Clearly Intelligent

I think about AI every day. It’s often difficult to orient the sheer volume of thinking I do about AI in a structured way. When I do, they come out in the form of a blog post here at Clearly Intelligent. The New York Times conducted a panel with eight AI leaders to grill them about their thoughts on AI. So, in today’s post, I’ve got my structure determined for me. I’m going to wishcast a little bit and put myself on the panel. Maybe I’ll get there by the 2030 iteration of this event. Thanks to the New York Times for doing this, and please don’t copyright strike me here – I’m a paying subscriber using this content fairly, and my small but fervent group of loyal readers will be quite upset if they cannot get my takes.

Roster

Bio snippets are straight from the NYT. The participants for this panel were:

Melanie Mitchell – Computer scientist and professor at the Santa Fe Institute

Yuval Noah Harari – Historian, Philosopher and author

Carl Benedikt Frey – Professor of AI and work at the University of Oxford

Gary Marcus – Founder of Geometric.AI (acquired by Uber) and author of “Taming Silicon Valley”

Nick Frosst – Co-founder of Cohere, an AI startup

Ajeya Cotra – AI risk assessor at METR, a research nonprofit

Aravind Srinivas – Co-founder and CEO of Perplexity, a chatbot search engine

Helen Toner – Interim executive director of Georgetown University’s Center for Security and Emerging Technology

I’m going to present the questions and answers from the piece and give my thoughts on the responses of the panelists, as well as my own answers to the questions.

AI 5 Years in the future – Panelists give their biggest bet about the future of AI in 5 years

Less than a 1% chance of happening. Probably like 0.01% (That’s one in 10,000 simulations of the next five years.) There are so many things that need to happen in order for countries to consider AIs legal persons, and I am very confident that all of the necessary conditions for this will not happen in five years. In my opinion, in order for legal personhood to even be considered for AI, characteristics like consciousness and sentience will have to emerge and also be proven. Scientists and philosophers do not have an agreed on definition of these characteristics, nor do they have agreed methodologies for testing for the presence of these characteristics. I doubt very highly that consciousness and sentience are both rigorously defined and agreed on in five years, and highly doubt they will emerge in AI systems within this short timeframe. Maybe in five years there is a chance that AI gets personhood in the same way that corporations are “people” because of Citizens United, but I don’t think that this is what Harari means.

Agreed. AI won’t have cured cancer or solved physics. But can all cancers be cured? Does making most cancers much more treatable count as a cure? Can all physics be solved? AI will help push the limits of scientific exploration, and I think will have made meaningful contributions to the fields of medicine and physics by 2031. But these absolute milestones will not be hit.

Yes – as stated previously, AI for science will be a big deal – more on that later in the post. I actually used AI to help me do some research for summer camps for my kid this year and it was helpful in replacing a lot of Googling and other menial tasks. In 2031 I do think that you’ll be able to say to Gemini – find and reserve a spot for my kid at a summer camp. Here are her interests. Give me a couple of options, let’s have a back and forth, then do it. Now will a parent have to call to confirm, or sign some paperwork – probably. But I hope (and think) that we will have the technical capability to do this in five years.

Yes, AI will be used to describe so many different product features that it will cease to be very distinct from other aspects of digital technology. Under the hood, or in the interface, AI will play a huge role. I’m hopeful that we see some new and interesting design and interaction pathways that take advantage of AI technologies to reimagine how we use digital tools. I don’t think that I agree with the characterization that “the most banal use cases [will] have the most transformative impact.” It’s very possible that as AI capability and adoption both increase, and the technologies diffuse through the economy, that some root node problems are addressed that have meaningful impact, enabling new ways of solving problems that have previously been intractable. If you look at the area under the curve, yes, it might be that bundling up the banal use cases like automating insurance claims processing or personalizing digital ad copy has the largest impact, but not the most transformative.

Fully agree. The promise of AI should be to deliver unto the human race an abundant future, free of material scarcity, where we can use the technological, societal, and economic scaffolding of society to increase flourishing of the human race. Cheaper spreadsheets will not do this. Hopefully, cheaper spreadsheets allow more work to be done with less human labor, and we can direct that labor up the value chain of human flourishing.

Here we have it – our first mention of the mythical “artificial general intelligence” in the article. As AI has become more mainstream in the first half of the 2020s, AGI as a concept has evolved from a distant sci-fi consideration to a distinct near-term possibility. Ask 10 AI researchers what their definition of AGI is and you’ll probably get 11 different answers. Because of the lack of rigorous definition and consensus of what AGI means, I think it’s probably time to move away from AGI as an actual milestone. As far as what Marcus says here, he’s probably right about 2027. There are still lots of things missing from a system that would represent what I’ll call “digital AGI.” I equate this version of AGI as a system you could drop-in to most remote work friendly occupations and they could perform as well as the median human at the work tasks within that occupation. I think that probably will happen by 2032, at least in some fields/occupations.

Agree with this fully – it is a goal within many frontier AI research labs to automate the process of AI research, the first step necessary to kick off the recursive self-improvement loop. Within this construct, AIs would be in charge of planning, executing, and evaluating AI research, and implementing these changes into subsequent versions. In five years, it’s very likely that humans are still in the loop in this process, both from an operational and safety perspective. I’m not sure that an increase in research speed will have as many consumer-facing benefits as internal benefits. Put another way, I’m not sure that speed of AI progress will be the only bottleneck to new capabilities that make the technology more impactful.

