The affidavit of an FBI special agent, and the Russian documents attached to it, offer a rare glimpse into the inner workings of a vast Russian network of disinformation.

Last week, the U.S. Department of Justice indicted two Russians — both employees of state broadcaster RT — accused of illegally funnelling $9.7 million into a Tennessee media company.

The unsealed indictment said the founders of the unidentified company — widely reported to be Tenet Media — knew their funding came from “the Russians.” Far-right influencers hired by the company, including Tim Pool, Benny Johnson and Dave Rubin, have said they were unwitting “victims” of the alleged scheme.

The indictment and its associated documents show a side of Russian influence operations people in the West rarely see, said Roman Osadchuk of the Atlantic Council’s Digital Forensic Research Lab in Washington.

Normally, he said, “we’re looking at something that surfaces, the open side of things, like what’s being published on social media. Here we definitely see something from the inside.

“So this was unique.”

The affidavit also reveals the growing sophistication of Russia’s disinformation methods, said Robert English, a Russia expert at the University of Southern California at Annenberg.

“It’s on the cusp of becoming, you know, a really disturbing, distorting actor in global politics,” he said.

While the indictment doesn’t name the Tennessee-based outlet, details in the court document match those of Tenet Media, a company founded by Canadian far-right commentator Lauren Chen and her husband Liam Donovan.

The affidavit supports the the U.S. Department of Justice’s request for the seizure of 32 internet domains and includes descriptions of Russian disinformation projects in both their original Russian and in English.

The Department of Justice alleges that the author of at least some of the descriptions is Ilya Gambashidze, founder of two companies — the Social Design Agency and Structura — that worked directly with Russian President Vladimir Putin’s office to create a series of influence campaigns. The Social Design Agency created the content, while Structura focused on dissemination.

Gambashidze’s writings, cited at length in the U.S. Department of Justice documents, reveal a keen understanding of political dynamics in the West, through the eyes of a man looking for pressure points to exploit.

They also show Gambashidze understood something that Russian propagandists have known since Communist times — that it’s a waste of time to try openly to promote Russia’s cause in the West.

When dealing with a U.S. audience, “there is no point in justifying Russia and no one to justify it to,” Gambashidze wrote in a project proposal called “Project Good Old USA,” which was among the supporting documents released by Washington.

A long history of spreading conspiracies

As a former KGB officer, Putin has always appreciated the value of working in the shadows.

“What amazed me most was how one man’s effort could achieve what whole armies could not,” he wrote in his autobiography.

If he’s comparing the lacklustre performance of the Russian Army in Ukraine to the success of Russia’s internet influencers, he could be forgiven for believing that today more than ever.

The KGB’s efforts to interfere in U.S. elections go as far back as 1968. It attempted to popularize the slogan “Reagan means War” in 1980, and in 1985 staged the successful disinformation campaign Operation Denver — the conspiracy theory that HIV was created in a CIA lab.

The FBI alleges that Putin charged one of his most trusted deputies, Sergei Vladilenovich Kiriyenko, with shepherding disinformation projects aimed at Germany, France, Mexico, Israel and the U.S. presidential election.

“What’s happened in Canada or the U.S. is already disturbing but not yet critical,” said English. “In Europe, and also everywhere from Qatar to Kazakhstan, Azerbaijan in particular, we have seen [the Russians] swing elections. We have seen them turn committee decisions in the European Parliament and other institutions of the European Union. No doubt it has been more effective there.

“And with AI and with ways of multiplying their impact through technology, the future is pretty grim. And that’s not even taking into account the use of deep fakes and fabricated evidence.”

Plans for a ‘guerrilla media campaign’

In his 2022 proposal for a “Guerilla Media Campaign in the U.S.,” cited in the Department of Justice documents, Gambashidze compares the two parties on the American political scene. The department redacted the names but there is no doubt which party is which.

Democrats, he writes, are “far-left globalists who advocate for perversion of traditional moral and religious values, while supporters of the [Republican Party] are normal people whose priority is to preserve traditions of the American way of life.”

In the same document, Gambashidze zeroes in on race.

