THE BIG NINE: How the Tech Titans and Their Thinking Machines Could Warp Humanity, by Amy Webb


Amy Webb cuts a broad intellectual swath in her sometimes sympathetic, sometimes brutally honest assessment of America’s path towards AI innovation, sharply contrasting with its counterparts in China.

Amy Webb is a quantitative futurist whose research focus is on artificial intelligence and how emerging technologies will transform the way we live, work, and govern. She is the author of The Signals Are Talking: Why Today’s Fringe Is Tomorrow’s Mainstream, a professor of strategic foresight at the NYU Stern School of Business, and the founder of the Future Today Institute, a leading future forecasting firm that helps leaders and their organizations prepare for complex futures. FTI advises C-suite of Fortune 500 and Global 1000 companies, as well as three-star generals and admirals, White House leadership and the leadership of foreign governments, large nonprofits, universities and startups around the world.

Prof. Jonathan Zittrain (George Bemis Professor of International Law and professor of Computer Science) at Harvard University puts it effectively – “Webb’s assessments are based on analyses of patent filings, policy briefings, interviews and other sources. Webb sets sensationalism aside in favor of careful arguments, deep historical context, and a frightening degree of plausibility.”

The author begins with a surprisingly insightful assessment current state of AI research within the companies. She takes a tour of the historical backdrop (unknown to many outside of AI circles and academics) making stops at key milestones such as the legendary Dartmouth workshop of 1955 that sparked a frenzy of both practical and theoretical work on artificial intelligence; as well as its traditional evolutionary paths covering Turing’s Imitation Game and the ELIZA program.

Here Are Some Profound Thoughts Covered In Her Book.



The author gives the eponymous “Big Nine” a name – the G-MAFIA and the BAT. These nine companies divided into two groups have the power to shape humanity’s future: Google, Microsoft, Amazon, Facebook, IBM, and Apple in the U.S.; and Baidu, Alibaba, and Tencent in China.
These companies that are building the custom frameworks, the custom Silicon, it’s their algorithms. It’s their patents. They have the lion share of patents in this space. They’re able to attract the top talent. They have the best partnerships with the best universities. Essentially, it’s these nine companies who are building the rules and the systems and the business models for the future of artificial intelligence.


We usually identify three important nuances/evolutionary capabilities of A.I.

ANI – Artificial Narrow Intelligence
AGI – Artificial Generalized Intelligence
ASI – Artificial Super-Intelligence

As we stand at the threshold of new capabilities in the field, its important to recognize that the biggest hype about uses that A.I. is being put to today, in commonly recognized consumer and industrial use-cases, are merely the first successful outcomes of ANI. As we will soon discover, despite their potent abilities to impact humanity, they are relatively harmless forms of A.I. and are merely an improvement over mere automation of tasks. Examples of competence, not necessarily of intelligence.

Revisiting Human Consciousness: When we use Alexa to find a table at your favorite restaurant you and she are both aware and responsive as you discuss eating, even though Alexa has never felt the texture of a crunchy apple against her teeth, the effervescent prickles of sparkling water against her tongue, or the gooey pull of peanut butter against the roof of her mouth. If one only had a white, two-dimensional outline of an apple, one know what it is–even though the taste, smell, crunch, and all the other data that signals to the brain this is an apple may be missing. Alexa has learnt the same way too. That makes us our learning process quite similar.

The question Webb asks next is – Must her (Alexa’s) machine perception meet all the qualities of human perception for us to accept her way of thinking as an equal mirror to our own?

An oversimplified summary – A deep neural net would be given a basic set of parameters about the data by a person, and then the system would go out and learn on its own by recognizing patterns using many layers of processing. For researchers, the attraction of deep learning is that by design, machines make decisions unpredictably. Its worth exploring Webb’s book to experience the full details. 

Along the way, Webb takes an exciting insider detour through A.I. developments post-AlphaGo widely hyped success at beating world champion Go players. How Google DeepMind’s  next evolutionary A.I. system Zero played games against itself, and actually discovered Go strategies that humans had developed over 1,000 years–which means it had learned to think just like the humans who created it. How it developed creative strategies that no one had ever seen before, suggesting that maybe machines were already thinking in ways that are both unrecognizable and alien to us. How when in December 2017, the DeepMind team published a paper showing that Zero was now “generally” capable of learning – not just Go but other information, that Zero was playing other games, like chess and Shoji. That Zero was learning much faster than before, it managed to develop incomprehensible, superhuman power with less than 24 hours of game play.


