BOOK REVIEW: Empire of AI

Empire of AI -- Karen Hao – 421 pages (482 with notes and index)

Virtually anyone on the planet with access to a computer or smart phone has experienced profound changes in the nature and quality of "content" presented to them stemming from the use of so-called Artificial Intelligence (AI). People working in fields related to "content" production (musicians, graphical artists, videographers, writers, software developers) have already experienced large reductions in hiring opportunities in these career fields as large companies attempt to adopt AI-based processes that can lessen their need for humans in these roles. Others looking at larger issues of intellectual property rights, resource allocation and prioritization, environmental impacts and economic power have been attempting to raise concerns with politicians and regulators to seemingly little avail.

Karen Hao's book Empire of AI was written to address the relationships between these different areas of concern and, in doing so, identify an underlying pattern of government, business and social abuse that provides a more meaningful perspective from which to combat all of the problems posed by Artificial Intelligence. The book is subtitled Dreams and Nightmares in Sam Altman's OpenAI but the saga of OpenAI is not the sole focus of the book nor, as the book makes clear, should it have been. The drama within OpenAI is merely a specific example of a larger trend seen in virtually every large, "successful" technology startup over the past twenty years.

The Book Review First

The mathematics and computer science theory behind AI are obviously extremely complicated and generate an unlimited number of potential tangents into business management, interpersonal communication and typical business posturing and deceit . However, the consequences for society stemming from the rush to be first in monetizing AI capabilities are far broader and involve economics, labor rights, natural resources, politics and – bluntly – existential risks, depending on how blindly humans allow the technology to be put into control of critical infrastructure and decision making related to national security.

I have a background in engineering, large scale software systems, executive level business management and more than a passing interest in economics, politics and public policy. Reading Empire of AI made it clear how carefully the author chose to thread the key themes of the book together and how precisely the points were chosen for switching between topics to highlight inter-dependencies without getting stuck in the weeds or failing to cement a point with the proper level of detail. Enough of the outline of the problem is provided in the opening pages of the book that the content never leaves the reader frustrated that X hasn't been covered yet when you know it needs to be. As the first few pivots in the narrative occur from X to Y to Z, the exposition quickly makes it clear why the narrative needed to swing from X to Y to Z at those points so the reader stops second-guessing the author and the larger narrative just unfolds naturally.

The exact themes covered by the book will be addressed below but the bottom line review of this book is this...

Empire of AI is easily one of the most consequential books published in the past few years. It wasn't written to cash in on the latest trendy technology fad or business success story. It was written by an author who has been reporting in the field for ten years and has consistently looked beyond the business and financial hype to understand all of the underlying effects of the technology, both in its development and application going forward. For professionals working in these fields or individual citizens of any country, Empire of AI provides a thorough understanding of AI and the business models adopted by those developing it. The book also makes it clear how AI is simply the latest technological iteration of an established pattern of colonialism and empire building that facilitates the further concentration of power and wealth to the few at the expense of everyone else.

Buy or borrow this book and read it cover to cover. That's as brief a review as I can muster.

Key Themes in Understanding Artificial Intelligence

Throughout the content of Empire of AI, these key themes are emphasized:

• AI is intrinsically tied to deception and inscrutability.
• Multiple philosophies for creating AI exist but only the most resource-intensive approach is being pursued.
• Concerns over implementation strategies have converged into two distinct camps, referenced throughout the field as Boomers (those who believe the best way to mitigate risks from AI is to achieve AGI as soon as possible so it can assist with mitigating its own risks) and Doomers (those who fear releasing every-more-powerful AI systems without understanding their core functionality to implement and verify safety controls poses an existential risk to humanity).
• AI development fixated on large language models has systematically violated the intellectual property rights of prior content creators.
• AI development has required massive human labor to score AI determinations to improve learning and has consequently exploited tens of thousands of workers in failing economies to work for poverty wages and be exposed to millions of images and videos of vile, abusive, sexual content.
• AI development has required massive build-outs of data centers DWARFING prior concepts of "data centers" whose power and cooling requirements potable water supplies in already drought-stricken areas and impose strains on local power grids whose costs are often being shifted to the public.
• In whole, the race to develop AI technology is pure colonialism and empire building – the TAKING of property controlled by others, the PROTECTION and SUBSIDIZATION of private commercial enterprises by governments , the EXTERNALIZATION of all negative consequences from parties enjoying the benefits and the CONCENTRATION of both power and wealth into existing elites.