Democrats, he writes, “are also people of color and supporters of ‘affirmative action’ and ‘reverse discrimination’, i.e. infringement on the rights of the white population of the United States, while [Republicans] are the victims of discrimination by people of color.”

Gambashidze identifies the cost of living as a key pressure point. Americans, he writes, are “suffering from rising prices, primarily for gasoline, historically high inflation and the actual impoverishment of white taxpayers, a significant part of the middle class. Under these circumstances, the recipients of public assistance, unemployed people of color and residents of large cities end up being privileged groups of the population.”

Those white Americans, he adds, “are afraid of losing the American way of life and the ‘American dream’. It is these sentiments that should be exploited.”

The first goal of the “guerilla media” program, Gambashidze writes, is “to secure victory of the Republican Party candidate” and the top themes to be used in that effort are inflation and “unaffordable prices for food and essential goods … risk of job loss for white Americans” and “privileges for people of color, perverts and the disabled.”

The campaign’s secondary goals, Gambashidze adds, are “to increase the percentage of Americans who believe that the US ‘has been doing way too much to support Ukraine’ to 51%” from 41%, to raise the number who believe the war should be ended soon even if it means Ukraine surrenders territory from 43% to 53%, and to drive [U.S. President Joe] Biden’s approval rating down to 29%.”

But Gambashidze also warns Russian propagandists to take care not to harp too much on Ukraine or Russia-specific matters that could attract attention: “The amount of the highly resonant content and hot topics should not exceed 20 percent of the total volume of all publications.”

Sleepers on the internet

Russia is famous for its use of “illegals” or long-term sleeper agents, a Cold War tradition that continues to this day.

That same tradecraft appears in the Department of Justice indictment and its associated documents. They describe a disinformation scheme — dubbed Operation Doppleganger by the DoJ — that allegedly used sleeper cells of influencers whose job was to quietly generate a following, without flagging themselves as overly political.

One of the documents released by Washington is Gambashidze’s original written proposal for Doppleganger. 

Producers of Doppleganger material would masquerade as regional news groups, he wrote in Russian. Their target audiences would include swing-state voters, voters in a small group of very red states, “U.S. citizens of Hispanic descent, American Jews, Community of American gamers, users of Reddit and image boards, such as 4chan (the ‘backbone’ of the right-wing trends in the US segment of the Internet).”

“The objective,” he continued, “is to create and for at least five months moderately promote news groups in swing states through Facebook, Reddit and X (Twitter) — a total of 18 communities, one community per media outlet in six states: Nevada, Georgia, Arizona, Pennsylvania, Michigan, and Wisconsin.

“While in a ‘sleeping’ state, communities attract an audience through targeted advertising, planting, and organic reaches. At the right moment, ‘upon gaining momentum’, these communities become an important instrument of influencing the public opinion in critically important states and portals used by the Russian side to distribute bogus stories disguised as newsworthy events.”

Those bogus stories — entirely fake webpages not searchable by Google that mimic websites for legitimate news organization like the Washington Post’s — gave Doppelganger its name.

The goal: spread anxiety and conflict

Doppleganger posts mimicking both U.S. and European media outlets have been appearing online since 2022.

While some of those fake pages have conveyed key Russian messages about Ukraine — such as a phoney Fox News story titled “Sad Outcome and Tragic Finale: Zelensky Loses in War and Diplomacy” — others just sought to generate anxiety and discontent.

Those anxiety-inducing fake posts include one titled “Young Americans Face a Poverty-Stricken Old Age,” about the supposed future collapse of medicare and social security.

It may not seem obvious what benefit Russia derives from scaring U.S. millennials about their retirement prospects, but Russia’s themes always connect back to its objectives.

Gambashidze’s written proposal suggests a fake reader comment that could be appended to a Doppleganger story to sound an isolationist note: “Our country should solve its own problems and let other countries solve their own problems.”

It also pitches a “text factory” that would churn out content linking support for Ukraine with domestic economic pressures for U.S.-based influencers to repeat. Gambashidze’s pitch offers one suggested message for the text factory: “Last night, the House of Representatives approved the allocation of 40 billion dollars to Ukraine, while American families have to do without baby food.”

