Sam Altman's comment - II

Sam Altman makes it sound as if the training of AI is an independent activity that has no connection with humans.  But one of the factors that has powered the technology’s dramatic progress over the last few years has been the explosion of available data on human behaviour and creativity. A few major well-placed players extract value from other people’s creative work, personal data, or labor. Articles, blogposts, books, paintings, You Tube videos ... practically the entire internet is used as the training data to train the chatbots. 

These tools do a lot of copying of training data. A language model outputs a paragraph that describes an interesting idea with no link to the source(s) it was drawn from. Sometimes a system outputs large amounts of text verbatim or images identical to ones in their training data. The New York Times has brought a copyright lawsuit against OpenAI that features exhibit after exhibit of text, generated from ChatGPT prompts but outputting verbatim text from the newspaper. 

Artists have sued AI companies stealing their work and having people use tools from these companies to pass off AI-generated art as their own. Others, such as author George R. R. Martin of Game of Thrones fame and novelist Jodi Picoult, among others, have also filed suit against OpenAI and Meta for copyright infringement for using their books to train language models. Whatever value AI has is due to the creativity of the human workers who produced the original art, science, or journalism that was used as training data. Artists are being replaced by the very AI models that were built from their work without their consent or compensation. 

AI always involves people. In November 2023, the self-driving car company Cruise admitted that its “driverless” robotaxis were monitored and controlled (as needed) by remote workers. The New York Times published a story that reported that these cars “frequently” had to be assisted by remote human workers. Calling this “misinformation”, Cruise CEO Kyle Vogt clarified: "these cars didn’t need to be remotely driven “frequently,” but 2–4 percent of the time in “tricky situations.” 

Most AI tools require a huge amount of hidden labor to make them work. These kinds of workers do a host of tasks. They are asked to draw green highlighting boxes around objects in images coming from the camera feeds of self-driving cars; rate how incoherent, helpful, or offensive the existing responses from language models are; label whether social media posts include hate speech or violent threats; and determine whether people in sexually provocative videos are minors. 

These workers handle a great deal of toxic content. Given that chatbots recombine internet content into plausible-sounding text and legible images, companies require a screening process to prevent their users from seeing the worst of the web. OpenAI had subcontracted Kenyan workers making less than two dollars a day to filter out gore, hate speech, child sexual abuse material, and pornographic images from ChatGPT and OpenAI’s image generation tool DALL-E. Those workers were lured in by the prospect of breaking into the lucrative field of computing, but ended up with PTSD. 

OpenAI also employed over a thousand other contractors globally to perform reinforcement learning from human feedback on its language models, including prompting the models repeatedly and scoring the answers, in an effort to make the model give appropriate and inoffensive answers.

The generative AI rush has created the “red-teamer”. Red-teaming is a strategy of feeding provocative input of language or text-to-image models, and assessing whether the outputs are biased or offensive. For a model to reach general release to the public, it is the full-time job of multiple people to hurl slurs, violent descriptions, and all manners of internet filth at the model to see if it produces words that are worse, or responds with something morally appropriate. 

They must then deal with potential hateful material in model responses and report them as such. There are people who do this all day long for almost every commercial language and text-to-image model. This takes an immense mental toll on these workers, being subjected to hours of psychological harm everyday. This work is also highly precarious, with tech companies largely directing when and where there will be more work. 

The ImageNet project is a large visual database containing more than 14 million images designed for use in visual object recognition software research. It’s creation would not have been possible if it weren’t for the development of a new technology: Amazon’s Mechanical Turk, a system for the buying and selling of labor for performing small sets of online tasks. It took two and a half years and nearly 50,000 workers across 167 countries to create the dataset. 

This industry has been called by many names: “crowdwork”, “data labor”, or “ghost work” as the labor often goes unseen by consumers. But this work is very visible for those who perform it. We wouldn’t have the current wave of “AI” if it weren’t for the availability of on-demand laborers who could be called upon at any time to perform a set of tasks whenever some AI researchers or corporate engineers demanded it. The critic Astrid Taylor calls it faux-tomation, or fake automation. In this sense, we are certainly used to being told that systems are intelligent and almost see magic, but actually are being propped up by exactly this type of click work.            

