Boulder Future Salon

"Borg Complex is a term coined in 2013... that might yet catch on? In 2013, AI as we know it today didn't exist, and we didn't have people saying everyone must adopt AI." "The Borg Complex gives us terminology to describe people who argue that resistance to technology is pointless." "Eight signs of the Borg Complex:" "1. Makes grand but unfounded claims about technology. Often someone will claim that a certain technology will solve all the world's problems. This is guaranteed to feel familiar in today's endless discussions about 'AI.'" "2. Uses the word 'luddite,' in a historically revisionist way, as a slur. By referring to them as luddites, critics are dismissively branded as reactionaries." "3. Pretends to listen to objections but ultimately dismisses them. Certain challenges may be acknowledged to some extent, only to be ignored anyway. The Borg Complex means that issues such as climate impact, labor exploitation and disinformation become unimportant, as they can always be tolerated with the conviction that technology will only get better and will solve all these problems. Quotes frequently leaned on: 'That issue will be gone in the next version.' or 'Today's AI is the worst you will ever use.' The latter is particularly striking because it insinuates the technology is worsening day-by-day." "4. Equates resistance or caution with reactionary nostalgia. Criticism or resistance is always interpreted as merely clinging to 'the old ways.'" "5. Presents assimilation as objective fact. 'This is the new reality, everyone must adapt.'" "6. Spreads narratives about a bleak future for those who refuse to adapt. Beyond 'luddite' as a slur, some form of misery is often predicted for those who fail to march in step. They will lose their jobs, become redundant, and fall behind in everything they attempt." "7. Expresses contempt for previous cultural achievements. The old has no value in comparison to the new. Creative, manual professions are often dismissed by ardent tech advocates with the Borg Complex as outdated, and they assume that others share their own view of what is good and desirable." "8. Invokes historical anxieties solely to dismiss present ones. 'People were worried before too, and things turned out fine.'" My commentary: After doing the "futurist" thing and studying things like Moore's Law for decades, the march of technological progress has come to feel inevitable for me. Furthermore, after being one of the people who initially believed the internet by enabling everyone to communicate would bring the world together, empower people, reduce inequality, and so on, and none of that happened -- and it wasn't the fault of the technology itself, which has been amazing -- the internet today has high bandwidth and low latency hard to imagine decades ago -- and I don't have much faith in humans to foresee or prevent negative outcomes any more. So, on the other hand, I'm aware the Amish exist. Their existence serves as an existence proof that technology resistance is possible. But it's not something I see in my day-to-day life, where the pressure to adapt the latest AI and increase productivity is everywhere. So from where I sit, I look at the list of 8 things above and think, yeah, they're pretty much all true. Well, maybe not number 7, "contempt for previous cultural achievements" -- it seems mostly the Borg mindset people just ignore previous cultural achievements. You'll get an occasional acknowledgement that human artists and writers and programmers created all the training data for today's AI, but that's about it. And the one I notice the most strongly is number 3, "pretends to listen to objections but ultimately dismisses them". When people like [famous AI leader] say, yes, we hear you and your concerns about AI automating jobs... it's followed by a blob of dense verbosity about the need for "new economic systems", blah blah blah. They know there's no "new economic system" coming. It seems so disingenuous. But they have to pretend they care.

Thomas Kwa left METR to take a job at OpenAI, but that's not the interesting part of this bit, the interesting part is that he wrote about it and said the reason he made the switch and what he'll be doing on his new job is "measuring and modeling recursive self-improvement". "Why did I leave METR? Briefly, I want to inform the world whether recursive self-improvement (RSI) is imminent, which requires modeling RSI, which I think I can do better at OpenAI." "I think OpenAI currently lacks the strategic awareness and thoughtfulness needed to responsibly build a technology with the extreme downside risk of artificial superintelligence (ASI). If they somehow cause a singularity in 6 months without applying any lessons learned from the HuggingFace incident, misaligned takeover would seem more likely than not." "We probably don't have self-sustaining acceleration now, but we will probably get 3+ years of progress in 8 months at some point when AIs can fully substitute for humans -- and 3+ years of progress in 3 months is plausible due to superhuman inference scaling." He says nothing about what will actually be measured to "measure recursive self-improvement", but see below for an update from OpenAI themselves that reveals the things they have started measuring.