The message there, said Osadchuk, is that “it’s not your war. Here is problem X,Y and Z and you should be focusing on them instead of helping other countries.”

Taking both sides of an issue

During the Cold War, the nations of the West also aimed propaganda messages at the Soviet Bloc. But there was an important distinction between those messages and Soviet propaganda, at least in theory: Western governments held that it was important that the messages be consistent, because it would undermine their credibility to be seen speaking out of both sides of their mouths.

The Russians don’t appear to care much about consistency. Because their goal is to spark conflict and polarize societies, they are often active on both sides of the most controversial issues.

In the DOJ affadavit, Gambashidze presents a plan for a social media campaign targeting Israeli and American Jews. The stated goal of the campaign, aimed at right-wing Israelis, “is to rip Israel out of the general Western anti-Russian agenda.”

“The right-wingers also want better relations with Russia,” Gambashidze writes, adding that “the current head of Israeli government is considered a ‘friend of Putin.'”

The document proposes to boost the Israeli right. “Influencing the public opinion of Israel will impact the public opinion of Jewish voters in the U.S. prior to the 2024 Presidential Elections,” Gambashidze writes.

But at the same time, Russia appears to support some of the loudest anti-Israel voices on social media, such as pro-Putin U.S. influencer Jackson Hinkle, who has spread false negative stories about Ukraine, appeared as a speaker at pro-Russia rallies and is sometimes retweeted by Russian official sources.

The same is true of left and right. While Russian disinformation in North America and Europe currently tends to push right-wing and white supremacist themes, in Africa it pushes anti-colonialist narratives that present the West as an arrogant white exploiter.

Russian disinformation appears equally happy promoting the far-left and the far-right, since the goal is to weaken the centre.

“The idea to make people disrespect, hate and basically not speak to each other from both of the wings,” said Osadchuk. “Basically, making society more polarized, unstable and thus not able to come to some conclusion that would be beneficial for both of the wings for the whole country.”

While Doppelganger is clearly aimed at energizing and radicalizing U.S. Republicans or those leaning Republican, he said, there may be other Russian disinformation programs that seek to push Democrats further to the left. “The whole scope is unknown,” he told CBC News.

English said that closing websites is not a long-term solution, since the same content will soon pop up elsewhere.

“We also just have to inculcate internet hygiene and critical reading and thinking skills. Because there’ll always be one more way to reproduce, to create some new kind of content to get around some technical technological block or some legal obstacle,” he said.

“As long as our people are basically dumb, are being more and more dumbed-down and take things at face value, only read what they like and wallow in all of these websites, Instagram, when their main source of news is Twitter … I don’t know that we’ll ever be able to get a handle on this until we have more intelligent media consumers again.”

This is the second in a three-part series from Medscape Medical News on the impact of artificial intelligence (AI) on drug discovery and development. Part 1 is about AI’s role in designing speedier, more effective clinical trials. Part 3 reports on AI’s ability to create new proteins from scratch, streamlining the creation of protein-based therapeutics.

Scientists the world over are racing to end Alzheimer’s disease. Over two decades, they’ve conducted hundreds of clinical trials and spent billions in funding. Yet only a handful of Alzheimer’s medications have been approved.

But what if there were drugs already on the market that could help treat or even prevent this devastating disease?

If such drugs exist, geneticist Gyungah R. Jun, PhD, is determined to find them — using AI.

“Using big genetic and molecular data from patients and AI, I can predict everything in silico,” she said, including who is at a risk for Alzheimer’s and how these individuals will respond to existing drugs.

The idea is not far-fetched. About a third of US Food and Drug Administration (FDA)–approved drugs are later found to offer at least one new use. Some have racked up more than 10 post-approval indications.

Historically, these discoveries have happened by chance (think glucagon-like peptide 1 receptor agonists for weight loss), but AI is giving researchers like Jun an edge in the process.

The goal is simple: To quickly uncover new uses for approved drugs or previously studied chemicals, thus skirting the slow pace of new drug development and carving a fresh path to treatments for diseases that desperately need them.

Finding a New Way to Alzheimer’s Treatments

Candidate drugs for Alzheimer’s often target sticky amyloid plaques, and the development process typically ends with trying to find a target population of patients for a clinical trial.