Strongly agree with this given that AI capability to actually do useful things in the world improves. Right now, agentic AI is still in its relative infancy. However, I’ve started using Claude Code recently and have seen the light. Agentic AI will be extremely powerful when either its capabilities improve in areas like memory, statefulness, and continual learning, or when the systems that the AI interacts with evolve and start to be designed with AI agents as the users in mind, not just humans. I think we will see Apple, Google, and OpenAI take big steps this year in the realm of truly personal AI assistants, as I predicted in my 2026 AI predictions post.

My biggest bet about the future of AI in five years

The most meaningful disruptions in the business landscape will result from enterprises that are designed from the ground up with AI in mind. Because organizational change and digital transformation take a long time in large companies, and the people that are currently employed within those large companies have a vested interest in their labor not being replaced by AI, adoption is probably going to be slower than most AI-boosters think. But as an example, I think an insurance company created with 5% of the headcount of a comparable insurance company might disrupt that industry. Sure, there will probably still have to be humans in the loop for legal reasons or physical reasons, like going out into the world and verifying claims, but I can foresee a company that subcontracts a lot of that work and is run mostly by AI. A company like that might be able to offer similar services and coverage for a much lower price because they are paying for 5% of the human labor that their competitors are paying for. This is more verbose than the responses given in the NYT piece, but that’s the benefit of inviting myself to the panel.

Will The AI Doctor See You Now?

I would probably put myself here in the “Moderate” category, with the hope that the actual result is “Large.” I think Marcus here is focusing a little too much on LLMs only, not thinking about things like AlphaFold from Google DeepMind. There is so much potential for AI in medicine that I wouldn’t be able to do it justice in a short paragraph to describe all the ways that I believe it could be useful. In the clinical setting, I think LLM-based tools could definitely aid in way that Frosst mentions and therefore increase the actual amount of time doctors spend focused on patients, not paperwork. Ideally, agentic AI systems could reduce friction within notoriously arcane and byzantine healthcare workflows. Here’s a humorous tweet that might not be that far-fetched in the coming years.

someone at this JPM panel said the future of healthcare interoperability is going to end up being teleoperated humanoid bots sending faxes to each other to get information

— Nikhil Krishnan (@nikillinit) January 14, 2026

On the research and development side of the house, I believe AI could be extremely useful in helping explore the very large problem space of diseases and potential treatments. I’m not quite sure that our medical system is ready to take advantage of the speed that AI can deliver though – if a new drug candidate can be proposed with the help of AI but it still takes 5-7 years to get to market, we’ve only sped up a portion of the process. This would still be a good outcome, and hopefully AI would increase the hit-rate for clinical trial success, but we should be looking to apply AI to every step in the drug development, testing, and approval process to get treatments into the hands of doctors and patients sooner. We should also be focused on reviewing the current regulatory processes that govern getting these therapies to market and ensure they are updated appropriately to take advantage of a potentially vastly increased volume of new treatment candidates.

An Army of Superhuman Coders in a Data Center

“Large” – obviously. In the extremely polarized world of AI opinions, it’s nice to stumble upon a point of unity. All but the most ardent AI-skeptics admit that current AI systems provide a substantial power-up to human programmers. As Harari notes, coding is an ideal arena for AI to showcase its skills. Programming tasks are easily verifiable (does the code do what you wanted it to do) and there’s an enormous amount of training data in existence for models to learn from. As I mentioned previously, I have only recently gotten around to experimenting with Claude Code and have been overjoyed with the results. If you haven’t vibe-coded anything yourself, please jump in – it’s a real joy to see an idea come to life in a few minutes on a screen. Frey’s point about human review is important, but I don’t know for how long.

An Eager Collaborator

I would place myself in the “Large” camp here. I don’t fully agree with Mitchell’s assertions. In December, OpenAI published researching showing that GPT-5 created novel wet lab protocols that increased efficiency of a molecular cloning protocol by 79x. AI is solving Erdos problems now. AlphaFold famously won its creators a Nobel Prize for its advancement in the realm of protein folding. The frequency and impact of these accomplishments is much more likely to accelerate in the future than to slow down or stop.

I watched a great podcast episode recently from the Cool Worlds podcast by Professor David Kipping of Columbia University. In the episode, he details a recent meeting he attended where the consensus was that AI is absolutely vital to the practice of science moving forward. He thinks, as do I, that AI will be viewed as indispensable a tool as a calculator or a computer in science in the near future. He also wrestled with the possibility of an identity crisis among scientists who may feel obsoleted by the technology. This is a valid concern, but I find solace in Srinivas’s response to this question – the role of humans will be even more concentrated on asking the right questions, and choosing where to focus their research efforts. If we can use AI to speed up the process of science, and also increase the volume of science we do as a species, the ceiling for progress is raised significantly.

Improving the way we move through the world

Frosst’s point is spot on. I think it’s very likely that the short term benefits of AI in transportation will be less flashy than self-driving cars, but only if the entities in charge of maintaining and building these systems embrace the technology. If we can use AI to more proactively approach problems in the realm of transportation, or tackle challenges that would have previously been cost-prohibitive, we could start to realize some great benefits. An example of this is a company I encountered last year at O3’s 1682 Summer Session conference on Innovation and AI – Scout Robotics. They are focused on using AI and robotics to reimagine inspection and maintenance for transportation infrastructure. There are thousands of miles of railroad in this country, the inspection of which is currently constrained by the cost of labor to carry out those inspection operations. If we can use AI to alleviate some of these constraints, we can make grand problems, in the realm of transportation and elsewhere, more tractable.

