To really answer this question we have to get away from the narrow lens of the past half century-to-century and approach this from a historical lens. What was the last “big edge” that humans had that a new technology took away? And how did it play out? Who were the winners and losers? And how can this frame the impact of AI over the next century.

The first thing that I can think of is labor.

The steam engine replaces labor as an “edge”

Pre industrial revolution, human labor was what drove society forward. The human wonders of the world - from the pyramids of Egypt to the Taj Mahal - were built on physical labor. While the steam engine in the 18th century was a step change in productivity and what “human labor” meant, it wasn’t an elimination of labor entirely.

However, in the 20th century, with the invention of the internet, the mind became the new “workhorse.” Calculators, then excel, automated calculation. Smartphones and computers become productivity multipliers. Google took knowledge and placed it at everyone’s fingertips.

“The steam engine replaced muscle.”

Both these revolutions materially impacted society and what was considered the human “edge.” Whereas pure physical strength was relevant and valued pre-industrial revolution, post industrial revolution human labor was no longer the singular constraint. Humans could leverage skills and machines.

This further led to changes in how humans operated and organized. We created governments, corporations, and legal systems. This needed lawyers, accountants, engineers i.e. knowledge work. With the internet, physical skills became even less important and the “edge” pivoted even further to intellect.

AI replaces the current “form” of intelligence

Over the last decade, we have seen a new revolutionary technology - artificial intelligence. AI began as machine learning - spell checkers, search algorithms (i.e. Google), GPS. Now you have LLMs that can think, analyze, write, compose art and music. And slowly, we are seeing LLMs move deeper and replacing more.

Tech and venture leaders alike are warning that knowledge-work is in for a rude awakening. Anthropic co-founder Dario Amodei forewarned in an essay that LLMs could take over ~50% of starting-level white collar work over the next several years.1

There are, I believe, two ways to think about AI. The first is that it is the next stage of humans leveraging machines to improve productivity and scale. The second is that it is “replacing” the current form of intellect as the human edge. The honest answer is that it is probably a bit of both. I don’t believe the two views are mutually exclusive.

AI will not replace “humans”

There’s a debate on the “extent” of AI and the big concept of “AGI” i.e. a “super human” intelligence. However, this is a moving goalpost and many would argue we are already there. In many domains today, AI easily passes the “turing test.” Many people struggle to detect AI writing, art, music. There are rumors that AI code is a meaningful percentage of code today.

In fact, AI far surpasses “individual” human intelligence in many domains if we define intelligence as pure knowledge, thinking, or analytical ability. While a trained cardiologist may have a better understanding of the heart than an LLM, an LLM has a better understanding of the heart than the average person that is not a cardiologist.

AI further surpasses many humans in terms of writing capability, creative ability, and even emotional awareness and empathy — or at least the show of it. That said, to define an LLM as “human” is both under-rating its capabilities and over-rating them. Andrej Karpathy has called LLMs “ghosts” or a different species which I believe is the most accurate definition I’ve heard.

What LLMs can do (honestly)

We must ask ourselves - what is it that an LLM cannot do. And in that question, I believe we may find the “edge” we are searching for. First, though, we can take out the things it can do. And the roles that are “at risk.” This includes:

• Repetitive knowledge work: medical coding, service/call centers

• Knowledge-based advice: therapy, medical advice, legal advice, accounting

• Analysis: entry/mid-level white collar jobs in finance, law, engineering, academia, marketing

• Writing: journalism, blogging, coding, author

• Creating: artist, poet, writer, creator, marketing, actor, producer

Now, the next stage of this is understanding the “level” at which AI can “aid” vs. “replace.” This is, I believe, harder to really assess given the pace of change of the technology and the lack of consistency across roles and expectations. For example, one could argue that the mediocre writer will be replaced but a great writer will never be replaced.

However, one could also argue that there will be a new “kind” of writer, one who has great ideas but lack of ability / language barriers held them back from getting their ideas across effectively. One could even imagine society facing back-lash on AI writing and reverting to preferring people that write “normally” and imperfectly vs. what could be considered “ai slop.”

Human preference and culture is so hard to predict but I go back to my initial point - that some of writing - and writers - will likely be replaced and the writing that remains will likely have an ability to leverage LLMs to improve quality, productivity, scale. Will they be the same writers of today is harder to say.