Jun believes that’s backwards.

“My suggestion is we should start with precision medicine,” said Jun, a PhD and associate professor of biomedical genetics at Boston University Chobanian & Avedisian School of Medicine, Boston.

Her strategy focuses on genetics, which accounts for about 80% of Alzheimer’s cases. Jun began developing her approach several years ago, while working on genome-guided Alzheimer’s drug discovery projects for a large pharmaceutical company. She wanted to start at the cellular level inside the brain, identifying specific genes within networks of genes that would be “prime targets” for drugs already in use. But she could only explore drugs produced by her company, so it didn’t work.

When she returned to academia in late 2018, Jun expanded her efforts to include PubChem, a massive public database of drug compounds. Her method relies on machine learning, a type of AI that identifies patterns in large datasets.

Scientists already know many genetic markers associated with Alzheimer’s disease. Jun groups those markers into network-based “subtypes” according to cell type and function. Next, she identifies which gene within a subtype to target. To do that, she uses a graph neural network, a type of machine learning that makes predictions based on relationships between data points (called nodes) linked by lines (edges).

Once a target is in her sights, Jun sets out to find existing drugs that match up. She uses “unsupervised learning” — giving AI unlabeled data (in this case, PubChem) to interpret without any instruction.

Jun’s lab has tested this approach, landing on the APOE gene in the astrocyte subtype as their top contender. They used autopsied brains and astrocytes derived from human pluripotent stem cells to validate their findings.

Results suggest that estradiol, an FDA-approved estrogen replacement therapy, could be effective for treating Alzheimer’s in the APOE genotype. As it happens, at least three clinical trials repurposing estradiol for Alzheimer’s disease have already been completed or are underway. But Jun believes that they need to be reconsidered based on individuals’ genetic risk profiles.

“Clinical trials with patient selection markers will dramatically improve success by up to 90%,” Jun said.

Her hope is that pharmaceutical companies could borrow her technique and design clinical trials around specific genetic subtypes.

“Hopefully it can be quickly applied,” she said, noting that women have a higher risk of developing the disease.

Taking Aim at Rare Diseases

The odds of finding an effective drug are especially slim for patients with rare diseases, those affecting fewer than 200,000 patients. There are more than 7000 rare diseases, and just 5% have any FDA-approved treatment.

“It’s actually shocking,” said Marinka Zitnik, PhD, assistant professor of biomedical informatics at Harvard Medical School, Boston. “As someone who works in AI, that was such an important statistic in terms of how making even small improvements in saving time or decreasing the failure rate in drug discovery could have a huge effect on patients down the line.”

Her lab is investigating the use of existing drugs for diseases with no effective treatments, an effort named zero-shot drug repurposing. “Zero-shot learning” happens when a machine learns to recognize things it’s never seen before. It’s useful for drawing conclusions from large quantities of unlabeled data — in this case, rare, complex, and neglected diseases.

“We developed a model that, for the first time, can nominate a drug for a disease, even though that disease has zero known treatments,” Zitnik said.

Her lab specializes in geometric deep learning, a field of deep learning that can consider the geometry of molecules (as mapped in graph neural networks) to make predictions or generate new designs. They used their model to analyze more than 17,000 diseases, most of them lacking treatments and not well understood.

When the researchers asked the model to find potential drugs for a rare disease, it was able to identify previously hidden relationships to other diseases, including shared pathways, phenotypes, and pathologies. The findings enabled the model to identify existing drugs that could be effective. Its predictions were consistent with off-label prescriptions made by clinicians in a large health system, and because the model explains its reasoning, it may give clinicians greater confidence in its predictions.

In a case study, the model identified several approved drugs that may have positive therapeutic effects on Wilson’s disease, a rare disorder associated with excessive copper accumulating in the body that can trigger cirrhosis. Patients often develop intolerance to a common existing treatment, making long-term care challenging. But the model surfaced a promising potential therapeutic candidate, which previous studies suggest may remove iron from the liver. The lab is doing follow-up biological tests to confirm its effects on copper.