On the self-driving cars front, I’ve written previously about why I think this is a tricky problem worth solving. I’ll point you to that post for a relatively recent vision related to autonomous cars.

Education – What, how, and why

I would probably put myself off the charts here, somewhere in the realm of “Monumental”. I think AI has the potential to not only disrupt the way education is delivered, but at an even more important level, AI’s effects on the world may call into question what the true goals of education are. If we find ourselves in a future scenario, let’s say in 2040 (about the time my son will be going through his first year or two of college), where AI labor has proven to be a sufficient substitute for human labor in a wide variety of fields, what exactly will we be preparing our children for? This problem is so large, so consequential, and would require such coordination to re-think current systems, that I believe it will probably just be kicked down the road, where the collective hope is that “things will just work out.” I am very averse to this approach and would agree with my good friend Henry Cavill here – hope is not a strategy.

On the positive side of things, like Frey and Toner indicate here, I think AI can help rethink and remake education for the modern era. By providing personalized tutoring and tailoring educational content to specific pupil’s needs and aptitudes, we may be able to better serve individual students and allow them to progress at the pace that fits them, not at the pace the entire class progresses. One of the most interesting podcasts about AI that I listened to in 2025 was from one of my favorite AI content creators – Nathan Labenz at The Cognitive Revolution podcast. This episode from June of 2025 is an interview with with Mackenzie Price, founder of Alpha School, a school that uses personalized AI instruction to accomplish the learning of a traditional school day in two hours a day, with the rest of the time focused on passion projects, sports, arts, and other activities where “guides” (not teachers) work with the students.

Education is a third rail on most parts of the political spectrum. There is widespread agreement that our education system is outdated and not fit for purpose in many aspects, but almost no one can agree on the ways to make it better. A theme that underlies lots of my thinking about AI is that AI technologies may help us reimagine better ways of doing things that would have been unthinkable in the past. What if we really could enable every child on earth to access a free personal tutor? What if we could reorient the goal of education to not simply prepare workers, but to build a population that learns, grows, and thrives to bring about positive changes in the world? These may seem like pipe dreams for now, and Marcus’s observation that AI is used for rampant “cheating” and assignment completion is inarguably correct. The negative effects/uses of these tools today should not preclude us from realizing a better future by the smart integration of the tools.

An alien entity traverses our minds and our world

I’m in the “Moderate” camp here in the short term and the “Large” camp in the long term. We have already seen documented cases of horrific outcomes related to LLM psychosis, and I expect that to continue to be a problem. Any technology in the hands of hundreds of millions of users is going to generate adverse outcomes. I don’t want readers to view this as insensitive or downplaying the negative impact in these cases, which include suicide and murder. These are seriously challenging ethical situations, and they have devastating consequences in the most extreme cases. Add to that the fact that these systems are non-determinative, that each interaction is created anew rather than produced by software that relies on traditional IF THEN rules of behavior, it will never fully be able to account for every edge case. I want to see AI companies put real effort into explaining how they monitor for conversations and interactions that look like they may result in real mental or physical harm to people. I don’t know how you solve for this legislatively.

Alternatively, and similarly to the education point above, it might be hugely beneficial to put a therapist in the pocket of billions of people around the world at very low cost, in the same way it would be to put a tutor or personal assistant in their pocket.

I do love Harari’s point here, and it’s something I think about consistently as it relates to AI. We are all guinea pigs in the AI revolution. You may still be able to insulate yourself by not using the tools right now, but my guess is that in five years, AI will saturate nearly every part of society, and to remove yourself fully you might have to adopt a lifestyle similar to if you were to remove yourself from the influence of electricity. Add in the potential existential crises that arise from the lack of need for a huge swath of human labor at sometime in the future, and you get a huge impact on human mental health in the long term.

Creation and worth – slop or transcendence?

“Large” – positively large in the most optimistic scenarios and negatively large in the most pessimistic scenarios. I find the proclamations about AI as it relates to art and entertainment very interesting, and could probably talk at length about it. If you’ve gotten this far, I don’t want to lose you, so I’ll keep it succinct. AI is probably going to be able to enable lots of people to create material they would not otherwise be able to create. AI itself cannot generate “art” but it can generate “content.” Art, in my opinion, has to have an intentional component, and intentionality (also in my opinion) requires sentience. Hollywood is not “dead”, the video game industry is not “dead”, influencers are not “dead.” These landscapes will have new entrants to compete with that rely heavily (short term) or fully (medium term) on AI, but there will be a continued preference for human-involved material for the foreseeable future.

Update Your Priors – AI misconceptions worth dispelling

Panel question: What’s one misconception about AI that you think is worth dispelling?

I don’t necessarily see this as a misconception. Disregarding “magic” and focusing on “emergent” I think is important here, as semi-conventional wisdom holds that any sufficiently advanced technology is indistinguishable from magic. I think as AI tools become more advanced, and especially more agentic, we will see emergent capabilities that were not predictable or part of the training process. Semantics are important but sometimes frustrating, so I’ll leave you with this thought – we probably will not always be able to predict the capability of AI tools, and the result of that will be both incredible and frightening.