Note that none of these themes have anything specific to do with OpenAI. They are equally applicable with any firm attempting to develop or apply AI technologies. The content within the book unique to OpenAI reflects its own set of recurring themes:

• OpenAI's corporate structure emphasizing its focus on ensuring "open" research into AI over profit-making opportunities was fundamentally a bait and switch, not only for attracting investors but engineering talent under false pretenses.
• OpenAI's management ranks reflect a pattern seen in every other large tech business reaching multi-billion dollar evaluations – engineers and leaders selected for abilities to deliver technology in cutting edge fields OFTEN present personality traits ill-suited towards honest, effective communication and management of personnel and OFTEN reflect traits leading to constant personal conflicts and a blinkered perspective on the impacts of the technologies being developed.
• In particular, Sam Altman has demonstrated these flaws throughout his career and, as such, is possibly one of the worst people one could select to be managing a firm that is aiming at creating a technology approaching "artificial general intelligence."

Again, for the full picture, buy or borrow this book, then read it cover to cover. Knowing that many will not, many of these themes are worth an attempt at summarizing, if only to at least provide a cocktail party level of understanding in the topics, if not to encourage a full read of the book.

AI Is Intrinsically Tied to Deceit and Inscrutability

Research into developing algorithms that could be implemented on computers to provide human-like interpretation and reasoning capabilities began in earnest in 1956 under a rather dry, academic sounding term automata studies. One of the original researches realized the term lacked a certain pizazz and landed on another: artificial intelligence. As Hao summarizes,

The name artificial intelligence was thus a marketing tool from the very beginning, the promise of what the technology could bring embedded within it. Intelligence sounds inherently good and desirable, sophisticated and impressive; something that society would certainly want more of; something that should deliver universal benefit. The name change did the trick. The two words immediately garnered more interest – not just from funders but also scientiests, eager to be part of a budding field with such colossal ambitions.

Cade Metz, a longtime chronicler of AI, calls this rebranding the original sin of the field: So much of the hype and peril that now surround the technology flow from McCarthy's fateful decision to hitch it to this alluring yet elusive concept of "intelligence." The term lends itself to casual anthropomorphizing and breathless exaggerations about the technology's capabilities.

As Hao explains, use of the term "intelligence" with these technologies poses two related problems. First, without any scientific definition of what "intelligence" actually is, it is impossible to devise an objective measure to know when it has been implemented. Decades ago, the "Turing Test" was viewed as a crucial milestone – the ability of a human to interact with an "entity" and not be able to discern whether that entity was a real human or a computer. Technology evolved by the 1980s and 1990s to eclipse that threshold and suddenly the goalpost was moved to expect abilities to process and respond to visual data. Clock rates and available memory improved enough for those capabilities to be achieved in the 2000s. Now the goals have expanded into synthesizing entire songs or writing novels, etc.

The point here is that the inability to DEFINE what intelligence IS allows those in the field to conveniently shift expectations when doing so suits their financial or legal advantage. At the same time, the undefined nature of the very word intelligence means that ANY conversation between ANYONE involved in the field is inherently incapable of reflecting a meeting of the minds because the core word behind the discussion has no concrete meaning scientifically, legally or ethically.

Symbolism Versus Connectionism

The capabilities and underlying infrastructure commonly associated with AI today reflect a crucial conceptual choice that was made decades ago between two alternative approaches for modeling information for use in "AI"-like functionality. Put simply, there are two approaches that can be taken to design systems to abstract information into machine-processable forms then create algorithms to act upon those abstractions to do something useful. One approach involves optimizing the model used to represent the discrete units of knowledge to be housed in the system – this focus is referenced as symbolism. Another approach involves keeping object models more simple but focusing on relationships between them – this focus is referenced as connectionism.

