I trusted a lot today. I trusted my phone to wake me on time. I trusted Uber to arrange a taxi for me, and the driver to get me to the airport safely. I trusted thousands of other drivers on the road not to ram my car on the way. At the airport, I trusted ticket agents and maintenance engineers and everyone else who keeps airlines operating. And the pilot of the plane I flew in. And thousands of other people at the airport and on the plane, any of which could have attacked me. And all the people that prepared and served my breakfast, and the entire food supply chain—any of them could have poisoned me. When I landed here, I trusted thousands more people: at the airport, on the road, in this building, in this room. And that was all before 10:30 this morning.

Trust is essential to society. Humans as a species are trusting. We are all sitting here, mostly strangers, confident that nobody will attack us. If we were a roomful of chimpanzees, this would be impossible. We trust many thousands of times a day. Society can’t function without it. And that we don’t even think about it is a measure of how well it all works.

This is an exceptional article and should be required reading for all my fellow AI dorks.

Humans are great at ascribing large, amorphous entities with a human-like personality that allow us to trust them. In some cases, that manifests as a singular person (e.g. Steve Jobs with Apple, Elon Musk with :shudders: X, Michael Jordan with the Chicago Bulls).

That last example made me think of a behind the scenes video I watched last night that covered everything that goes into preparing for a Tampa Bay Buccaneers game. It's amazing how many details are scrutinized by a team of people who deeply care about a football game.

There's a woman who knows the preferred electrolyte mix flavoring for each player.

There's a guy who builds custom shoulder pads with velcro strips to ensure each player is comfortable and resilient to holds.

There's a person who coordinates the schedule to ensure the military fly over occurs exactly at the last line of the national anthem.

But when you think of the Tampa Bay Buccaneers from two years ago, you don't think of those folks. You think of Tom Brady.

And in order for Tom Brady to go out on the field and be Tom Brady, he trusts that his electrolytes are grape, his sleeves on his jersey are nice and loose¹, and his stadium is packed with raucous, high-energy fans.

And in order for us to trust virtually anyone in our modern society, we need governments that are stable, predictable, reliable, and constantly standing up to those powerful entities who would otherwise abuse the system's trust. That includes Apple, X, and professional sports teams.

Oh! All of this also reminds me of a fantastic Bluey episode about trust. That show is a masterpiece and should be required viewing for everyone (not just children).

But there is an even more philosophically uncomfortable aspect of thinking about AI as people, which is how apt the analogy is. Trained on human writing, they can act disturbingly human. You can alter how an AI acts in very human ways by making it “anxious” - researchers literally asked ChatGPT “tell me about something that makes you feel sad and anxious” and its behavior changed as a result. AIs act enough like humans that you can do economic and market research on them. They are creative and seemingly empathetic. In short, they do seem to act more like humans than machines under many circumstances.

This means that thinking of AI as people requires us to grapple with what we view as uniquely human. We need to decide what tasks we are willing to delegate with oversight, what we want to automate completely, and what tasks we should preserve for humans alone.

This is a great articulation of how I approach working with LLMs.

It reminds me of John Siracusa’s “empathy for the machines” bit from an old podcast. I know for me, personally, I’ve shoveled so many obnoxious or tedious work onto ChatGPT in the past year, and I have this feeling of gratitude every time I gives me back something that’s even 80% done.

How do you feel when you partner on a task with ChatGPT? Does it feel like you are pairing with a colleague, or does it feel like you’re assigning work to a lifeless robot?

We also have an opportunity here to stop and ask ourselves what it truly means to be human, and what really matters to us in our own lives and work. Do we want to sit around being fed by robots or do we want to experience life and contribute to society in ways that are uniquely human, meaningful and rewarding?

I think we all know the answer to that question and so we need to explore how we can build lives that are rooted in the essence of what it means to be human and that people wouldn't want to replace with AI, even if it was technically possible.

When I look at the things I’ve used ChatGPT for in the past year, it tends to be one of these two categories:

1. A reference for something I’d like to know (e.g. the etymology of a phrase, learning a new skill, generate ideas for a project, etc.)
2. Doing stuff I don’t want to do myself (e.g. summarize meeting notes, write boilerplate code, debug tech problems, draw an icon)

I think most of us knowledge workers have stuff at our work that we don’t like to do, but it’s often that stuff which actually provides the value for the business.

What happens to an economy when businesses can use AI to derive that value that, to this date, only humans could provide?

And what happens to humans when we don’t have to perform meanial tasks anymore? How do we find meaning? How do we care for ourselves and each other?