For right now, AI is for all intents and purposes under human control related to the motivation to action. You don’t birth an AI into the world and without any instruction it goes and starts accomplishing things. There is no root motivation or direction. Now this could change in the future, if sentience develops. I’m going to put it on my vision board one day to talk to Harari about sentience and consciousness, as these aspects of AI development probably have the most impact on the extreme longterm (100, 1,000, 10,000 years into the future.) I do agree with the second part, that models will be able to pass a consciousness Turing test within a decade. Agentic AIs are starting to become more common place, and they aren’t asking permission to carry out tasks, so I guess you could in some sense say they are “deciding” which actions to take. Currently though, I would think of that more like my heart “deciding” to continue beating.

Agreed. I’ve already used AI tools to help troubleshoot issues around my home or work on other handyman style projects. I wouldn’t trust it in areas with potentially hazardous or costly mistakes (i.e. electricity or plumbing) but it serves as a very useful first pass in this realm. I do think however that manual work will probably be “safer” from labor displacement for a decent amount of time. The world is very tricky. I think it’ll probably be about eight years until a humanoid robot can assemble a never-before-seen Lego set (I’m working on a post for the future about lots of different physical benchmarks I’d like to see and predictions related to these), so if that’s the case, it will be more than eight years before a humanoid plumber is showing up to fix my leaky sink or install a new water heater.

This one is hard for me – people are greatly confused about large language models, and do attribute humanlike intelligence to an intelligence that is structured and trained very differently from our own. There are many philosophical thought experiments that are useful to consider when thinking about AI, and one of the most famous is John Searle’s Chinese Room. In short, the thought experiment considers a computer that has the ability to perfectly carry a conversation in Chinese by passing pieces of paper back and forth to the human participant. The computer uses a translation dictionary to accomplish this task, painstakingly looking up the words and transcribing them, then handing them back to the human participant. From the human’s perspective, they seem to be conversing with a fluent speaker. The computer doesn’t actually know anything about Chinese, let alone how to speak it.

I think the real question here is if the output of a “mimicry machine” is indistinguishable from humans, does it matter that it is simply a mimicry machine? I also think Marcus’s second point about reasoning flexibly in the face of the unknown is a test where LLM based tools are have made continuous progress and don’t have nearly as hard of a time with new idea generation than they have previously. If you type into an LLM right now and say “think of a new idea”, a new idea will be provided to you. How “new” the idea is will probably be defined by personal preference, and unfortunately, by your priors related to how you think about AI. You might say “oh it’s just a combination of existing ideas” and if that’s the case I’d point you to tabula rasa and let you wrangle with that for a few minutes/the rest of your life.

Agreed. Again, sentience and consciousness are key areas to consider here when you start using the word “thinking.” However, it’s worth thinking about how much of human cognition is comprised of “sophisticated pattern matching.” My guess is, based on the ability of AI tools today to produce meaningful outputs, that this portion is huge.

I am standing, hooting, and hollering for this one. The real world is tricky, annoying, rough, complex, and chaotic. AI capability in a perfectly organized and receptive environment is likely far greater than AI utility in the real world full of humans and other unpredictable entities. Every technology has skeptics, bubbles, hucksters, vanguards, visionaries, and Cassandras. In the age of AI, all of these people have social media megaphones and captured audiences. The future is really, really unpredictable. My advice – don’t be definitive, update constantly, try to balance emotion and objectivity. I also don’t think this should be viewed only from a catastrophic standpoint – a new capability or new model might be the butterfly wing flap to a world of abundance, even if it simply looks like a dot release from a frontier AI lab today.

This is far too confident of a take for me. Like I said above, we have no idea what the employment effects of AI will be. New technologies almost always shift the nature of work in society, and in some cases they do remove types of work from society. How many horse-drawn carriage drivers or telephone switchboard operators have you seen recently? I think AI is different from these previously creatively destructive technologies because it has the potential to substitute not simply the output of human economic endeavor, but the substrate of labor itself. We have had tools in the past that have accomplished this for physical labor, but have never had it for cognitive labor at the scale we are on the precipice of. The first computers were people – they were in charge of actually computing mathematical operations. We began automating this cognitive labor in the mid-20th century, and it unleashed an unprecedented wave of economic growth and invention. If the previous 80 years of technological progress grew from that seed, what could the next 80 years look like if we can conquer all cognitive domains?

Absolutely agree, especially in the short term. Now, if we have the entire planet tiled with data centers that use gas turbines for power, that would be extremely bad. And I think the race dynamics at play today combined with the United State’s notoriously slow pace of building new infrastructure might lead to some short-term deleterious environmental impacts. But overall, in the end, environmental worries will be low on the list of concerns from AI. A particular shout-out here to one of my favorite voices in the AI space – Andy Masley, who has tried tirelessly to accurately quantify the water usage in the realm of AI. My hope and prediction is that AI will speed up our ability to make advances in electricity generation, storage, and transmission, hastening our departure from fuel that dumps carbon in the atmosphere.

Fast Forward – Life in 2030

Question: Which of the following statements do you think will be true by 2030?

I say “False.” Many factors, including at the minimum, friction within organizations to adopting these tools in a way that displaces labor and the lack of physical ability in the real world means that the employment impact of AI won’t be that large by 2030. I’m fairly confident in that prediction, and I also predict that will be different in 2035, 2040, 2050, 2100, and so on. Looking forward to continue elaborating on this over the next 80+ years.

“True” as long as you don’t specify that AI accomplished this feat “itself.” Scientists will be supercharged by AI, and humanity will benefit greatly from this fact.