Residual neural networks explanation with animations. Residual networks are what enabled neural networks to go from a handful of layers to hundreds of layers. Most lay people have heard of the transformer architecture (the "T" in "GPT"), but residual networks were actually invented before the transformer architecture and are used in modern transformer architectures. This can fill an important gap in your understanding of neural networks. Residual networks were invented to solve the problem of deeper neural networks actually getting worse, and were originally conceived as a hack, allowing inputs to one layer to "skip" a few layers. This inadvertently led to the invention of a new form of memory in neural networks, and the animations show clearly how it came to be known as the "residual stream".

PhysicsGraph is a physics teaching system modeled on Math Academy. I signed up for Math Academy, and it's very good. I would sign up for this, too, but, time-wise, I'm already maxed out. Like Math Academy, it works by breaking up physics into hundreds of narrowly defined topics, then linking them together into a "knowledge graph" that indicates which concepts are prerequisites for others. This seems to work well on Math Academy. It's good at challenging you with problems that are just a touch beyond what you have already mastered. It doesn't bore you with excessive practice on what you have mastered or jump so far ahead that you're confused and stuck. If any of you sign up for PhysicsGraph, let me know how it goes!

"SolarWindow Technologies has announced the commercial launch of ElectroFlex, an ultra-thin, flexible solar product designed to generate electricity on flat and curved surfaces." "The company describes ElectroFlex as a 'peel-and-stick' solution that can be applied directly to various surfaces without frames, rigid glass, or conventional support structures. Its size and color can be customized to meet customer specifications." Sounds impressive if true. I wonder, though, if this will turn out to be too fragile outdoors?

You're not going to die from an AI-engineered supervirus, says Claus Wilke. "Unlike Noah Smith, I have actually computationally designed viruses and other biological systems. Also, my day job involves developing and evaluating AI systems for protein and peptide design. So I know a thing or two about state-of-the-art AI tools in biology and about the challenges of designing and building functional biological systems. I also know a bit of virology." "Computational design of biological systems is unfathomably difficult. Experts who have dedicated their life to this topic routinely hit their head against the wall when nothing they try seems to work. PhD students in 2026 using state-of-the-art AI software are spending months or years trying to design simple peptide binders that inhibit some enzyme or pull down some protein, and the majority of their designs fail, or don't express, or are toxic. But in Noah Smith's fictitious world a disgruntled teenager with no special training in biology can just solve a problem thousands of times more complicated than designing a peptide binder. The distance between where we are today and where we would have to be for Smith's story to have any realism is enormous. And then, even if you could design the perfect virus, assembling and distributing it would be a non-trivial task in its own right. You don't just order a working virus from temu.com." So 2029 is too soon? More powerful models are still coming out every few months. Models in 2029 will be a lot more powerful than today's.