Predicting How a Disease Will Respond to a Treatment

The prospect of repurposing existing drugs for rare diseases “has a lot of low-hanging fruit,” said Shantanu Singh, PhD, a principal investigator at the Carpenter –Singh Lab at the Broad Institute of MIT and Harvard. Rare diseases are generally less studied, despite FDA support. “It’s hard to incentivize pharma companies to develop a drug that benefits just 1000 or 10,000 people,” Singh said. That leaves ample opportunity for researchers in academia and biotech to step in.

The Carpenter–Singh Lab uses AI to search for insights in cellular images, yet another approach that could help realize the potential of existing drugs.

When biologists look under a microscope, they rely heavily on their own knowledge to understand the image and detect abnormalities or changes, said Singh. This took him by surprise. In his previous role computing for an auto manufacturer, “my work was completely focused on this idea that you can make computers see, just like humans can see,” he said.

The auto company had been developing an early version of a self-driving car. Singh used data from satellite images to build predictive models that could recognize objects and people. It’s a type of AI known as computer vision, which lets computers recognize and glean information from images and video.

At the Carpenter–Singh Lab, automated robot microscopes take pictures of cells treated with different chemicals and compounds. AI and machine learning help “describe” what’s happening inside the cells.

“Just by looking at similarities between the patterns of different chemicals’ effects on cells, you can tell a lot,” Singh said.

Rare diseases are often genetic, and many feature mutations in more than one gene. But some — roughly 3000 or so — are thought to involve only one gene.

By introducing a single mutation into a cell line, researchers can create simplified models of these single-gene diseases. From there, they can analyze how the cell responds to different drugs, looking for any that revert the disease phenotype back to a healthy state.

“You’re able to now, at scale, across the 3000 diseases and across the [20,000-plus] drugs that are on the market, find potential drug-disease combinations,” Singh said.

It’s the technique that launched biotech company Recursion. (Anne Carpenter, PhD, senior director of Broad’s Imaging Platform, is a scientific advisor.) The company’s founders used cell imaging to identify a repurposed candidate for cerebral cavernous malformation (CCM), a rare neurovascular disease. Recursion’s repurposing efforts draw on the company’s database of about 17,000 known molecules, including approved drugs, and chemicals that other organizations attempted to develop but later shelved.

Available drugs only treat symptoms of CCM, such as seizures and bleeding in the brain. Cell imaging pointed to the effectiveness of a small molecule known as a superoxide scavenger. The researchers were surprised, but when they found previous research linking a CCM gene to oxidative stress, they moved forward with animal studies. Now, that molecule is in phase 2 trials, with data expected later this year.

For the approximately 350,000 people with CCM, a new treatment could become available soon. The finding may also have implications beyond CCM, which causes leaky blood vessels, a symptom seen in many other diseases, including multiple sclerosis and sepsis.

Bringing Repurposed Drugs to Patients

About one in five scripts are for off-label uses, which lack FDA approval. Generally, doctors can make the determination. They are unlikely to write a script without a disease model, which some rare diseases lack, according to Julie Owen, director of chemistry at Recursion.

“A lot of academic institutions are offering up suggestions for repurposing based on a biochemical assay or protein binding assay,” said Owen. “It’s just not sufficient. It’s not going to give confidence to clinicians to give a drug to a patient as an off-label use.”

If compounds are well understood or have FDA approval for other indications, their toxicity and possible side effects have been assessed. Knowing that a drug can be safely administered to healthy patients “can save you the phase 1 trials,” said Owen. “Potentially, you can get into phase 2 trials, depending on the disease model evidence that you have.”

Three repurposed candidates for rare diseases (including CCM) identified by Recursion are currently in phase 2 studies. Repurposed candidates for cancer indications are also in phase 2 and preclinical trials.

If these efforts pan out, thousands of patients could be able to access more effective treatments for conditions that pharmaceutical companies might otherwise have ignored. And the possibilities for AI-enhanced drug repurposing are only expanding.

The “pooled version” of the Carpenter–Singh Lab’s platform, for instance, uses a strategy known as optical barcoding, which tags genetic mutations in each cell model. This allows researchers to put thousands of cells representing hundreds of different diseases into each “well” (mini test tube) in a multiwell plate. Then, they can add a different compound into each well to study how thousands of compounds interact with hundreds of diseases simultaneously. Machine learning algorithms analyze images of the cells to “see” how they change.