“True” and terrifyingly so. With the emergence of agentic AI, and the desire amongst optimists to dangerously skip permissions, it’s very likely we will see an either intentional or unintentional major global warning shot by 2030. I hope that it will be in a relatively immaterial domain, and that we can recover from and learn from it. I’m worried that an inverse scenario (bad material impact or loss of life and that we learn nothing from it) is also fairly likely.

“True” but it might not be in chatbot form. I think it will be in ambient assistant form, like a Siri or a Gemini. This will depend on AI’s ability to actually “do stuff”, but I think it will be there and will be as omnipresent as the smartphone itself.

The Arrival of AGI

I would put myself in a new category here – “Likely.” Harari is correct that defining AGI as comparable to human intelligence is meaningless these days. I think we will definitely have something that one could reasonably consider “Digital AGI” by 2036, previously defined in this post as a drop-in remote worker. I don’t think we will have “Physical AGI”, meaning an embodied AI that can accomplish most physical tasks in the real world that a human can by 2036.

Historical Analogs – Have we been here before?

I love this question. It’s so hard to answer definitively, but luckily, since this is my blog, I’ll just tell you what I think right at this moment in February of 2026. I’m going to go out on a limb here, and say that in the short term, lets say between now and 2100, it will be as impactful as the steam engine. In the long term, it will be between the emergence of Homo sapiens as a species and the invention of agriculture.

Wisdom for the age of AI

I think these are all great pieces of advice honestly. And I really credit the Times on getting a diversity of opinion and experience for this piece. I’ll be happy to add my own if I can make my own luck and get an invite to a future version of this panel. My favorite piece of advice here is probably Srinivas’s about learning to ask the right questions. This is probably due to my deep appreciation of design thinking, which is all about asking the right questions to understand the problem you’re truly solving. In the age of AI, we will have more intelligence than we ever have before to solve any challenge we want. It will be crucial to point this intelligence at the right problems to create a better future where we can all thrive and benefit from this miraculous and scary time to be alive.

As far as my own advice to a high school student, I would say the most important characteristics to have are flexibility, creativity, and adaptability. The world is changing quickly – having the ability to adapt to the new realities that we encounter each day is going to be key to be successful and avoid being overwhelmed by the pace of the present.

A note on AI use: Here at Clearly Intelligent I’ll be adopting a scale suggested by Seb Krier that explains how I use AI in generating my posts. I’ll file this under 1.2 on this scale. I used AI to refresh my memory on the Chinese Room and tabula rasa, as well as to search and find some links used in the article that I’ve collected over the past few years thinking about AI. I also used Gemini to generate the cartoon image at the beginning of the post.

This post is not going to focus on AI. It’s probably going to be more political than I would have previously thought my writing here would be. But I don’t have a problem with that, because in this post, I’ll be arguing for a political philosophy that every American should cherish and work hard to keep extant in our political landscape: Liberalism.

What is Liberalism?

Liberalism is a political philosophy that emerged during the Enlightenment, when John Locke published his Two Treatises of Government in 1689. In this work, Locke argues against the divine right of kings to rule, and against the power of feudal lords who’s fiefdoms dotted the pre-Industrial Revolution world. He instead argues for things like consent of the governed – making sure people have a voice in the way they are ruled, and for Natural Rights – rights to life, liberty, and property that exist before any political consideration. Liberalism laid the foundation for liberal democracy, the American Revolution, and set the United States on a path to becoming the richest, most prosperous country in the world. It also inspired our collective journey to be the freest country in the world.

We’ve made varying amounts of progress on each of these axes, depending on how they are measured. We have the highest GDP in the world, and rank 10th in GDP per capita. We rank 61st in life expectancy and have far worse income inequality than many other developed countries, with that inequality exacerbating greatly in the past three and a half decades. What I hope to show with these statistics is that we are indeed on a journey in the United States. It hasn’t always been pretty (open any history book) and progress can seem haltingly slow (look around you in 2010, then in 2025 – you’ll notice not much has changed) but a commitment to Liberalism means that we can continue that journey. I fear we are in danger of losing that commitment and I aim to defend it.

The Evolution of Liberalism in the United States

There are many flavors of liberalism, too many for me to fully dissect here. Based on some rudimentary research that I’ve done to orient myself for this post, the main characteristic that’s up for debate within liberalism is whether the role of the state acts to suppress these Natural Rights (Classical Liberalism) or to enable these Natural Rights to exist (Social Liberalism).

If I had the desire, and the actual expertise, I could probably write an entire book on this, but I’m sure others have done it better than I could. Instead of doing that, I’m going to show you a graph of how I think the Democratic Party has treated it’s commitment to liberalism over the years. Special thanks to my co-author of this chart, Claude Opus 4.5.

Liberalism vs. Authoritarianism

My main motivation for writing this post is the horrific killings of both Renee Good and Alex Pretti. There have been many things that I’ve been disgusted by during the Trump era, but these incidents were particularly horrific to me because they showed that tribalism and the politics of retribution and punishment have reached levels that typically precede extremely bad times ahead. I’m still working my way through Slouching Towards Utopia and it’s not lost on me the parallels between what we are seeing today and what Germany saw in the 1920s and 1930s. The emphasis on punishment of the “other”, the questionable adherence to the rule of law, and the consolidation of power across aspects of government. These are not ingredients for a stable, liberal democracy. We can decide if the Trump years are a decade-ish of departure on our rocky journey as a polity, or if it represents a fork in the road that leads us into an authoritarian future.