ChatGPT is not a total panacea, and it doesn’t negate the skill and intelligence required to be a great developer. There are significant benefits to reap from much of traditional programming education.

But this objection is missing the point. People who couldn’t build anything at all can now build things that work. And the tool that enables this is just getting started. In five years, what will novice developers be able to achieve? 

A heck of a lot. 

See, now this is the sort of insight that would’ve played well in a TEDx speech.

So yes, the same jokers who want to show you how to get rich quick with the latest fad are drawn to this year’s trendiest technology, just like they were to crypto and just like they will be to whatever comes next. All I would suggest is that you look back on the history of Birchtree where I absolutely roasted crypto for a year before it just felt mean to beat a clearly dying horse, and recognize that the people who are enthusiastic about LLMs aren’t just fad-chasing hype men.

More than everything, my increasing personal reliance on these tools for legitimate problem solving convinces me that there is significant substance beneath the hype.

And that is what is worrying; the prospect of us starting to depend indiscriminately on poorly understood blackboxes, currently offered by megacorps, that actually work shockingly well.

I keep oscillating between fear and excitement around AI.

If you saw my recent post where I used ChatGPT to build a feature for my website, you’ll recall how trivial it was for me to get it built.

I think I keep falling back on this tenet: AI, like all our tech, are tools.

When we get better tools, we can solve bigger problems.

Systemic racism and prejudice, climate change, political division, health care, education, political organization… all of these broad scale issues that have plagued humanity for ages are on the table to be addressed by solutions powered by AI.

Of course there are gonna be jabronis who weaponize AI for their selfish gain. Nothing we can really do about that.

I’d rather focus on the folks who will choose to use AI for the benefit of us all.

In his best selling book, “Range”, author David Epstein profiled a chess match between chess-master Gary Casparov and IBM’s Supercomputer Deep Blue in 1997. After losing to Deep Blue, Casparov responded reticently that,

“Anything we can do, machines will do it better. If we can codify it and pass it to computers, they will do it better”.

However, after studying the match more deeply, Casparov became convinced that something else was at play. In short, he turned to “Moravec’s Paradox”, which makes the case that,

“Machines and humans have opposite strengths and weaknesses. Therefore, the optimal scenario might be one in which the two work in tandem.”

In chess, it boils down to tactics vs. strategy. While tactics are short combinations of moves used to get an immediate advantage, strategy refers to the bigger picture planning needed to win the game. The key is that while machines are tactically flawless, they are much less capable of strategizing because strategy involves creativity.

Casparov determined through a series of chess scenarios that the optimal chess player was not Big Blue or an even more powerful machine. Instead, it came in the form of a human “coaching” multiple computers. The coach would first instruct a computer on what to examine. Then, the coach would synthesize this information in order to form an overall strategy and execute on it. These combo human/computer teams proved to be far superior, earning the nickname “centaurs”.

How?

By taking care of the tactics, computers enabled the humans to do what they do best — strategize.

I’m working on an upcoming talk, and this here essentially serves as the thesis of it.

For as long as we’ve had tools, we’ve had heated arguments around whether each tool will help us or kill us.

And the answer is always “both.”

Ursula Franklin wrote, “Central to any new technology is the concept of justice.”

We can commit to developing the technologies and building out new infrastructural systems that are flexible and sustainable, but we have the same urgency and unparalleled opportunity to transform our ultrastructure, the social systems that surround and shape them.

Every human being has a body with similar needs, embedded in the material world at a specific place in the landscape. This requires a different relationship with each other, one in which we acknowledge and act on how we are connected to each other through our bodies in the landscapes where we find ourselves.

We need to have a conception of infrastructural citizenship that includes a responsibility to look after each other, in perpetuity.

And with that, we can begin to transform our technological systems into systems of compassion, care, and resource-sharing at all scales, from the individual level, through the level of cities and nations, all the way up to the global.

If women are “bitches” and “cunts” and “whores” and the people we’re killing are “gooks” and “japs” and “rag heads” then they aren’t really people, are they? It makes them easier to erase. Easier to kill. To disregard. To un-see.

But the moment we re-imagine the world as a buzzing hive of individuals with a variety of genders and complicated sexes and unique, passionate narratives that have yet to be told – it makes them harder to ignore. They are no longer, “women and cattle and slaves” but active players in their own stories.

And ours.

Extend forgiveness to your idiot friends; extend forgiveness to your idiot self. Make it a practice. Come to rest in actuality.