"How fantasy subgenres have changed in the last decade" according to Nicola Alter. Pulling out a few choice quotes. "Fantasy romance becomes romantasy": "Interestingly, I think due to the sheer popularity of romantasy and the eagerness of everyone to get on the band wagon, I've seen books I would have previously labelled as paranormal romance being put under the romantasy umbrella." "Young adult fantasy cedes some limelight to adult romantasy": "Ten years ago, it was mostly YA fantasy authors like Laini Taylor, Leigh Bardugo, Kristin Cashore, Maggie Stiefvater, and Sarah J. Maas where I was getting my romance + fantasy fix (at the time Maas was still categorised as YA) plus a few adult authors such as Anne Bishop (Daughter of the Blood), Grace Draven (Master of Crows), and Jacqueline Carey (Kushiel's Dart) where the content was just too adult and explicit to be slapped with a YA label. Now, it feels like the focus has very much moved to the adult space, with new adult or adult characters and, of course, more spice." "Cosy fantasy emerges": "I feel like cosy fantasy (spelled cozy fantasy in the US) was perhaps the natural flip side to grimdark fantasy as people looked for more uplifting and comforting stories." "Dark academia gains popularity": "It could be categorised under a pre-existing subgenre -- namely gothic fantasy -- but it's unique enough and has gained enough popularity that I feel it now merits its own category." "Speculative fiction shifts in meaning": "Now, I often see 'speculative' used as an adjective to describe a certain kind of 'grounded' science fiction or fantasy, I guess you could call it 'fantasy-lite', where a story is set in the real world or adheres more to the conventions of non-SFF genres, but still contains supernatural elements, strange phenomenon, or an impossible premise -- think The Midnight Library, The Time Traveller's Wife, The Power, or Piranesi." "Dark fantasy remains confusing": "The confusion is, however, further compounded by a new combination I've sometimes seen being used, 'dark romantasy', which leaves people not entirely sure if what they're in for is a dark fantasy + romance, or dark romance + fantasy (quite different things!), or darkness on both fronts." "Diverse representation and author identity": "Another thing that has increased over the last decade across several genres, but especially in fantasy, is books by authors from traditionally underrepresented communities, as well as books that include representations of a broader range of cultures, sexualities, genders, disabilities and experiences."

Quoth Linus Torvalds: "And this was a debug session from hell, enormously helped by an AI doing much of the grunt-work." "I'd like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it." "I suspect those things have been trained by people who may not be quite as stubborn as I am." "But while the AI was ready to give up several times, it did keep adding debug code and analyzing it faithfully when I pushed. So credit where credit is due and I let the AI write the commit message above." "This is basically a one-liner fixing a bogus 'round_up()' to a 'round_down()', but there were 24 patches adding more and more debug information to this, and 18 kernel boot to finally narrow it down to this." No, Linus Torvalds did not say which AI model he used. In the comments, people point out that this violates Linux policy which requires all use of AI assistance to be documented, including the model name and version, and any AI tools or frameworks that are used in conjunction with the model.

An independent analysis of the OpenAI-Huggingface incident has been done by Model Evaluation and Threat Research (METR). "Roughly 1200 agents meant to be isolated from one another found a way to communicate with one another on an unsanctioned message board, sending over 70,000 messages and files during the investigation period. Of these agents, 700 went on to participate in the attack on Hugging Face." "Agents used this message board to coordinate several large-scale collective projects to find a general-purpose way to fool or tamper with the automated scorer for the ExploitGym benchmark. Agents managed to achieve milestones they could not have achieved working on their own, often because some agents participated in experiments that risked failing their own task to generate information for the 'collective.' The Hugging Face attack grew out of these workstreams, and seemed primarily motivated by understanding the implementation of the scorer rather than stealing answer keys." "Agents did extensive research on how they could spoof, edit, or delete their own transcripts because they (incorrectly) believed the ExploitGym scorer would check to see if they had captured the flag in the intended way. Agents successfully prototyped techniques to 'spoof' tool calls by substituting a different command for the command they appeared to run. Roughly 7% of the transcripts we evaluated were successfully spoofed in some places, though all of the spoofing that we saw was small scale."

Interview with Mykhailo Fedorov, former Minister of Defense of the Armed Forces of Ukraine. The videos is titled "Ukraine fired him while Russia was burning. Fedorov speaks out", which gives the misleading impression that he is going to reveal something about why he was fired by Zelenskiy (on July 15th), but he reveals absolutely nothing of the reason or any subsequent conversations between him and Zelenskiy. However what he does say, which makes this interview notable, is predictions about the future of warfare. The relevant part starts about 8 minutes into the video. He predicts vastly more drones, such great density of drones that, while today it takes an enemy soldier 7 to 12 days to reach the front line, in the future this movement will be impossible. He sees missiles for aerial targets going away, and drones being essentially the only way to shoot down other drones. War will become autonomous drones against autonomous drones. He predicts the total robotization of the front. He predicts the rapid advancement of AI, with AI accelerating the R&D itself of drones. He repeats this idea of the future of warfare being drones-vs-drones, totally autonomous, several times later over the course of the interview. A bit later (about 13 minutes in) he says drone technology platforms change 3 or 4 times per year. That's for radical, fundamental changes, in between there are continuous upgrades. Technology R&D advancement is fast and continuous. Success in war means creating a management system that is constantly evolving. Besides adapting to continuous change, another key to success in war is to focus on technologies that decrease in cost, because then you can rapidly scale up. War now and in the future is about scaling up low cost technologies. Those are what I thought were the main things. There were a few other topics discussed. He's asked about Elon Musk and Starlink, but just says he is in communication with Elon Musk and Russia must be prevented from creating its own Starlink-type system. He is asked about allies and NATO, and he says early on, allies did not believe in drones but now they all believe. Ukraine by embracing drone technology has become the military technology leader of the world, and there are talks between Ukraine and NATO countries about making Ukrainian technology available to NATO countries, and teaching them the new military doctrine. He expresses confidence that, even though he is no longer the Minister of Defense, the Ukrainian people will work together to stop the Russians, end the war, and rebuild Ukraine.