I don’t want to be the one who continually blows the “descent into fascism” whistle, because I don’t think it’s particularly useful to jump to those sort of extremes, and is easily cast as histrionic by people who do not agree. But I also think we have been frogs in a very large cauldron, and the temperature has been turned up every day for a dozen years. It’s kind of hard to beat the authoritarian-curious accusations when you are, 6 years later, still trying to come up with a fake story about how you actually won an election.

The killings of both Good and Pretti have also shown that there are cracks starting to form in the Trump coalition, which gives me hope that there are swathes of voters who are open to examining their own loyalty/fealty to Trump. It doesn’t give me hope though that many elected Republican officials continue to be yes men and foot-soldiers. My hope for these officials is that they realize that Trump will not be in power forever, and they will no longer be solely judged by their obedience to him.

Liberalism and the Fourth Industrial Revolution

So what relevance does a commitment to liberalism have as it relates to the age of AI? Well, for starters, evolutions of liberalism have often coincided with, or been forced by, changes in the economic landscape that necessitated new thinking around the roles that different entities play in society. The progressive era was a response in liberalism to the fact that the Second Industrial Revolution, beginning around 1870, created lots of new problems that the state was best positioned to address. Workers’ rights, anti-trust regulation, and the beginnings of the social welfare state in ex-US countries were all borne out of the rapid industrialization of that era. Surely child labor laws reduced the amount of factory output that was possible, but that seems like a pretty good trade to me.

I think the US got fairly lucky with the Third Industrial Revolution, which isn’t as widely recognized as the first and second, but I equate with the digitization and miniaturization of the economy, starting in about the 1950s. At this point, much of the world was still rebuilding from WWII and the US was a major manufacturing hub. China, a global superpower today, was very much a developing nation. As such, there was not as significant of an effort towards or need for large scale economic reform – the US was doing pretty well economically, and the burst of technology was making lots of cool products (TV, automobiles, personal computers) widely accessible, and these products made life materially better for the population. The Civil Rights movement and the Great Society of the 1960s expanded both civil liberties and the welfare state in ways that have paid enormous dividends over the last 60 years.

However, the relative stagnation and economic uncertainty in the 1970s lead to the a new kind of liberalism – neoliberalism. Neoliberalism is akin to a modern reimagining of classical liberalism, promoting free trade, free markets, and a more limited governmental role in the economy. By many measures, it was successful – our GDP grew enormously, the world at large became a global marketplace, and the proliferation of consumer products and technologies that we all enjoy today were accelerated, if not enabled, by this turn. It also resulted in a decoupling of wages from productivity gains and severely exacerbated income inequality.

Currently, we find ourselves in a situation that I believe, at least in part, is due to a failure of neoliberalism to effectively bring the entire populace along for the ride. I think Trump 2016 was an early mainstream indication that there was trouble brewing – people were dissatisfied with the status quo and they showed it at the ballot box. I still think most people are dissatisfied with the status quo, which might lead to a pendulum swinging back and forth electorally. I think that the Democratic Party has fallen into the trap of corporate-pleasing-GDP-maxxing-neoliberalism. I don’t necessarily begrudge the thought process behind this – for most of human history, if you made a society richer, you made a society greater. I think that trend has broken and needs to be cast aside as the main motivation in governing.

What I want to see is a Democratic Party that is focused on making society greater. Focusing on human thriving, not GDP growth. I also want to see a Republican Party that is focused on making society greater. There might be very different ideas on how to get there, but I hope that this common goal, of a democratic, liberal society, is what follows this period of upheaval in American politics.

On the eve of the fourth industrial revolution, this is uniquely and specifically important. I came across a really timely tweet today that eloquently summarizes why.

Worth noting that all the Industrial Revolutions (we’ve had three) have worked out well in the end, but the first two were pretty disruptive to actually live through. https://t.co/6wK3qfsFta

— Ethan Mollick (@emollick) January 29, 2026

Ethan Mollick (professor focused on AI at the University of Pennsylvania) is correct in that these previous periods of history have worked out pretty well in the end. There are lots of factors about the AI industrial revolution though that will make it even more challenging. The speed and breadth of diffusion of AI will be faster than the physical goods that needed to be shipped port to port in previous revolutions. The scope of the economy that the technology will impact will be broader than any technology we’ve seen previously (knowledge work first, then physical work.) And the fact that AI is positioned to possibly be able to actually substitute for labor itself means it threatens one of the only two bargaining chips a polity really has to make an impact in society.

If we lose both our ability to use our labor and our votes as bargaining chips, by slowly sliding into anti-Democractic/authoritarian-curious governance, then We The People will be at the mercy of whomever is in power when that comes to pass.

The Third New Deal

Americans yearn for a political entity that will fight for them. Right now, the two major parties, at least where the concentration of actual political power lies, are both corporatist parties. Trump won on the promise that he will fight for the average American, then proceeded to plunge the country, and maybe the world, into a prolonged period marked by division, incompetence, and malice. Democrats largely, either by being true believers or being too afraid to upset the lucrative ecosystem of the affluent political class, have remained mostly aligned to a GDP-maxxing viewpoint.

A new strategy is needed, one that will break the spell of both Trumpism and Corporatism. Nearing the centennial of its predecessors, a successful candidate in 2028 will propose a Third New Deal to the American people. One that is focused on maximizing the flourishing of the citizenry of this country. I’m not sure what the exact tenets of this would be. It’s going to be hard to balance appropriate state action that protects and improves the wellbeing of the people of the United States without hampering innovation in the age of AI. But meaningful pursuits are usually difficult. If we don’t try to address the societal frustration and malaise that’s manifested in recent memory, we will simply be kicking the can down the road. This might lead to further erosion of the social contract, further fracturing in society, and open the door for even more extreme politicians to take power in the future.

When you pair this corporatist attitude that’s broadly applicable across the political spectrum today with the idea that we are basically betting the success of the global economy on the success of AI, you get a very volatile mix with a wide range of potential outcomes. I don’t want the future of the human species to depend on the benevolence of a yet-to-be-determined CEO of THE AGI COMPANY. You shouldn’t want that either.

Worthy Opponents

I’m not trying to hide the ball on where I stand politically. I’m not even trying to convince classical liberals to adopt my viewpoints. What I am trying to do with this post is to defend and preserve liberalism, to encourage everyone to weed out the roots of authoritarianism, even if they occur on “your side” of the aisle. There are few things worth fighting for more than for our ability to live in a liberal democracy. It’s not perfect, but as we descend on our 250th birthday, we must remember that we’ve only got a republic if we can keep it.

A note on AI use: Here at Clearly Intelligent I’ll be adopting a scale suggested by Seb Krier that explains how I use AI in generating my posts. I’ll file this under about a 2 on this scale. I used AI to do research on liberalism and create the chart within the post. The opinions herein are mine, and mine alone.

In 2026, AI will undoubtedly continue to be a huge part of our social, economic, technological, and political progression as a species. Here are a few things I think we’ll see in 2026 in the world of AI and elsewhere.

Autonomous Driving Accelerates and Runs Into the Real World

Waymo is planning to aggressively expand its areas of services throughout 2026 and beyond. Buoyed by impressive safety data and growing (albeit slowly) acceptance of autonomous driving, they will continue to increase their YoY miles logged and cover more geographic areas than they previously have. I’m very bullish on autonomous driving and think ideally designed and deployed autonomous driving systems will be widely available to riders in the coming decades and will dramatically reduce (>95% reduction) road fatalities once we hit the point at which a human is no longer necessary to drive the vast amount of miles logged in this country.

However, my prediction for 2026, is that Waymo will see its first rider fatality. My hunch is that it will actually be the fault of the human driver in the other vehicle in the accident (i.e. a blind merge/lane change on a freeway), but it will happen nonetheless. At some point, the law of averages dictates that it must. I hope that I’m wrong, but we know that Waymo has been preparing for that moment whenever that first fatality does happen.

What will the public think when this accident happens? Judging by other cases where fatalities (both human and feline) have involved autonomous technology, they will receive outsized media attention compared to a run-of-the-mill road fatality. Safety stats make for a boring story – a first of its flavor tragedy does not. Autonomous driving, like many other new technologies, will have skeptics that point at edge cases encountered in the real world, like when Waymos were more or less rendered inert during a PG&E outage in San Francisco in late 2025. These considerations are important – the world is messy! It’s full of edge cases. But they should be weighed appropriately against the preponderance of evidence of current road experience (excessive speeding, impaired driving, texting and driving, sexual assault by Uber drivers) to adequately assess the pros and cons of bringing a new technology into the real world.

Chatbot Wars Intensify

OpenAI’s ChatGPT is still by far the dominant market leader in consumer AI applications. Due to many factors, including explosive early adoption, rapid early iteration, and the very public personas of OpenAI’s leadership, ChatGPT is the “Kleenex” of AI chatbot applications today. Gemini will continue to increase its marketshare, as Google’s renewed and intense focus on consumer facing products and AI technology was an important 2025 trend. Expect lots of Super Bowl ads for AI again, really leaning in on the chatbot experience and form factor. It is still relatively early in the chatbot game, and I think that other than for superusers, the switching costs are still low. If Google can show that Gemini’s capabilities are on par with ChatGPTs and thread the needle of more seamlessly integrating into G-suite and Android phones (something I’m admittedly a complete novice regarding considering my pseudo-religious devotion to the Apple ecosystem) I think they have a real shot at taking a bite out of ChatGPT’s marketshare.

Apple Kills Siri – But “Phoenix” Rises from the Ashes

Apple has been one of the weirdest players in the AI space. Over the past several years they have overpromised on features that never shipped or made AI a footnote in keynote addresses. I don’t necessarily blame them for it – they have continued to perform well both in the consumer marketplace and in the stock market and probably actually have made a wise decision to let the other companies duke it out in the capabilities war. Apple as a company has long had a corporate philosophy of not necessarily doing it first, but doing it best. As someone who thinks a lot about the creative process of product design, the thing that is most important to me when considering creating something new is to truly understand what the problems are that your product is going to solve for. Right now I think it’s the lack of well-defined and well-specified problems that are keeping AI adoption relatively low when you consider the actual capabilities of these tools. My bet here is that Apple retires Siri, sending it respectfully afloat on a barge, down the digital river to join other storied relics of the past. They use their fall keynote to launch Phoenix, Apple’s Ambient Intelligence.