A.I. is more pervasive in our lives today than we realize. In theory this is the best outcome one expects from a new technology wave.
Robotic process automation (RPA) enables businesses to automate certain tasks and processes within offices, which lets employees spend time on higher value work. Google’s Duplex bot is designed to make routine phone calls to other people. Amazon uses RPA to sift through resumes before prioritizing top candidates for review. In banking, Blue Prism and Automation Anywhere help staffs process repetitive work. The availability of artificial intelligence tools and frameworks is allowing companies to digitally automate more of their functions.
Productivity bots help teams and individuals operate more productively by automating tasks that are time consuming and mundane for people but perfectly suited to bots. With more than 8 million daily active and 9 million weekly active users, Slack is by far the most popular platform integrating hundreds of productivity bots into the workplace. The Obie bot, an on boarding tool, allows new employees to find answers to simple questions about the company. Scheduling bots like Meekan sync with co workers’ calendars to provide possible meeting times. If you’re trying to reduce the wasted time during a stand up meeting, bots send out a request for an update from team members and push out a report once everyone has responded. Bots like Lunch Train help coordinate team lunches and locations. Mattermost, Trello, Asana and Rocket.Chat are all helping boost productivity. With distributed teams and co working spaces on the rise, automation and productivity tools will continue to move toward the mainstream, cannibalizing traditional office technology like email.

We humans are rapidly losing our awareness just as machines are waking up. We’ve started to pass some major milestones in the technical and geopolitical development of AI, yet with every new advancement, AI becomes more invisible to us. The ways in which our data is being mined and refined is less obvious, while our ability to understand how autonomous systems make decisions grows less transparent.

As The Structures And Systems That Govern Society Come To Rely On A.I., We Will Find That Decisions Being Made On Our Behalf Make Perfect Sense To Machines – Just Not To Us.


The American portion of the Big Nine (Google, Microsoft, Amazon, Facebook, IBM, and Apple) are inventive, innovative, and largely responsible for the biggest advancements in Al. It’s a closed super-network of people with similar interests and backgrounds working within one field who have a controlling influence over our futures. A handful of universities supply the vast majority of the talented individuals who join the ranks of these illustrious companies. They studied together, they shared the same career paths, celebrate the same successes, commiserate over the same failures. They are small homogenous band of researchers, mostly male, alumni from the same universities who have been tasked with engineering the future of artificial intelligence. At this particular moment in time, Google wields the most of that influence among the Big Nine, over AI’s direction.
Humanity is facing an existential crisis in a very literal sense, because no one is addressing a simple question that has been fundamental to AI since its very inception: What happens to society when we transfer power to a system built by a small group of people that is designed to make decisions for everyone? What happens when those decisions are biased toward market forces or an ambitious political entity?.
The homogeneity within A.l. tribes is a problem with the Big Nine, but it doesn’t start there. The problem begins in universities, where AI’s tribes form – the “pipelines” to the Big Nine, as the author puts it.
Tribes get established within concentrated social environments where everyone is sharing a common purpose or goal, using the same language, and working at the same relative intensity. It is where a group of people develops a shared sense of values and purpose. They form in places like military units, medical school rotations, the kitchens of Michelin-starred restaurants, and sororities. They go through trial and error, success and failure, heartbreak and happiness together.

The goal of Amy Webb’s book is to democratize the conversations about artificial intelligence and make the average person smarter about what’s ahead—and to make the real-world future implications of AI tangible and relevant to personally, before it’s too late.

She places blame on Big Tech for producing platforms that are utilized to systematically disadvantage underrepresented groups, such as the algorithms that underlie an increasing number of computer-generated decisions. Examples of AI bias can be found in areas as diverse as hiring and recruiting (bias in resume sorting), credit and banking, and automatic medical diagnostics. She pinpoints where the largest tech companies are falling short in these areas. However, she also cautions, stopping with mere blaming of tech companies for problems of AI implementation may miss the point and root causes of the problem. In failing to train a diverse set of future computer and data scientists, and in privileging only hard analytical and computations skills over a broader set of liberal arts courses in the curriculum, universities are perpetuating structural AI based inequalities.



Amy Webb envisions this as a single unifying ledger that includes all of the data we create as a result of our digital usage (think internet and mobile phones), but it would also include other sources of information: our school and work histories (diplomas, previous and current employers); our legal records (marriages, divorces, arrests); our financial records (home mortgages, credit scores, loans, taxes); travel (countries visited, visas); dating history (online apps); health (electronic health records, genetic screening results, exercise habits); and shopping history (online retailers, in-store coupon use). In China, a PDR would also include all the social credit score data. A.l.s, created by the Big Nine, would both learn from your personal data record and use it to automatically make decisions and provide you with a host.

PDRs don’t yet exist,  there are already signals she contends, that point to a future in which all the myriad sources of our personal data are unified under one record provided and maintained by the Big Nine. In fact, you’re already part of that system, and you’re using a proto-PDR now. It’s your email address. The average person’s email address has been repurposed as a login; their mobile phone number is used to authenticate transactions; and their smartphone is used to locate them in the physical world. If you are a Gmail user, Google-and by extension its Als-knows you better than your spouse or partner. It knows the names and email addresses of everyone you talk to, along with their demographic information (e.g., age, gender, location). Google knows when you tend to open email and under what circumstances. From your email, it knows your travel itineraries, your financial records, and what you buy. If you take photos with your Android phone, it knows the faces of your friends and family members, and it can detect anomalies to make inferences: for example, sudden new pics of the same person might indicate a new girlfriend (or an affair). It knows all of your meetings, doctor appointments, and plans to hit the gym. It knows whether you observe Ramadan or Rosh Hashanah, whether you’re a churchgoer, or whether you practice no religion at all. It knows where you should be on a given Tuesday afternoon, even if you’re somewhere else. It knows what you search for, using your fingers and your voice, and so it knows whether you’re miscarrying for the first time, learning how to make paella, struggling with your sexual identity or gender assignment, considering giving up meat, or looking for a new job. It cross-links all this data, learning from and productizing and monetizing it as it nudges you in predetermined behavioral modes.

In a future scenario, the G-MAFIA are the custodians of AI and of our data, but they own neither. Our PDRs are heritable: we can pass down our data to our children with the ability to set permissions (for full, limited, or zero visibility) on different parts of our records.


According to Amy Webb, In America, the government relies on the G-MAFIA, and since were a market-driven economy with laws and regulations in place to protect businesses, the Valley has a significant amount of leverage. “There is an imbalance of power because the US government been unable to create the networks, databases, and infrastructure in to operate. So it needs the G-MAFIA, For example, Amazon’s gov ment cloud computing business will likely hit $4,6 billion in 2016 while Jeff Bezos’s private space company, Blue Origin, is expected to start supporting NASA and the Pentagon on various missions.”

She does not begrudge the G-MAFIA’s role as successful, profitable companies, nor construes that earning lots of money is in any way negative. The G-MAFIA should not be constrained or regulated in their pursuit of profit as long as they aren’t violating other laws.

However, she believes it is important to remember that humans are in charge of AI’s development and use: “We are literally entangled with it, because it is our data that are being used to train AI systems, to build future applications, and to make millions of decisions on our behalf, both small and significant.” That being said, its not the robots we should fear, but the people in charge of them.

She proposes a Global Alliance on Intelligence Augmentation, or GAIA. The international body would include AI researchers, sociologists, economists, game theorists, futurists, and political scientists from all member countries. GAIA members would reflect socioeconomic, gender, race, religious, political, and sexual diversity. They would agree to facilitate and cooperate on shared AI initiatives and policies, and over time exert enough influence and control that an apocalypse is prevented. GAIA nations should collaborate on frameworks, standards, and best practices for AI.

In order to stress the plausibility of such an idea, she point to a precedent. In the aftermath of World War II, when tensions were still high, hundreds of delegates from all Allied nations gathered together in Bretton Woods, New Hampshire, to build the financial structures that enabled the global economy to move forward. That collaboration was human-centered — it resulted in a future where people and nations could rebuild and seek out prosperity.

The GAIA is not a global inclusion agency. It is a standards body that also does technical inspections and audits on advanced AI.




  • Smartphone sales will start to decline; smart peripherals will see a bump.We expect to see many new kinds of wearables: connected performance clothing, headbands, shoes.

  • We will start to see city-scale projects that harness electric devices (think IoT, but much larger in scope), traditional infrastructure and citizen data.

  • We’ll see the convergence of several game changing technologies, such as AI and genomics, and quantum computing and encryption.

  • We’ll see further consolidation across media and tech.

  • Interfaces won’t just be screens that we look at. In 2019 we’ll begin to see new kinds of interfaces: biophysical, sound wave, light, gesture and of course, voice. We’ll begin to ask questions about the implications of anthropomorphizing AI agents.

For example: How do our experiences change when an AI agent is taller or shorter than us? What if it has a distinct gender? What if it has a deeper or higher voice, relative to our own?

  • We’ll start to reframe conversations around privacy rights in the wake of new connected devices and spatial computing environments.

Examples: Who owns the rights to my face? What if my face gets hacked? Do the walls of an office –– the physical walls –– have the right to privacy? Who should be the gatekeepers of our geo-location data? If the answer is everyday people like you and me, what would be required to make sure we’re not setting ourselves up for continual problems, considering that a lot of people don’t actively update passwords and firmware?

BOOK REVIEW – THE BIG NINE: How the Tech Titans and Their Thinking Machines Could Warp Humanity