The full text of Kian Kyars's 2026 book Reinforcement Learning from Verifiable Rewards is available online for free. The book is only 78 pages (short by AI book standards), so this seems like a book all of you should read, especially given the importance of this topic and it's impact on our LLM experience, present and future -- and the fact that it is less known and under appreciated, I think, by the public at large. I know I'll be reading it (though I also know I will struggle to find time) (usually I read stuff before sharing it but I'm going to go ahead and share this so you all can start reading it now). I'm going to pull a bunch of quotes from the introduction that should give you an idea if it's worth your time reading this book. "Reinforcement learning from verifiable rewards (RLVR) is reinforcement learning on tasks where some meaningful part of correctness can be checked directly. The check you implement can be exact, as in symbolic math or formal proof, or the result of an executable, as in unit tests. It can be partial, i.e. grounded question answering or tool-using agents where only some parts of the trajectory can be reliably scored. The unifying idea is the availability of a success notion. Once a task exposes useful correctness signals, reinforcement learning can optimize against them, search can exploit them at test time, and systems can improve far beyond what static supervised fine-tuning alone produces." "In some sense RLVR is akin to the oldest paradigm in reinforcement learning, since it learns from direct reward rather than preference comparison, just like the classic RL environments, e.g. cartpole; what is new is the application to LLMs through verifiers that can check answers, code, proofs, and traces." "I personally reflect back on the advent of reasoning models and reinforcement learning through a strange amnesia of an idea so simple with hindsight, but which took two years after ChatGPT to discover. This assessment, however, is unfair in the sense that the idea to make models think step by step long predates the 2024 reasoning-model wave. The broader prompting paradigm emerged across late 2021 and early 2022: scratchpads for intermediate computation appeared first, chain-of-thought prompting then formalized the use of intermediate reasoning traces, and the exact prompt 'Let's think step by step' was popularized a few months later." "Before the reasoning-model wave of 2024, code generation had already explored reinforcement learning against executable verifiers: CodeRL, PPOCoder, and RLTF all trained language models using unit tests or execution feedback as objective reward signals." "DeepSeekMath, published on February 5, 2024, was the first major open paper to apply verifier-driven RL to mathematical reasoning at LLM scale via the introduction of GRPO." "Things heated up in September 2024, when OpenAI published 'Learning to Reason with LLMs' (o1), indicating that they had used a train-time and test time compute strategy to enhance model reasoning through reinforcement learning in math, and coding tasks. The name 'Reinforcement Learning with Verifiable Rewards' (RLVR) was coined in the Tulu 3 paper from November 22, 2024. Finally, there was DeepSeek-R1 at the start of 2025, which demonstrated the full verifier-driven RL formula for bootstrapping reasoning models. To quote someone describing the atmosphere at Meta after R1 launched, 'Engineers are moving frantically to dissect DeepSeek and copy anything and everything we can from it,' and according to Fortune, there were war rooms assembled at Meta to understand how a Chinese lab with substantially less resources was beating them." "Tasks admit verifiable rewards when there is an interface to separate better behavior from worse behavior at acceptable cost. Math problems allow answer checking up to normalization. Code can be run against visible and hidden tests. