Seventy-five years ago this month, mathematician Alan Turing published the academic paper that is widely considered to have launched the field of artificial intelligence (AI). The paper, titled Computing Machinery and Intelligence, opened with a deceptively simple question: “Can machines think?”
Today, Turing is perhaps best known through Benedict Cumberbatch’s portrayal of him in the 2014 film, The Imitation Game, which focused on his wartime codebreaking. But it’s the philosophical questions about thinking machines, which he laid out in this paper and distilled into what came to be known as “The Turing Test” that still endure as a foundation for how we measure and debate AI today.
In simplest terms, The Turing Test was intended to show that if a machine could convince a human interrogator through conversation that it was human, it should be considered capable of thinking. The questions and debates it launched about consciousness, intelligence, and what makes us human continue to this day. Every time we interact with ChatGPT, Claude, or any modern AI interface, we are living in the world Turing imagined, where the line between human and machine intelligence has become increasingly blurred.
The Royal Society in England is marking the 75th anniversary of Turing’s paper with a major celebration, featuring computing pioneer Alan Kay, prominent AI researchers like Gary Marcus, and widely cited neuroscientist Anil Seth; all are gathering to reflect on Turing’s legacy.
Facing a different question now
For the past 14 months, I’ve been conducting my own type of reflection through my newsletter, A Short Distance Ahead, a series of 75 weekly essays covering 75 years of AI history.
Each week, I explored a single year in history to uncover how its broader context shaped the development of AI; for each essay, I used the newest AI interfaces and models to help me research and structure the essays. It’s an experimental project that I embarked on with a deep concern about the impact AI tools will have on human creativity and storytelling, while learning about the history of how they came to be. The title of the newsletter comes from the last line of Turing’s 1950 paper: “We can only see a short distance ahead, but we can see plenty there that needs to be done.”
Over the last few weeks, I’ve come to consider that 75 years after Alan Turing asked, “Can machines think?” we should be much less focused on that question and much more focused on the question of how these AI tools are changing us. In other words: “Will humans come to think like machines?”
We’ve built elaborate frameworks for evaluating machine intelligence — benchmarks, perplexity scores, economic projections. But we have no comparable framework for understanding the most critical transformation of all: what these tools are doing to human thought itself.
The studies exist, scattered across journals, tech publications, and think pieces. But they remain disparate data points, not a cohesive model for understanding our evolving relationship with AI. We’re so busy measuring whether machines can think like humans that we’re not systematically tracking how they’re reshaping us.
It reminds me of something we’re only now reckoning with: smartphones and social media, and how we handed addictive technology to kids without asking what kind of relationship we wanted them to have with it. Now we’re watching a generation struggle with mental health challenges, short attention spans, constant comparison to others, and deficits in human connection.
The solution isn’t to abandon these tools. It’s to approach them with a deliberate awareness of how they’re changing us.
We’re at that same inflection point with AI – and it’s not too late to reflect on the types of relationships we want to have with it.
To follow this more closely and understand it better, I highly recommend subscribing to Julia Freeland Fisher’s newsletter Connection Error. I‘ve only discovered her writing very recently, but she does a great job of surfacing meaningful reports and studies and is regularly “exploring our changing relationship with AI and how AI is changing our relationships.”
Take for example, a study from Upwork showing that workers using AI tools wrote differently; not just when using AI, but even when they were writing without it. Their natural writing had shifted to match AI’s patterns. It’s only one example of how we’re not just using these tools — we’re absorbing them. New research also shows that even productivity apps like TK are designed to create emotional attachment, not just efficiency. We’re building dependencies we don’t fully understand. And when Harvard Business Review warns about “workslop,” it’s not just describing reality, it’s showing us choices we’re making without realizing we’re making them.
The evidence of transformation is being written and talked about by a lot of national publications (albeit the same ones often most criticized for being alarmist about technology or formulaic in their arguments.) For example, The New Yorker has been tracking how AI infiltrates our daily routines, examining what it means when we form “relationships” with systems that only simulate empathy. It has also been reporting on companies like Atlassian betting $600 million that AI browsers like Dia — a browser that promises to “know a lot about you,” and probably represents how we’ll interact with this technology going forward.
