants only encourages them to seek growth and an increase in revenues at all costs. Therefore, it is naive to think that the tech oligopoly will democratize powerful aspects of AI. Rather, they will have a considerable incentive to utilize the powers of AI to advance their ambitions.

With each passing day, these titans are utilizing AI to enhance their services and products further, making them more personalized and increasing their attractiveness. Apple is enhancing its HomePod and iPhone X with distinct AI functionalities, Amazon’s Alexa has spurred others to offer AI-based personal assistants and Google’s Duplex created a sensation and spurred ethics debates. And these are merely the most visible manifestations of a much deeper trend; these same companies also leverage AI behind the scenes in countless ways, from Google Deep Mind’s AI for optimizing power usage in server farms to Amazon and Alibaba’s use of robots in their warehouses, and so much more.

Even the Global500 cannot keep up with the Tech Oligopoly when looking at Future Readiness and AI Focus

The very nature of AI, the mind-boggling value that it will create, means that it is a technology that no company can afford to ignore. As the above graphic highlights, the giants focus on AI has elevated them in a very favorable position to be even more dominant in the future.

As the kings of the tech industry monopolize AI talent, other companies who seek to benefit from AI have two options: either they can utilize the AI services being offered by the big five or they can hire AI developers and utilize their talent to enhance their own competitiveness.

As we discussed earlier, hiring AI talent involves competing with the members of the tech oligopoly. That is a war that most companies cannot afford to win. The only other viable option will be to utilize the AI services being offered by the tech titans.

As the ambitions of the tech oligopoly members grow and they strategically enter new industries (Whole Foods), companies without AI talent will discover that in order to function effectively in the intelligence economy, they have to feed the tech giants and compete against them at the same time.

What’s Next?

The immense potential of AI means that it can either increase the inequalities of our societies or liberate us from numerous sufferings.

How can open access networks incentivize talent, design democratic processes and build community-driven AI initiatives that provide viable alternatives to society? Can we partner with the better angels of the oligopoly to incentivize positive social outcomes? Can our friends and partners execute on privacy first designs that enable the sovereign individual? What risks and pitfalls lie before us as we build towards a democratic future?

We will cover this and more in the final part of our series.

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