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Will AI Take Our Jobs? Exploring AI's Impact on Society
How AI Will Change Labor, Our Perception of Reality, and Economic Value Distribution
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Will AI Take Our Jobs? Exploring AI's Impact on Society
The pace of technological advancement is staggering. I’ve always liked this visualization: 5 megabytes in 1956 vs. 1 terabyte today. For context, 1 terabyte = 1,000,000 megabytes. A terabyte hard drive in 1956 would’ve been the size of a 40-story building; today, it fits on your fingertip.
Or take another example of technology’s progress: within a single lifetime—63 years—we went from the Wright Brothers’ first flight (1903) to putting a man on the moon (1969), 239,000 miles from Earth.
To connect these two examples: the iPhone in your pocket is exponentially more powerful than the computer NASA used to send astronauts to the moon. NASA’s Apollo 11 computers cost $3.5M apiece and were the size of a car. Today’s iPhone can process 3.36 billion instructions per second and could be used to guide 120,000,000 Apollo-era spacecraft to the moon, all at the same time.
(By the way, the TI-84 calculator you used in AP Calculus is 350 times faster than Apollo computers and has 32x more RAM and 14,500x more ROM.)
The point is: things are moving fast. And the pace of advancement carries over to AI. Ten years ago, AI struggled to tell the difference between a cat and a dog. Today, AI can not only classify, but generate detailed images of cats and dogs from a text prompt. Models are improving rapidly. From last week’s piece, here’s a side-by-side of Midjourney’s output to the same prompt—“Barack Obama and Donald Trump playing basketball”—from 12 months ago vs. today:
People are using AI to write code and to design their apartments, to draft 4,000-word essays and to craft poetry. A new peer-reviewed study found that AI can now diagnose diseases better than 72% of general practitioner doctors.
The pace of AI has led some, including Elon Musk and Apple co-founder Steve Wozniak, to publicly call for a pause in training models more powerful than GPT-4. A good Times piece on OpenAI’s Sam Altman summarized the controversy: “Some believe [AI] will deliver a utopia where everyone has all the time and money ever needed. Others believe it could destroy humanity.”
The reality, in my mind, is that AI will neither deliver utopia nor destroy humanity. The future instead lies somewhere in the murkiness between those poles. What fascinates me are the societal ripple effects that come from technological advancement. I’ll tackle three timely questions about AI this week, in a follow-up to last week’s “State of the Union” on AI applications.
The first question: is AI coming for our jobs?
What’s interesting about the AI revolution is that white-collar jobs are the jobs most at risk. The Industrial Revolution disrupted blue-collar work, but many people believed that knowledge work would always be safe; after all, how could a machine replicate human creativity? New reports from OpenAI and Goldman Sachs are sobering: OpenAI estimates that 80% of the U.S. workforce will be impacted by AI, and Goldman predicts 300M jobs are at risk of automation. Below, we’ll examine how things might play out.
The second question: what is a reality in a world of generative AI?
The Pope went viral last week for wearing a stylish, puffy white jacket. The only problem: the image was completely fake. Buzzfeed News called it “the first real mass-level AI misinformation case” and tracked down the guy who made the image, Pablo Xavier from Chicago. In a you-can’t-make-this-stuff up moment, Pablo Xavier revealed that he was tripping on mushrooms when he decided it would be funny to dress the Pope in a Balenciaga puffer coat.
Generative AI blurs the reality between fact and fiction, which will have ripple effects for trust and misinformation in society. Below we’ll unpack that in more detail.
And the third question: where does value accrue in AI?
Goliaths like Microsoft and Google seem poised to reap the economic gains here; do startups stand a chance, and what does defensibility look like in the world of AI?
First up, AI and our jobs 👇
Will AI take our jobs?
Here’s how I see AI’s impact on work unfolding:
Yes, AI will render many jobs moot. Knowledge work will be most affected.
This will happen more slowly than we expect.
Many lost jobs will be offset by new jobs, as has historically been the case.
There will still be near-term pain, and worker retraining is essential.
A utopian future that’s free from labor is a pipe dream. In 20 years, we’ll be working just as much as we are today (sorry!) but with higher productivity.
Let’s unpack this in more detail.
OpenAI’s recent report on jobs—produced in tandem with the University of Pennsylvania—found that 80% of the U.S. workforce could have at least 10% of their tasks affected by the introduction of GPTs, the large language models made by OpenAI. About 19% of workers, meanwhile, will see at least 50% of their tasks impacted by GPT.
In a reversal from the Agricultural and Industrial Revolutions, high-income jobs are most at risk. The more your job touches atoms and not bits, the safer you seem to be: OpenAI and Penn list the industries most at risk to be data processing, information services, publishing, and insurance; the industries expected to be least impacted are food manufacturing, wood product manufacturing, and, ironically, agriculture.