Meanwhile The Atlantic has published a series of warnings: Tyler Austin Harper argues that AI dating concierges could erode our capacity for autonomous connection by outsourcing the work of intimacy. Russell Shaw explores why AI chatbots can never truly be children’s friends, by pointing out how they short-circuit how kids learn to handle conflict, Nicholas A. Christakis asks us to consider how AI fundamentally rewires human connection itself.
And these are just the mainstream outlets. Across Substack, independent researchers and writers (many doing more nuanced work than any major publication) track these changes daily, with the most dedicated work being done by The Center for Human Technology. But documenting these changes isn’t enough. What we lack isn’t evidence but synthesis. We’re drowning in observations while thirsting for understanding. We have no unified framework for making sense of how these tools are reshaping us, no equivalent to the benchmarks and leaderboards we use for AI itself.
Each report, each data point, each article is really asking us a question: Is this the relationship we want with these tools?
Without a framework to connect them, the transformation taking place on human behavior feels like it actually is in many ways: fragmented, confusing, and complicated.
Learning from history
So, as I often do, I’ve turned to history. The past reveals patterns, including having shown us what happens when new technologies impact our ways of thinking and offering lessons from those who recognized the transformation as it unfolded. And no one seems to have more passionately thought about this challenge than Neil Postman.
Postman, greatly influenced by Marshall McLuhan, spent his career warning us that technologies aren’t neutral tools, but instead environments that reshape how we think. I turned to his book Amusing Ourselves to Death for perspective after we first elected a reality TV show star for President. In it, Postman showed how television didn’t just change what we watched, but how we processed reality itself, by reducing complex ideas to entertainment. His key insight was that we become what we behold. The medium shapes the mind.
Postman always asked: What kind of people does this technology create?
With AI, I fear the technology is being stewarded by many people (albeit brilliant) who think like machines: optimized, efficient, statistically average, data-rich, but story-poor; all while hoping machines will become more like us.
75 years after Alan Turing, we should be much more focused on the question about how these AI tools are changing us, in other words: “Will humans come to think like machines?”
The solution isn’t to abandon these tools. It’s to approach them with what Postman called “technological resistance,” a deliberate awareness of how they’re changing us. This means working diligently at creating spaces where different logics prevail. Where efficiency isn’t the highest value. And where the statistically unlikely response might be the most human one, and therefore, the most valuable.
Here’s what gives me some hope: We still have time to shape this relationship. And although, unfortunately in the words of George Saunders, I have considerable doubt that we are “a culture capable of imagining complexly”— I still have hope.
I have hope because the real conversations that matter won’t happen in think pieces or on social media. They’ll happen in small moments: parents talking to kids about why they should write their own homework, colleagues deciding together when AI helps and when it hinders, friends admitting they feel strange about outsourcing their thoughts to machines or asking one or two more questions on a topic that they worry about not understanding enough or risk sounding too judgmental about.
It will be messy. We’ll contradict ourselves. We’ll use AI to write about not using AI (as I do in my newsletter). But that’s okay. The act of noticing, of pausing to ask “what kind of relationship do I want with this technology?” That’s where our human agency lives.
Ultimately, I’ve come to believe — maybe naively, or idealistically — that the most powerful form of media today isn’t AI or TV or social platforms or Substack. It’s, as MIT Professor Sherry Turkle has modeled, human conversation, in real life. Where we can work through these questions together. That’s the gap between the ones and zeros that matters most, because that’s where our narratives take shape. That’s where we don’t just process information, but absorb and decide on the stories that we choose to believe and tell ourselves and others. That’s where we’ll figure out how to use these tools without losing ourselves in them.
The next chapter of AI’s history isn’t just about what machines can do — it’s about what we decide to do with them.
And that story is still being written.
David Williams writes the Substack A Short Distance Ahead by using AI to help research and structure his essays. He will be speaking about the history of AI at the 1682 Conference, at the Barnes Foundation on October 8.
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