“That can really scale it up,” Singh said. “If you’re interested in a specific disease, we don’t know what’s going to happen, but you’re increasing your chances of finding something.”

Continue on to part 3 of AI’s Drug Revolution.

What will you be doing only a decade from now when advanced versions of the artificial intelligence program ChatGPT have wormed their way into the fabric of life?

According to some experts, you may be out of a job. Two current labour disputes involving autoworkers and screenwriters are at least partly about the future threat of AI.

When AI comes for the jobs, writers may be among the first to go, warn two respected technology mavens writing in Foreign Affairs magazine. And they are not alone in that view. Even current versions of the AI program ChatGPT can sketch clearer prose than most humans, they say. And those programs are getting better. 

By 2035, as “white-collar workers lose their jobs en masse,” declare Ian Bremmer and Mustafa Suleyman, AI will be running hospitals and airlines and courtrooms. “A year ago, that scenario would have seemed purely fictional; today, it seems nearly inevitable.”

Thumb-twiddling time?

For Bremmer and Suleyman, job losses are a relatively mundane result of the AI revolution. Their ultimate concern is nothing less than the usurping of government power by intelligent machines and those who control them.

But will massive numbers of writers and lawyers and stockbrokers and coders and office workers really be sent home to twiddle their thumbs in a little over 10 years? There are many thoughtful skeptics who say there are really good reasons why that just won’t happen. And at the core of it all, they say, is our unique humanity.

Peeking 10 years into the future leaks into the realm of science fiction, and those who imagine the future — while sometimes offering useful warnings — can easily get things wrong. Viewing the 1968 movie 2001: A Space Odyssey is a good reminder.

“Anyone who says they can tell you that they can predict what’s going to happen is either deluded or lying,” said Canadian science fiction writer Karl Schroeder, who has written about AI in his novel Stealing Worlds and the short story The Suicide of our Troubles.

There is a certain irony in the comment, since Schroeder is also a professional futurist helping companies prepare for what may be around the corner.

He is convinced there is a value in using imagination to frame the possible extent of the AI problem as it becomes better at human tasks.

“It isn’t any different from the question of what to do with the jackhammer when you’re the guy with the pickaxe,” Schroeder said. 

Essential human skills

The lack of certainty over how AI will develop — and how quickly — means its eventual impact is open to infinite speculation, he said. As governments around the world consider how to regulate it, the unknowable nature of what AI will become is just one of many complications.

But unless intelligent machines grow into evil geniuses that decide to crush us like bugs, said Schroeder and everyone else I talked to, there is one certainty in the future relationship between humans and machines, and that is humanity.

“Much of what we do as humans, even though we have our official job titles, goes outside of the official job descriptions,” said AJung Moon, who teaches computer engineering at McGill University in Montreal.

While the artificial intelligence and robotics expert sees various portions of jobs being stolen away by smart software, as that happens, she said, humans will do more of the things AI isn’t so good at.

In her own job teaching university students, she sees AI taking away the boring, bureaucratic and redundant parts of the work, leaving her more time for the kind of human interaction that leads to student success.

“What is their learning journey like? What is their life like?” Moon said. “I can actually get to more forming of connections with my students.”

Things robots cannot do

As someone who has been working at the leading edge of robotics for more than a decade, Moon said a lot of work humans do is in no danger from AI. Hands-on human finesse, the “haptic feedback” of human touch, fine motor skills, the ability to switch abruptly from gentle care and stroking to heavy tasks, or figuring out how to fix old piping in an old house — “that is impossible right now.”

Despite the imminent arrival of devices like Elon Musk’s Optimus robot, Moon said she doesn’t see AI changing that any time soon, meaning that the many jobs that require human judgment, instant decisions and human dexterity will continue to need humans.

In a hospital, for example, artificial intelligence can count the pills, do the paperwork and help create efficiencies in treatment. The advantage is that it will leave more time for tasks where humanity remains indispensable.

That essential humanity entailing not just what we do and how we do it, but the reasons for doing it, is encapsulated in a concept called “human centricity.” It is an approach at the core of work by anthropologist Paul Hartley, CEO of the Toronto-based Human Futures Studio, a kind of management consultancy that has helped tech companies from going off track.

“It’s an articulation of how to keep people really at the centre,” he said.

Hartley, author of the book Radical Human Centricity, said the concept predates recent thinking about AI, growing out of notions about “user experience,” or “UX,” in the technology sector where tech geeks might be tempted to wander off into the never-never land of technology for technology’s sake.

In some science fiction future, AI may eventually be able to think for itself and find its own motivations that are incomprehensible to us. But until that time, no matter how advanced, AI will remain a tool for use by humans for human purposes, Hartley said.

The essential lesson of human centricity is that technology and software tools, including AI, have no purpose if they fail to respond to human needs.

The requirements of humanity, insists Vurain Tabvuma, a professor at the Sobey School of Business at St. Mary’s University in Halifax who has collaborated with Hartley, are also at the heart of why human work will never be supplanted by AI.

Even after it becomes ubiquitous in a decade or so, Tabvuma said he foresees AI as being similar to previous technological advances that, in theory, killed jobs. Human librarians used to bring him books and articles. Now he gets them online.

Machines replaced weavers. Rooms of typists and calculators have been replaced by email and spreadsheet software. Robots have been taking the place of humans on assembly lines and in warehouses for years. But none of those changes have reduced the amount of work people do. Unemployment has never been so low, and many of us seem busier than ever.

Capitalism to the rescue

Reminiscent of the prediction by economist John Maynard Keynes in his 1930 essay Economic Possibilities for our Grandchildren that by now we would be working 15-hour weeks, Tabvuma thinks we probably won’t have a chance to put our feet up this time, either.

Some have warned that the capitalist economy will use AI against human workers, but he said that history shows the capitalist free market will guarantee future work because it will keep finding new ways to use human talent and resources. Tabvuma’s analysis echoes a statement by tech entrepreneur Jack O’Holleran in an essay earlier this month.

“If AI can do 10 times the work of a coder, the majority of companies won’t fire nine of their 10 software engineers,” O’Holleran wrote. “They’re just going to [expand to] 100 times the amount of output they can produce with their current team of 10.”

Tabvuma said it is in the nature of the capitalist economy — the constant renewal known as “creative destruction” or “churn,” motivated by a search for profits — to repeatedly eliminate routinized work and use the resources saved in that process to create new work. AI will not stop that process, he said.

“Over time companies will identify an opportunity, and over time they will start working to make the most of that opportunity,” Tabvuma said. 

And the process does not just happen in a corporate setting. Tabvuma talks to his students about the history of art and artisans going back to Greek and Roman times. On the surface, it appears that techniques for creating posters and painting using printing and photography and then computers have been progressively replacing the skills of human artisans.

“It moves away from people and firmly into the realm of technology,” Tabvuma said. But that has not eliminated artists, he said: “You look at it right now in history? We have never had more artists in the world.”

Human replacement or human helpers

Tabvuma also rejects the idea that a single corporate entity will take hold of artificial intelligence and use it to concentrate wealth and power and dominate humanity. For one thing, while it is now new and expensive, AI will become cheap and widely available to a new generation that understands how to use it. He said it will be hard for any business or sector to corner the market.

“Some of these ideas are advocated by people who believe that the world we live in is a constant and that the businesses we see are always constant, but in capitalist economies, the businesses we interact with right now are not going to exist 10 years from now, or 20 or 30 years from now,” Tabvuma said. At some point, companies like Facebook and Amazon and Apple are going to fail, he said.

“There will be other companies that come up, and if they’re coming up, they will employ people and expand their workforce, improve their technology and gain market share.”

And as for the work of writers offering you something you actually want to read? Tabvuma said as well as manual dexterity, humans have another big advantage.

“Think of the interaction you and I are having right now, the fact that you thought of ‘How am I going to write this new article? I’m going to reach out to these people and interview them, and then out of that process. I’m going to write this article,'” Tabvuma said.

“And that is not physical dexterity, it’s mental dexterity.”