The Goldman report has similar findings. Goldman summarizes:
Using data on occupational tasks in both the US and Europe, we find that roughly two-thirds of current jobs are exposed to some degree of AI automation, and that generative AI could substitute up to one-fourth of current work. Extrapolating our estimates globally suggests that generative AI could expose the equivalent of 300M full-time jobs to automation.
Automation stems from the fact that AI is becoming better than humans at uniquely-human things. The solid lines here show how AI performs against human benchmarks (the dotted lines) of image classification and reading comprehension:
Here’s a visualization of the industries that Goldman expects to be most-impacted. Administrative office work, legal work, and architecture & engineering lead the way.
We’re already seeing startups tackle these areas. To take the top two categories: Xembly is a startup that gives everyone an automated executive assistant to schedule meetings and summarize calls, while Harvey serves lawyers, helping with contract analysis, due diligence, litigation, and regulatory compliance.
For comparison, here’s the extent of automation Goldman expects across industries:
If you work in maintenance, repair, or construction, you’re pretty safe. If you work in admin or legal, you have a ~40% chance of being replaced by AI.
Yet I don’t expect many jobs to be replaced in the near future. Historically, organizations are slow to adopt new technology. They’re skittish and they’re bureaucratic. I see AI acting as a companion rather than a replacement, at least for a few years. Most paralegals can probably breathe a sigh of relief in the short-term; in fact, their jobs might become easier, with the benefit of a powerful sidekick to make work faster and less monotonous.
It will become second nature for us to use AI as a tool in our work, in the same way that using the internet has become second nature. It will become silly for software engineers to not use GitHub Copilot to code faster, for consultants to not ask ChatGPT to brainstorm solutions for problems, for interior designers to not lean on image models to generate mock-ups to show clients.
In the long-term, though, things will be a different story. Many jobs will likely fade away over the next 10 or 20 years. And those physical-world jobs aren’t safe either—robots will automate many of these jobs in the same way that language and image models automate knowledge work and creative work.
Many modern jobs will soon seem like distant memories. We’ve seen the same patterns in past technology epochs. Before there was automatic equipment to reset bowling pins, “Bowling Pin Setter” was an actual occupation. Ditto for “Lamplighter”—Lamplighters walked on average 10 miles per day, lighting lamps before dusk and distinguishing them at dawn. Will jobs like Executive Assistant, Copywriter, or Accountant follow the same extinction?
The silver lining: worker displacement from automation has historically been offset by the creation of new jobs. We’ll see jobs emerge that we can’t even think up yet (“Prompt Engineer” seems to be an early, and lucrative, example). Who would have thought 30 years ago that jobs like Cybersecurity Analyst, Database Administrator, or Machine Learning Engineer would exist? Let alone Social Media Manager, SEO Consultant, or Discord Community Manager?
This being said, there will be significant short-term pain along the way. Worker retraining is crucial. Startups like Guild Education and Outlier have made upskilling more accessible with elegant business models (Guild, for instance, gets employers like Disney, Walmart, and Taco Bell to pay for education as an employee benefit because doing so reduces worker attrition), but we need both more venture-funded solutions and more government-funded policies. Effective retraining in the face of AI disruption will require a remodeled education system, which I’ll tackle in a future piece.
Some people believe that AI will deliver us to a future free from work. In an interview with Semafor this week, Vinod Khosla said: “This large transformation is the opportunity to free humanity from the need to work. People will work when they want to work on what they want to work on.” Unfortunately, I see things playing out quite differently.
In 1930, the economist John Maynard Keynes predicted that his grandkids would work just 15 hours a week. He imagined that by the 21st century, we would work Monday and Tuesday, and then have a five-day weekend. What Keynes missed was that work is as much a product of culture as it is of technology. And work has increasingly become the foundation of culture; as Derek Thompson put it so elegantly: “Here is a history of work in six words: from jobs to careers to callings.” It’s easy to give up a job; it’s harder to give up a calling.
Barring a cultural reckoning around work, I feel strongly that we’ll be working the same number of hours in 2043 as we are in 2023. Human labor used to be about subsistence—farming, to get enough to eat. Now work has moved up Maslow’s Hierarchy to fill our needs for community and self-actualization.
AI will make us more productive, yes. Goldman’s report estimates that AI will eventually increase annual global GDP by 7%. We’ll all get superpowers that make us better at our jobs. We’ll get a lot more out of those 40 hours a week, operating collectively on a higher plain. But I expect that we’ll still be working those 40 hours.
What is reality in a world of generative AI?
My friend Miles Fisher looks a lot like Tom Cruise. People have noted the resemblance for years. But only recently did Miles become Tom Cruise.
Miles is the man behind @DeepTomCruise, a popular TikTok account (5.2M followers) that puts out viral AI-powered videos of Miles embodying Tom Cruise. Here’s Miles before and after overlaying his Tom Cruise deepfake, an effect created by using deep learning to transpose Tom’s face onto Miles’s:
Miles is now behind a company called Metaphysics AI that powers visual effects for film studios. Metaphysics is currently working on de-aging Tom Cruise and Robin Wright for an upcoming Robert Zemeckis (Forrest Gump, Back to the Future) film.
The interplay of generative AI and reality is fascinating to think about.
On the positive side, it’s exciting to think about how this can revolutionize entertainment. Don’t like Robert Downey, Jr.? What if you could watch Avengers with Tom Cruise playing Iron Man instead? Or better yet, what if you and your friends could be The Avengers and watch yourselves fight Thanos?
In this future, we can enjoy modern films that star Marilyn Monroe and experience a Beatles concert in our own living room. As NVIDIA’s CEO Jensen Huang recently said: “Every single pixel will be generated soon. Not rendered: generated.” To use one example of what’s already-possible, last September Metaphysics used a deepfake to have Elvis perform on America’s Got Talent:
This is cool stuff; it can revolutionize entertainment and education. (Imagine being taught about the Emancipation Proclamation by Abraham Lincoln himself.) Startups are already working on this: Character AI lets you chat with a facsimile of virtually anyone, living or dead. Want to talk sonnets with William Shakespeare, or learn about rockets from Elon Musk, or just shoot the shit with Billie Eilish? You can do that.
But generative AI is also a double-edged sword. In a world where anything can be conjured, how do we know what’s real? One worry here is a world brimming with misinformation and void of trust. We used the example of the Pope’s puffer coat earlier; that’s pretty innocuous. But there have been recent examples that were more troubling. When news hit that Trump might be arrested last month, AI images depicting Trump’s arrest went viral. Many people thought the images were real, confusing fabrication with news.
We’ve seen this play out before. Remember the Nancy Pelosi deepfake that made the then-Speaker sound slurred and drunk? Thousands—maybe millions—of people probably still think that it was real. More recently, a fake video went viral that depicted Elizabeth Warren saying that Republicans shouldn’t have the right to vote.
The lines between fact and fiction are blurring. In one of the most bizarre examples, people on Reddit are making up stories of events that never happened. The below images from Justine Moore look real, and they’re supposedly from the Great Cascadia Earthquake that devastated Oregon in 2001. But that earthquake never happened; that event doesn’t exist. The images are AI-generated, used online to spread the story of a completely-fabricated natural disaster.
Language models also make things up. A model will occasionally “hallucinate,” confidently stating something that is inaccurate or even potentially dangerous. Earlier, we used the example of how some AI models are outperforming doctors in medical diagnoses. But what about when the AI is wrong? What about when it repeatedly insists that it’s correct, even when it’s not? Most people assume that these systems are sophisticated and intelligent. It’s likely that less-privileged groups—say, uninsured people who can’t get a professional medical opinion—will be hurt most here.
How will all this evolve?
One outcome may be that everyone becomes more skeptical and distrustful of news—you literally won’t be able to believe your eyes. A more hopeful effect may be some sort of “watermark” on AI-generated content—something that can tell us whether a paragraph or image has the fingerprints of AI on it. This is one area for more innovation as AI goes mainstream. Some early, though imperfect, solutions exist. Turnitin, a plagiarism checker, claims its tool can detect AI-written text with 96% accuracy, compared to OpenAI’s 26%. The downside, of course, is that some students may wrongly be accused of cheating.
Misinformation and AI-generated content will also fall to the responsibility of social platforms—check out Twitter’s disclaimer under this (fake) image:
The most-likely outcome here is a little bit of everything: developers working hard to reduce hallucination; platforms trying to minimize the spread of AI-instigated misinformation; and, unfortunately, a growing distrust of reality.
Who will capture the value being created?
In technology—and in business—distribution often wins. Look no further than the battle between Microsoft Teams and Slack; Microsoft neutralized Slack’s momentum by adding Teams to its Office 365 suite:
Or think of Oprah’s famous comment onstage at Apple TV+’s launch, succinctly summarizing why Apple would immediately become formidable in the streaming wars: “They’re in a billion pockets, ya’ll.”
That’s the pessimistic argument for how AI’s value creation will ultimately be distributed. Microsoft is building AI into its Office 385 suite, used by over a million businesses around the world; Google is building AI into Google Workspace, which now counts 2 billion monthly active users. How does a startup compete with that?
The optimistic view is that we’ve seen this movie before. When mobile took off a dozen years ago, much of the value accrued to Apple, Google, and Facebook. Of Apple’s $394B in 2022 revenue, 52% came from iPhone sales; as a standalone company, the iPhone would rank ~20th in the world by revenue, larger than AT&T, Microsoft, and Chevron. Google and Facebook, for their part, monopolize mobile advertising with over 50% combined market share. But mobile also gave rise to net-new companies: Uber leveraged geolocation to become a $63B public company; Instagram used mobile cameras and TikTok used mobile video to amass 2B and 1B users, respectively (and likely $100B standalone businesses); and the pattern continues across sectors—Spotify in music, Robinhood in investing, WhatsApp in messaging, and so on.
It’s the same story in cloud: much of the value was vacuumed up by Amazon’s AWS, Microsoft’s Azure, and established companies like Salesforce. Yet we also got dozens of new startups. Just look at the Cloud 100.
I expect AI will play out the same way. OpenAI may be the AWS equivalent here, an infrastructure layer upon which others build; perhaps Anthropic is Azure or Google Cloud in this analogy. But we’re seeing massive changes in user experiences and interfaces. Even Google Search is at risk. This creates an opening for new entrants at the application layer that are AI-native.
The go-to moats in technology, like network effects, will still apply. Focus will be key. One reason startups win is that they’re specialized to start. They build 10x-better products, often for a highly-specific user to begin with. To return to examples above, focusing on AI executive assistants or AI lawyers might be an advantage; better specialization fine-tunes both the AI models and the business models over time, creating compounding advantages. Startups can then layer on products over time to drive up contract values and improve net dollar retention.
A final advantage startups have: agility. Big Tech moves slowly. Earlier this year, four leading AI researchers left Google to found Mobius. These researchers were behind Google’s text-to-image diffusion models. I’m confident they’ll be able to move faster and more nimbly outside of Google than within it. Similarly, Dust is a platform for large language model applications founded by former OpenAI employees; the same benefits from speed and flexibility will exist there. These are critical advantages that entrepreneurs can capitalize on.
Yes, value will accrue to Microsoft and Google. The big will get bigger. But there will also be plenty of opportunities (many hard to predict right now) for emerging players to be AI-native and to create enormous value over the next decade.
In the 1980s, Dunkin’ Donuts released a famous commercial that came to be known as “Time to Make the Donuts.” In the ad, a middle-aged man drags himself out of bed in the wee hours of the morning, puts on his Dunkin’ Donuts uniform, and groans, “Time to make the donuts.” He shuffles out the door, and the commercial cuts to him returning home, bone-tired and muttering, “Made the donuts.” The cycle repeats again, and again, and again. The commercial was so popular that Dunkin’ made over a hundred different versions.
When we think of technology replacing work, we think of this kind of work—monotonous, thankless tasks. And traditionally, this is the type of work that innovation has targeted. In the early years of America’s manufacturing boom, one worker on Henry Ford’s assembly line complained:
“It don’t stop. It just goes and goes and goes. I bet there’s men who have lived and died out there, never seen the end of that line. And they never will—because it’s endless. It’s like a serpent. It’s just all body, no tail.”
Much of that assembly line is now automated. This came at the cost of some jobs, yes, but it also freed up workers to focus on more fulfilling, interesting work.
What’s so interesting about this moment in time is that it’s the humanness of the work that’s being disrupted. AI can now do many knowledge worker tasks better than we can; one study even showed that AI is already as creative as we are. As a result, generative AI will create a dislocation in the labor markets. It might take some time to show itself, but long term, millions of jobs will either be complemented by AI or outright substituted with it. Worker retraining will be essential.
We’re also going to see generative AI continue blurring the lines of fact and fiction, requiring a healthy dose of skepticism from everyone. We’re still in the top of the first inning here, and thoughtful policy, moderation, and innovation will be required to ensure that rampant AI-generated misinformation doesn’t wreak havoc.
When it comes to value creation, the jury is also out. My take is that Big Tech will reap many rewards, but that there’s plenty of value to be created and captured by upstarts. AI’s application layer is barely getting going, and this is a unique moment for new user experiences and behaviors to calcify. When that happens, big companies usually follow.
It’s the optimistic view that keeps me energized: AI is augmenting what humans are capable of. That said, it’s a scary time. Innovation is scary. Change is often met with backlash. Even the humble calculator faced headwinds:
Innovation is somewhat predictable in its patterns of adoption and disruption. We’ve seen major revolutions before—in agriculture, in industry, in information technology. Expect near-term pain while we adjust to the AI revolution, but long-term gains in productivity and quality of life.
Sources & Additional Reading:
I enjoyed Derek Thompson’s recent writings about AI and work
Related Digital Native Pieces
Last week’s Part I: The New Avengers: How AI is Giving Us Superpowers
Here is January’s deep-dive into AI: AI in 2023: The Application Layer Has Arrived
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