The key difference between a symbolism approach and a connectionism approach is that approaches focused on symbolism generally require more HUMAN thought and inventiveness. The value of the symbolism stems from combining enough attributes about a thing to make it easy to find linkages to other objects while keeping the total data required to reflect that model relatively small. It isn't helpful to model a complex concept with a single letter only containing 8 bits but it isn't useful to have 30,000 bytes used to reflect an instance of that object either.

In contrast, the value of the connectionism approach is that, for some types of data models, a connectionism approach can boil down to pure mathematical reduction. A greater number of "connections" / associations / linkages can be modeled by logically assigning larger mathematical matrices to the object and performing more matrix mathematical calculations on the content of those matrices. Humans know enough about how to program computers to perform matrix mathematical operations that the SIZE of those matrices isn't a human problem. Just throw hardware and memory at the work. The problem this brute force connectionism approach is the resulting data produced to matrix operations on TRILLIONS of data points is completely indecipherable to any human, including those who devised the algorithms.

Until the 1980s, these two approaches were essentially neck and neck in popularity within the field, partly because both approaches were equally crippled by the limitations of memory and processing speed at the time. Memory limitations impaired the complexity of models that could be created for objects while the lack of networking protocols prevented smaller computers from being linked together to share tasks, impairing the ability to advance "neural network" algorithms. As computing performance began accelerating in the 1980s, those conducting AI research could foresee where Moore's Law would take computing power. At that point, research became almost entirely focused on connectionism based algorithms.

This shift to connectionism had PROFOUND impacts on the AI field and has PROFOUND impacts on society today. First, the matrix model nature of the connectionism approach means that improving model quality generates EXPONENTIAL growth in computing resources needed to create an AI system. It also requires EXPONENTIAL growth in the volume of data required to compute all of those trillions of connection probabilities. It also consumes EXPONENTIAL amounts of compute to USE the results for end-user requests after the system is developed and trained. However, for certain types of problems that provide financial rewards to solve, the connectionism approach DOES work... up to a point. This has further limited the quantity of people and financial resources involved with research in the symbolism realm. Yet not all problems that might benefit from a generic artificial intelligence are best served by connectionism-centric models.

This is worth restating in a couple of different ways.

The connectionism approach reflected in the Large Language Model systems prevalent today is suited for using TEXT data and SYNTHESIZING new output that may be required to follow expected styles or conventions or is needed to quickly SUMMARIZE a given text input into an alternate form that's "close enough." A connectionism-based system is ALWAYS going to generate ouptut based upon PROBABILITIES but can NEVER, EVER guarantee a correct "answer" to any specific input without significant "wrapper code" adding guardrails for well-defined criteria. The connectionism approach is NOT optimal for spotting correlations between a continuously changing set of inputs to boil them down into a smaller set of information according to rigidly established formulae. That type of problem is best solved using "machine learning" paradigms which are vastly different than connectionism models and – for what they do – are more compact and processing efficient. These technologies have been productized and sold commercially for over a decade and do not require hundreds or thousands of servers to generate results even for huge applications.

Another way of conveying the same point? It has become common for thousands / millions of users to use ChatGPT or similar large language model AI systems as their search engine. Even Google is now including AI "search results" in its regular web search output in an effort to complete. This is equivalent to booking a flight on the Concorde SST every time you decide you need to take a "jet" from point A to point B. That's no exaggeration. The author Hao referenced data in a research paper written by Sasha Luccioni, a climate lead at a competing AI firm named Hugging Face. Luccioni estimated every single AI-generated image likely consumed the amount of energy required to charge a cell phone to 25 percent. One thousand AI-generated images might equate to 242 full cell phone charges. If people are beginning to use AI to generate video content, the energy consumed to synthetically create hi-res video for a 5-10 minute video clip becomes staggering. But even for traditional search usage, LLM based AI models are GROSSLY less efficient than traditional web searches with twenty five year old indexing technologies. And MILLIONS are now using ChatGPT and other engines in exactly that fashion.