Then there is also the labor involved throughout the supply chain of mining, construction, transportation and installation of the hardware. The philosophers Michael Hart and Antonio Negri have a phrase that captures this nicely - The dual operation of abstraction and extraction. Abstracting away the material conditions of production while extracting ever more information and resources. It seems magical from the front end because we never see the deep costs. 

Sam Altman's comment - I

During the AI summit in India, the OpenAI boss, Sam Altman, tried to ease concerns about how much power is used by artificial intelligence models. He told the Indian Express: “People talk about how much energy it takes to train an AI model – but it also takes a lot of energy to train a human. It takes about 20 years of life – and all the food you consume during that time – before you become smart.”

First let us take a look at who Sam Altman is. He is a product of Silicon Valley. His career was first as a founder of a startup, and then as the president of Y Combinator (YC), which is one of the most famous startup accelerators in Silicon Valley, and then the CEO of OpenAI. He is incredibly good at telling stories about the future and painting these sweeping visions that investors and employees want to be a part of. 

He tried to build a portfolio of different investments and different initiatives to place himself in the center of various trends, depending on which one took off. He invested in quantum computing, in nuclear fusion, in self-driving cars and he developed a fundamental AI research lab. Early on at YC, he came to the conclusion that AI would be one of the trends that could take off.

Ultimately, the AI research lab was the one that started accelerating really quickly. So he made himself the CEO of that company. Originally, he started it as a nonprofit to try and position it as a counter to for-profit incentives in Silicon Valley. But within one and a half years, OpenAI's executives concluded that if they wanted to be the lead in this space, they had to go for a scale at all costs approach. Apparently, there are actually many other ways to have progress in AI that does not take this approach.

But once they decided on this approach, they realized that the bottleneck was capital. It just so happens that Sam Altman is a once-in-a-generation fundraising talent. He created a new structure, nesting a for-profit arm within the nonprofit to become a fundraising vehicle for the tens of billions and ultimately hundreds of billions that they needed to pursue the approach that they had decided on. 

He is extremely good at understanding human psychology and then motivating people to do what he wants them to do.  He is able to wear different hats depending on who he's talking to. He pushes a certain narrative when he's talking to the government and he pushes another narrative when he's talking to podcasters or journalists. He figures out what it is that his audience needs to hear to get them to then come along with him to the next step. That's why he's been such a successful fundraiser, even when the financial picture of this company is rather bleak.

In Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI, Karen Hao paints a much more critical portrait of Sam Altman than the standard “visionary founder” narrative. He is someone who genuinely believes in the transformative (and potentially dangerous) power of AI. He talks about existential risk and the need for caution, yet simultaneously pushes aggressive scaling and deployment. He seeks to concentrate power in a small set of institutions (like OpenAI and its partners), with himself positioned as a central gatekeeper of the future.

She opened the book with a quote which encapsulates in a nutshell his character and the role that he has played in creating the mythology around AI, AGI and OpenAI: “Successful people create companies. More successful people create countries. The most successful people create religions.”

In her book, Hao describes the difference in opinion within OpenAI between Boomers (AI accelerationists) and Doomers (AI safety advocates). Many Doomers were skeptical about having Sam Altman as the leader of the company that brings Artificial General Intelligence (AGI) because he seemed in too much of a hurry to bring his products to market without paying adequate attention to safety and alignment concerns. AGI could give the company that develops it ultimate power and many employees felt Altman wasn’t the right leader.

Hao says that these tensions were often fueled by Altman’s untrustworthy character. Altman would listen carefully to people to understand what they wanted and would seem to agree with their views. Then he would do the same for others with opposing views. He preferred one to one meetings so employees would learn about this when they talked to each other and they were unsure of what exactly his views were. For those who opposed his agenda, he would quietly work behind the scenes to eliminate them from the company.

He has a younger sister, Annie, from whom he is estranged. She had psychological problems and even did sex work to support herself when Altman was living in million dollar homes. She has even alleged childhood sexual molestation by Altman. Hao writes: 

Annie’s story deepens the dueling portraits that people paint of Sam. He is at once generous and self-serving, agreeable and threatening, a benefactor for so many people and the source of great personal pain for others. Someone who projects sincerity and altruism in public but reveals a more complicated calculus through his behaviors behind closed doors. 

Someone who can give and take away, leaving many with an impression that they are part of a larger game of chess for which only he can see the full board, and the end game is to preserve his power as king.