What LLMs cannot do (honestly)

Now on to the important question, if these are all things that LLMs can do, at some level, what is it that an LLM cannot do?

• Experience things - human reinforcement learning is an attempt to “teach” LLMs from experience but it is not true “experience”

• Human contact - LLMs cannot touch, feel, taste

• Agency - LLMs, as of yet, do not have agency unless given it explicitly. You cannot “trust” an LLM

• Taste - I think of Anu Atluru’s essay on “taste.” In a world where knowledge becomes commoditized, “taste” and preference becomes the real edge

• Morality and ethics - for all you can “train” an LLM on morals and ethics, it is hard to convince ourselves that an LLM can be intrinsically moral or ethical.

• Existential - an LLM is not human and cannot die. Nor can it be alive.

I believe that we as a society have spent a lot of the last century over-indexing on pure verbal-analytical IQ. From standardized testing as prerequisites to institutions of higher learning to higher paying jobs given to people with the best GPA. From math Olympiad to chess championships, one type of intelligence has been our metric for reward.

However, there are a lot of types of intelligence. As one type of IQ becomes commodified, and creative work becomes less differentiated, these different types of intelligence - the ones that remain strictly human - may become more highly valued. Now, how do you value “existential” intelligence is a different thing.

Work is an economic concept not reliant on a single edge

Ultimately, we forget that when we fear AI is taking over the human edge, what we’re talking about is human “value.” The only reason knowledge-work has been valuable so far is because we have given it value.

It is a little odd to think of work and productivity as reliant on “knowledge” or “creativity.” We once had a society that relied almost exclusively on physical labor. The origin of work was never “knowledge” but rather a payment of goods from one party to another to get what needs to be done.

Work is simply a function of how we’ve organized society where economics are shared. As we cycle away from “knowledge” as an edge, the other IQ edges will become “work.” I don’t think AI will necessarily impact that “work” exists but rather what will be considered profitable or less profitable kinds of work.

I would think that the work that has become less profitable with the entrance of the industrial revolution or the internet, will revert to becoming more profitable - deep thinking, meaning-making, and somatic intelligence.

Perhaps philosophy will become highly valued again.

A lot has been written about humans vs. machines. Artificial intelligence has made the distinction less clear and at the same time, more important to understand. People often forget that AI, despite its eerily human capability, remains a technology. And technology is a type of human innovation. And all human innovations typically follow the same basic tenets: they will survive as long it is evolutionarily beneficial to enough humans.

Technology is a tool created by humans

We sometimes think of technology as “outside of the natural.” Machines, computers, and tech are otherworldly, mechanistic algorithms that do complicated tasks. However, at its most basic, technology is simply a tool created by humans. The definition of technology is “the application of scientific knowledge for practical purposes, often involving tools, machines, and systems.”

The history of technology in this sense goes beyond computers and machines. The first technology was the wheel. Airplanes, trains, and the steam engine all represented leaps forward for scientific application. Phones, telecom lines, and antibiotics were necessary precursors to the technological innovations of today.

Technology is a form of innovation

Over time, technology has also become synonymous with innovation. Innovation, while often enabled by new tools or technologies, goes beyond just technology and represents a new way of looking at or doing things. Innovation is not limited to things but also includes institutions i.e. governments or financial institutions.

The pyramids of Giza or the New York skyline were all forms of human innovation. EDM or impressionist art were innovations. Innovations additionally include concepts such as capitalism, communism, and democracy. They include the concepts of money, foreign exchange, and inflation. Innovations represent a key driver of productivity, growth, and improvement in life.

Over the last few years, however, technology or machines have been the guiding innovations driving society forward. From the internet to the mobile phone to self-driving cars, technology has enabled structural changes in how we work, eat, live, and pray. Artificial intelligence appears to be one of these generational innovations.

Innovation exists within the human context not outside of it

However, it is important to remind ourselves that innovations are typically created for a reason or purpose. They do not come out of thin air but often after years, decades, generations of work and resources. The rocket was created because humans wanted to conquer space. The moon landing was achieved to show national domination. Capitalism was formed to create prosperity.

That said, not every innovation is intentional and the results of innovation are not often known beforehand. Penicillin was invented accidentally and went on to become one of the core drugs to save millions of lives. The atomic bomb / nuclear weapon was created to end World War II and has become a symbol of peace and deterrence.

“Almost no prerequisite to any major invention was invented with that invention in mind.” ~ Kenneth O. Stanley, Why Greatness Cannot Be Planned: The Myth of the Objective

The human context of artificial intelligence

To understand AI we must understand why and how it was created. AI’s origin story is complex with early connections to the tech giant Google, leading academics in Stanford, and promising entrepreneurs at YCombinator. The initial goal of OpenAI was to keep AI - or AGI - out of the hands of corporate giants like Google.

That said, the success of OpenAI and chatGPT in November 2022 was a surprise, even to the company itself. No one could have predicted the virality and adoption of AI. No one could have predicted that LLMs would spark billions of dollars of investment to build out infrastructure, compute, and applications of AI.

Fast forward to today, OpenAI is far from the only company competing in the AI race. From the large tech companies to new start ups to existing businesses, there is a rush to implement AI. The term AI has gone from the world of academics to every day use. We’ve also started to hear a lot of heavy debate from policy to ethics on whether AI is good or bad for society, which often happens when a new technology seems powerful.

Innovation is not inherently good or evil

There are a couple extremist views on technology and innovation. On one hand, there is the techno-optimist view: the world’s hardest problems from biological breakthroughs to climate change will be solved with technology. On the other hand, there is the techno-pessimist view: technology is bad. Change will inevitably lead to negative impact over time. Humans are better off without new innovation.

The reality is, innovation is not inherently good or evil. Innovation happens usually for a couple reasons: 1) it will help those who create it or 2) the people that create it believe it will help other people / humanity. Even if created with good intentions, few innovations remain in the world of positive for long.

Sam Altman has said publicly that the ultimate goal of AGI is to solve the world’s hardest problems and cure disease. However, OpenAI’s initial creation was a consumer chatbot used by millions of people around the world. Alongside the positive benefits, we’ve already been to see some of the negative effects of chatGPT.

From chatGPT psychosis to copyright infringement, chatGPT has created a whole new world of ethics and consumer protections that likely need to be addressed. On the other side, doomers of AI continue to cite deep fears of artificial intelligence going rogue and destroying humanity. This is seen in sci fi movies like Origin or Robots.

In our excitement, we often forget that technology adoption is driven by emotion as much as it is by logic.

Fear and greed drive innovation as much as logic and thinking.

Every time a novel technological innovation happens, there is a tendency to follow the same cycle: 1) denial, 2) fear, 3) acceptance, 4) greed, and 5) over-reliance. Technology cycles are not dissimilar to market cycles in that human emotions play a larger role than people think.

Greed, and fear of missing out, often drive the greatest early investments into new technological innovation. While innovations often start off idealistic, a capitalist system does not support an innovation that does not make money or establish power. The success of the biggest tech giants today, Google and Meta, was directly tied to their ability to monetize a large distribution base with ads.

When it comes to AI, it’s not a battle of man vs. machines, but rather man vs. man. While many ask, will machines destroy us? The better question to ask is whether there is an incentive for certain humans to create a machine that will create more harm for humanity than good? If so, can it be monetized?

Morality doesn’t win, survival does

Humans create these things then worry whether the things they created were for good or for evil. Morality doesn’t necessarily win in the end but survival does.

Communism - and the soviet union - broke because it did not make economic sense for most humans. Dictatorships like Iran or South Korea survive because enough power in the strong and enough economic growth among the broader people ensure there is support for the ruling class.

Similarly, technologies like mobile, social media, and now artificial intelligence, will survive so long as there are enough profits to be made and people to benefit that offsets the harm. Thinking of technology as separate from humans is like thinking of democracy as separate from humans or banks as separate from humans.

We create things because they are useful to us. If they remain useful enough, they stay around. If they are no longer useful to enough people, they go extinct. Holding back technological progress is like holding back evolution. There is no path but to move forward. AI is unlikely to be an exception.

There is a view that your real thoughts are the ones you do not feel comfortable sharing. The snarky thoughts. The things you do not want your employer or potential dates to know. Or at least not without getting to know the real you.

People make “anon” accounts on X and ask uncomfortable questions on Reddit behind a no-name username. While it makes sense that most of us are afraid of being truly ourselves, it’s not clear that posting anonymously online creates the right incentives to be authentic.

It is difficult to determine when someone is authentic but perhaps let’s approach this by ruling out when someone is not authentic.

Malcolm Gladwell explores the topic of the real self in his book Talking to Strangers. In an extreme example, he talks about a teenage guy that committed a crime while blackout. The guy had a pristine record otherwise and had very little memory of the experience. Was he a criminal deep down? Did he do it intentionally on some level? His subconscious and conscious motivations were explored in a drawn out court case.

Gladwell concludes that alcohol impacted his mind and shut off his rational thinking brain. Actions done in this state should not be considered intentional or a reflection of his true self. This leads to an interesting point: without rational thinking, you cannot be authentically yourself. In extremes, intoxication leads to inauthenticity.

If you post anonymously, are you behaving as rationally as you would if you were not anonymous? Or is it akin to being intoxicated?

There is a story called the “Myth of Gyges” in Plato’s Republic. A man receives a ring that makes him invisible. He is a decent man but given the ring, he commits horrendous acts. The moral is that many people would commit acts of evil if they could get away with them. However, this is not necessarily less logical. It makes rational sense that we behave differently with certain consequences vs. others.

Being anonymous, then, may not make us less rational but it likely does change our behavior. Without consequences, humans tend to act differently. Posting anonymously online is arguably similar to wearing the “Ring of Gyges.” You are invisible to your social environment - family, friends, colleagues. You can be whoever you want to be.

Does anonymity always lead to negative actions though? There was a 1969 Stanford study showing a link between anonymity and abusive behavior. In Zimbardo’s study, female participants were dressed in lab coats and either hoods or no hoods. Each was told to give an electric shock to an individual. Those in hooded lab coats were 2x as more likely to comply.1

A body of scientific study on anonymity showcases a “tendency for many people to act rudely, aggressively, or illegally” under anonymity. In another 2016 study at Gettysburg College, college students that were anonymous were more likely to engage in cyberbullying behavior or approving of it in others.2

When people behave rudely online under an anon account, does it count? Going back to the criminal law example, if someone commits a crime under the influence, the law focuses on “recklessness” or whether the crime was impulsive enough to offset an intoxicated state. Perhaps, a similar test can be applied to internet anons. Are these individuals behaving in a “reckless” manner that it does not matter they are anon?

On the flip side, there has also been support showing how anonymity enables people to be more honest. From anonymous surveys to whistleblower regimes, anonymity reduces the consequences of societal pressures for honest disclosure. Anonymity on the internet offers opportunities to support others around sensitive topics i.e. abuse. Anonymity can make people unusually “forthcoming and helpful.”3

There is also another angle to anonymity which is group anonymity. One study shows that when primed with positive information, anonymity allowed individuals to be more helpful in a group situation. However, the studies around negative group behavior are the most disturbing. They range from aggressive driving when people have windows tinted to unethical behavior in anonymous chatrooms. 4

A stark and painful but well known study on this is the Stanford Prison Experiment. In this experiment, a group of the most amiable guys were chosen to do an experiment. They were separated into prisoners and guards. The crimes were so bad and unethical the experiment, expected to go on for 2 weeks, ended in 6 days.5

What does this all mean for anonymity online? It is not surprising the same group dynamics that happen in life happen online, but under the cover of even greater disassociation. It is possible that some authentically kind people behave even better under anonymity. There could be positive social spirals from good deeds online. However, the potential for aggressive and unethical behavior is scary.

The theory that anonymity leads to more authentic behavior is - on the whole - hard to defend. Research suggests anonymity creates a lot of perverse incentives for behavior and it is impossible to disassociate the positive from the negative. Anonymity online in groups is especially risky.

Is it fair to conclude that given the results of anonymity experiments, people on average, are authentically more unkind or aggressive? It’s hard to really say. A big part of who we are is a part of a society or community. Perhaps, the deeper conclusion is that, in life or online, authenticity is dependent on social norms and our individual identity.

Being authentic online is similar to being authentic in person - with its limitations and challenges. There is no easy way to being yourself.