The Demand for Training Content: Theft and Crap

As stated previously, a crucial consequence of the industry's complete fixation on connectionism based technologies is that connectionism-based solutions are EXPONENTIAL in their use of resources. Resources are not limited to the computing power used during training or the computing power to operate the resulting "trained" model for production requests from users. The exponential resource demand also involves the training data itself. This has profound legal and societal implications.

As Large Language Model approaches first began development in the mid 2010s, the data sets fed to them were typically on the order of tens or hundreds of megabytes. That amount of data helped validate different theories for underlying data structures and computations required to improve "connection" probabilities but engineers quickly found that LINEARLY increasing the number of "tokens" considered in calculating probabilities – essentially the "depth" of the system's memory when generating a response – required EXPONENTIAL increases in the data used in training. Exponential increases in compute and memory could be solved by simply throwing money at the problem and renting more compute across various data centers. But TEXT DATA is not as easy to obtain at the drop of a hat. At least, it's difficult to obtain if you are going to ASK for permission. And it's not easy to obtain in identical quality at 10x or 100x or 1000x of current volumes. So OpenAI (and other firms) simply didn't ask for permission. They used web crawlers and simply pulled in more content from public web sites as data needs grew. As data needs grew past the limit of what was available on formally curated, edited, secured content, OpenAI and other firms simply lowered their standards for what would be accepted and slurped in more data from lower quality tiers of content. Lower quality in terms of its veracity and, in MANY cases, the content involved, including racist rhetoric and sexually abusive text and imagery.

Even if these legal and ethical problems are completely discounted, the larger problems with models requiring exponential increases in training content should be obvious to the average mathematician, much less engineers working on the cutting edge of this technology. First, many of the secondary and tertiary sites sucked into the training maw of OpenAI and competitors were web portals offering peer-to-peer help for solving problems across a multitude of disciplines – software engineering, electrical engineering, Linux system administration, data center operations, etc. Anyone who has USED these sites can immediately spot huge problems with this strategy. First, every thread STARTS with a question posed by someone who BY DEFINITION doesn't know what they're doing. They may not frame their question correctly, they may mis-state their initial conditions, etc. These sites typically implement some form of reputation scoring which requires users without prior history to provide answers that get "up-voted" by those already with reputation points before their answers are promoted as viable. Yet scraping techniques likely were unable to distinguish between "ACCEPTED" content and presumably lower quality content submitted by untrusted users. ALL of the thread content was fed into the training process.

This assumption that MORE "data" will always improve model quality is logically insane. If you are given a cup filled to the brim with water but told the cup has one drop of dioxin in it, would you drink the water? What if someone offered to transfer the cup to a gallon jug and fill it with MORE water but they also told you the extra water would have two drops of dioxin in it. Would you drink water from the gallon? Repeat the process for a 5-gallon jerry can... For a 55-gallon barrel.. For a tanker truck... For a tanker ship. Are you feeling any better about the safety of that drinking water? Of course not. Any process that claims to clean BAD material by adding MORE material will never work but ESPECIALLY if the new material is of LOWER quality than the starting material. This is the current state of AI systems in use.

One far more subtle point that Hao makes in the book is that this exponential demand for electronic text is acting as an explicit filter on the types of thinking that are getting "learned" by AI systems. How? The need to scan PETABYTES of text data essentially requires that data to be online. No one has the money to scan books written in thirty languages then perform optical character recognition on those images then feed THAT text into training sources. So what IS getting fed into AI training? The easy answer is whatever is on the internet. In 2025, the Internet Society Foundation estimates that 55% of all Internet content is in English. What is stunning is the next highest language in use is Spanish but with only 5% of content. The shares get smaller from there. Ideas and idioms that might have unique expressions in other languages not popular on the Internet are NOT making it into training data sets and thus do not influence AI output. Spending BILLIONS to ingest content restricted to a handful of languages is thus leaving behind millennia of accumulated insight that might only be present in the color and idioms of languages not important enough to have gained a foothold on the Internet.

Exploitation and Abuse of Human Labor

To counteract the impact of "lowering the bar" on training data quality, firms developing AI systems devised processes by which training inputs and system outputs could be reviewed by humans to provide "scores" for various images and content regarding the presence of sexual imagery, sexual abuse, extreme violence, etc. The scores were fed back into training so probabilities could be manipulated to detect and avoid generation of inappropriate output. This is NOT pleasant work for anyone to do. But AI firms took advantage of worldwide internet access and economic / political strife in second and third world countries and farmed this via work out to people making the equivalent of ten dollars per day. Ten dollars per day to look at content that might be the WORST possible content you could imagine encountering because, remember, bigger models need PETABYTES of data and the only place we can get that much data is on fringe content sites.

This approach for farming out nasty work via "AutoTurk" style systems to people in dire economic circumstances is where the book begins cementing together the author's larger themes about empire building. Firms like OpenAI explicitly sought out third-party firms that had already created "piece work" content categorization systems that could employ thousands of people anywhere on the globe to do the unpleasant work. But in a world with legitimate economic opportunities, no one would voluntarily do this work. Instead, this work was predominately done in countries like Chile, Uraguay and Venezuela where political and economic upheavals suddenly yielded tens of thousands of English-speaking, educated workers with home computers and internet connections who suddenly had no other job opportunities and were prevented from leaving the country to find opportunities elsewhere. Hao actually references prior writings about this exploitative form of "disaster capitalism", particularly Naomi Klein's The Shock Doctrine, while tying this strategy into the larger theme of empire. These references are very apropos.

The Asperger's Generation of Corporate America

One thing becomes clear in Hao's coverage of specific events related to OpenAI as an operating business and the behavior of its senior leadership. We are now twenty years into what might be termed the Third Wave of Computer and IP technology. The first wave for "personal computing" could be loosely defined as the period from 1976 to 1993 as technology evolved to make individual computers financially viable for both home and business users. The second wave for "networking" lasted from 1994 with the advent of AOL then always-on home broadband connections to the Internet to 2004 when reasonably fast computers with reasonably fast network connectivity became the norm. The third wave for "social media" and "cloud hyperscaling" began in 2005 with sites such as YouTube and Facebook that converted users and their metadata into the product then advanced with technologies aimed at mining that metadata into enormous databases for further analysis and manipulation.

One thing in common to each of these generations of technical evolution is they all resulted in small disrupting firms suddenly become leviathans in the economy in very short periods of time. Apple, then IBM, then Microsoft in the first wave. Cisco, then Yahoo, then Google in the second wave. Facebook/Meta, then Amazon, then OpenAI in the third wave. In each of these waves, at least one of those disrupting leviathans was founded and led by highly intelligent, maniacally competitive people with obvious talent for their field but who were also not, as one might say, "hooked up right." The terminology wasn't in common use at the beginning of this simplified history but it is certainly widely used now. Some of these leaders demonstrate traits that place them signficantly off of center in the spectrum of "average" thinking and interpersonal communication skills.

One topic the author touches on when covering events at OpenAI is a "movement" that gained popularity in the 2000s and gained a name in 2011 – effective altruism, or EA. Briefly stated, EA is a term used to describe a set of priorities individuals can use to "optimize" the ultimate "net good" of their life, both for themselves and larger societal interests. When an individual becomes aware of a societal problem, they face a choice. Do they IMMEDIATELY, PERSONALLY engage in work to correct that problem? Or do they continue doing something that might be more financially rewarding to them in the immediate future and make charitable contributions to someone else who can IMMEDIATELY work the problem? Or do they continue their own career, working as hard as possible to climb the corporate ladder, make as much money over their career, THEN give some vast some of money away in the distant future to someone working that problem?

Extremely smart people skilled in areas of mathematics and science love to think of problems in terms of equations and rates of change (derivatives) and optimization. Extremely smart people skilled in these areas who are also on the Asperger's spectrum are even more prone to such thinking. Their skills make them HIGHLY valuable in the modern economy so they are often very well paid and they think of problems in systematic, global ways. Adopting EA as an organizing principle of life can be a crutch for some people to use to avoid legitimate conflict and remain on a path that is, in fact, doing great global harm. The EA thinker may conclude their efforts ARE causing some problem that might be marginally bad but by continuing this work, my lifetime net worth will be 50X instead of 5X and I can use 50X money to do good later and keep my total lifetime "worth" above the current 5X, thus optimizing the world.

That's what EA adherents like to believe. What they fail to understand is that their often blinkered understanding of the entire picture means they are not accurately assessing the potential harm of their short term actions. Despite their mathematical bent, many EA adherents fail to comprehend the cumulative, exponential damage caused by the short term damage they may recognize but discount. They may also be over-estimating their accumulated wealth, having little understanding of business cycles, wholesale fraud or the duplicity of those around them that may very well leave them with nothing to show for their efforts.

Of course, all of this assumes the EA adherent being discussed is being honest about their beliefs and motivations. Hasn't it been refreshing to hear these mop-haired, precocious 25-year old billionaires appearing on a panel in front of two thousand people at a trade show talking about the need to "give it all back" to the community? What if discussions of EA are simply part of the calculus these startup billionaires are using in their attempt to disarm critiques of their business practices and the potential for abuse of their creation and resulting wealth?

Moral / Ethical Tunnel Vision

The career path details shared by the author about many of the key players in AI reflect a not-so-obvious but crucial similarity to the paths of many tech titans of the first three bubble waves of modern technology – the PC era, the Internet era and the current social media era. Many AI business and science leaders had the intellectual chops to gain acceptance into the world's top institutions – Harvard, Stanford, MIT, etc. But like many of their counterparts in earlier tech bubbles, many didn't COMPLETE a college degree. Some abandoned college after only a year or two of course work. They saw enough overlap between their coursework and business opportunities opening up in the real world, saw zero expectations or requirements for holding a degree before entering those businesses and promptly quit to pursue some combination of money, influence and power.

If this were more infrequent of an occurrence, it might not be that much of a concern. When the pattern holds for a significant number of influential people in these critical fields, it becomes a great concern. Maybe they might have just taken one or two more years of courses in their core engineering or science degree program and simply emerged as an even smarter but narrowly trained wizard. But that's not what college is supposed to be, even for engineers and scientists. Most degree programs require some coursework in non-degree fields. For engineers, such coursework might involve psychology, economics, business and accounting or maybe ethics and government policy. A single class in any of these disciplines won't yield Nobel Prize winning expertise but it might break through the self-imposed tunnel vision of someone who started narrowly focused on technology and didn't even complete that degree program.

AI – Creating a Modern Empire

All of the prior themes summarized above are part of Hao's overall thesis of the empire-building nature of AI. However, Hao wastes little time in laying out that thesis of the book. On page 16, the entire premise of the book is layed out in crystal clear language worth quoting here:

Over the years, I've found only one metaphor that encapsulates the nature of what these AI power players are: empires. During the longer era of European colonialism, empires seized and extracted resources that were not their own and exploited the labor of the people they subjugated to mine, cultivate, and refine those resources for the empires' enrichment. They projected racist, dehumanizing ideas of their own superiority and modernity to justify – and even entice the conquered into accepting – the invasion of sovereignty, the theft, and the subjugation. They justified their quest for power by the need to compete with other empires: In an arms race, all bets are off. All this ultimately served to entrench each empire's power and to drive its expansion and progress. In the simplest terms, empires amassed extraordinary riches across space and time, through imposing a colonial world order, at great expense to everyone else.

The empires of AI are not engaged in the same overt violence and brutality that marked this history. But they, too, seize and extract precious resources to feed their vision of artificial intelligence: the work of artists and writers; the data of countless individuals posting about their experiences and observations online; the land, energy, and water required to house and run massive data centers and supercomputers. So too do the new empires exploit the labor of people globally to clean, tabulate, and prepare that data for spinning into lucrative AI technologies. They project tantalizing ideas of modernity and posture aggressively about the need to defeat other empires to provide cover for, and to fuel, invasions of privacy, theft, and the cataclysmic automation of large swaths of meaningful economic opportunities.

The Coup at OpenAI

Anyone with even cursory familiarity with OpenAI as a company will likely expect AI Empire to answer the question everyone in the industry was asking regarding events in November of 2023, events that arguably constitute one of the most bizarre power struggles in the history of Corporate America or Corporate Anywhere. WHAT THE HELL IS GOING ON IN THAT COMPANY? Hao addresses the events but does so near the end of the book. At that point, it becomes very apparent how the "coup" was nearly inevitable given the numerous unresolved personnel management issues within the company.

What exactly happened?

Publicly, OpenAI's board fired its CEO Sam Altman on Friday November 17, 2023. OpenAI's CTO was immediately named interim CEO. A few hours later, OpenAI's chairman Brockman resigned followed by three key technical leaders within the firm. By Saturday, November 18, reports were appearing stating the board was already negotiating to hire Altman back. By Sunday November 19, Altman and Brockman were negotiating in person at OpenAI's building to return. After those discussions failed, the board announced a new CEO and Microsoft instantly announced the hiring of Altman and Brockman and the three technical leaders under him into a new AI division at Microsoft. On Monday, November 20, an open letter began circulating that eventually gained signatures from 745 OpenAI employees (of 770 total) threatening resignations if the board didn't resign immediately. At that point, the board caved and by November 21, Altman was re-instated as CEO and the board was restructured with three new outside board members.

Just a little communication faux pas. Not that important really. In fact, people at OpenAI now try to downplay the entire incident by referring to it as The Blip.

But what REALLY happened?

In summary,

• Over the two years prior to November 2023, conflicts between teams responsible for Research versus Safety regarding spending and delivery timelines had resulted in increased staff churn and tensions at leadership levels.
• In early October 2023, distinct members of OpenAI's board were independently approached by two different senior OpenAI leaders with concerns about situations in which CEO Altman provided conflicting answers and direction to different company leaders.
• In the course of investigating those communication concerns, two different board members who talked to each other realized Altman had conducted private conversations with each of them mis-stating what the other board member had stated to him.
• Two senior leaders confirmed to the board they would back a decision to oust Altman from the company – one confirming they would agree to be interim CEO, the other confirming they would stay on with OpenAI to continue leading a core team.
• The board acted and fired Altman but failed to devise a message that appropriately articulated WHY Altman was fired, leaving employees worried about their personal wealth tanking from a potential plummet in OpenAI's value to demand his reinstatement "or else."
• Ultimately, none of the senior employees who first notified the board then offered their support for replacing Altman held their ground – they caved and signed the open letter demanding his return.
• Ultimately, the very board members who themselves had been lied to by Altman about their own conduct refused to stick to their guns, offering to return him to the company and agreeing to vacate their board positions.
• Ultimately, the 745 employees who signed the open letter demanding Altman's return did so despite their own concerns about prioritization of functionality over safety and their own observations of the tension and strife within the company stemming from Altman's manipulative communication patterns.

A real profile in courage on the part of everyone involved, huh?

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The conclusion one reaches from reading of the events surrounding this "coup" and the entire book Empire of AI is that it is perhaps the epitome of what to expect in a situation where the stakes involve billions of personal wealth, business leaders with severely deficient business and personnel management skills, and a workforce who themselves lacked the expertise and sophistication to comprehend the ethical nature of the underlying problems. The vast majority of participants in the drama from top to worker bee all chose the most expedient and lucrative path over the ethical path. If they were just making Milky Way candy bars and the disputes involved changes to the nougat recipe, no one would care. If they are developing technologies that can further concentrate power and trigger massive economic strife across the entire world, these are not the type of people you want in control.

WTH