Ambient Intelligence is a Key Theme in Consumer AI

An ambient intelligence, one that lives with you, hears what you hear, sees what you see, and knows what you experience in the world, would be capable of solving the problems that you encounter on a daily basis. Imagine a personal Clippy that rides around on your shoulder, adding to your to-do lists, ordering Ubers when you have a reservation, surfacing that ticket in your email as you arrive at a concert. This kind of ambient intelligence will make your life more frictionless. This is my bet for the consumer product category in 2026. Whether it be pens, pendants, pucks, or phones – ambient intelligence is coming. A truly personal assistant that takes the annoying things off of your plate and positively adds to your life.

This vision is probably going to have a hard time being accepted outside of techo-optimist circles. There’s going to be a very thorny set of social norms to navigate in a world where omnipresent recording devices are now just not within phones, but in peripheral accessories as well. What does obtaining consent for recording look like? How is that data stored? What about separating personal and work data, much of which comes with far stricter rules and regulations? I think there’s going to be significant cultural and social pushback on further fortifying the distributed panopticon that our modern world already represent. Add to that the general public’s distaste for AI and it’s a receipt for backlash and ridicule.

Utility over Capability – Benchmarks Fall Out of Favor

LLMs are going to saturate most benchmarks by the end of 2026 and I think they will no longer be a great measure of an LLM based AI tool. LLMs have made remarkable progress on human created benchmarks to date and I think that progress will continue. We will see intelligence per dollar across these benchmarks continue to fall, as smaller models improve and better computing is available, but the world starts to shift in favor of evaluating “utility” over “capability”. These means a lot, and at some point will probably be its own blog post in the future, but mark it down here – increased focus on “what have you done for me lately” not “what could you do in a theoretically perfect scenario.”

White Collar Job Growth Will Be Non-Existent

Slightly related to the capability vs. utility distinction in the preceding prediction, I think we see a continued holding pattern in the white collar world. C-suites all over are ecstatic about the promise of AI tools, wooed by AI industry players taking showcasing capability and promising utility. Huge, multinational organizations are going to be very wary of bringing in new workers to onboard and train if there’s a potential to automate their tasks in the next few years. While many companies may be using AI as cover for layoffs that occur for other, more traditional business reasons, I think it’s likely that organizations generally will take the stance of trying to keep teams relatively lean as they wait for the AI aha moment in their specific industry/use case.

AI Will Be a Major Issue in the 2026 US Midterms

At a time where a historically unpopular president already appears to be in his lame duck era, and has gone all in to appease the AI industry, it’s very likely that Democrats take a decisively anti-AI stance. It’s going to be the politically popular thing to do. I want Democrats to win back power and position themselves well for 2028, so I’m not going to necessarily begrudge them for harnessing popular sentiment and pointing it at the current punching bag.

Good politics doesn’t always mean good policy. What I would like to see is this issue framed like is focusing on building technology the works for us and increase our ability as humans to thrive, not technology that simply increases the valuation of a handful of trillion dollar companies. That’s a paradigm shift though that’s complicated and requires a lot of focus and effort – not something we are going to want to tackle in the 11 short months before these interim elections.

There will be lots of angles from which AI enters the political debate this year – nonconsensual AI image generation, data center construction, electricity prices, job displacement are just a few. I don’t think the population is in a receptive mood to the idea that “if we just go all-in on AI, the future will better.” Technology has done an incredible amount of good for human civilization, but we find ourselves in a precarious world currently, where we are chipping away at the last few percentage points of tangible improvements to our lives that entities are financially incentivized to go after, while majors problems like hunger, disease, violence, and inequality remain.

I want us to choose to use AI to help us maximize human thriving. We should be skeptical that a technology that has already provided outsized benefits to such a small percentage of people and corporations will magically enable to solve all the issues we face on this planet. The upcoming midterms will be a preview of the 2028 election, where I believe AI will be the single biggest issue.

AI Achieves a Scientific Breakthrough

And for now, my most positive prediction: 2026 will be the year where AI for science goes mainstream within the scientific community and aids in/discovers a scientific breakthrough.

We are around the time when then capability level of tools combined with a greater acceptance within the scientific community of integrating these tools into their work may be capable of producing something genuinely novel. Perhaps it’s a novel drug discovery, a math conjecture, or a materials science problem – all areas of knowledge with huge search spaces that are better approached with the intellectual horsepower of AI systems.

I don’t think that the Nobel Prize will be awarded (at least in part) to an AI system quite yet (I think that will happen around 2028-2029), but I think scientists are really starting to understand the parts of the scientific process that are amenable to AI tool inclusion, and are going to be better able to deploy this familiarity with the tools in an experimental setting.

Predicting, Updating, Iterating

I am really excited to put out my first formal predictions post. I want to hear what you have to think about these predictions and some of your own predictions. Putting a marker down at a specific point in time feels a bit treacherous, but I’ll be happily updating and iterating on these predictions over the years. I’ll also revisit this in December 2026 for my first prediction self-assessment. Subscribe if you want to make sure you track these through to the end.

Updated January 10th, 2026 – Added in an article that posits that AI is being used as cover for layoffs rather than AI actually replacing human labor. Kudos to my good friend Dr. Paul Hook for sending along this piece.

Welcome to Clearly Intelligent. I’m Mike Cottone, a consultant by day and an artificial intelligence commentator and communicator by night. I’m embarking on this project because I believe that we are in the early days of a period of profound technological change. I have two main goals in mind with this endeavor.

Goal #1

Expand understanding of AI technologies and their impact on our world.

Goal #2

Steer the world toward a better future.

Artificial intelligence has the potential to change nearly every aspect of our lives, but the path before us is unpredictable and complex. Join me as we navigate it together.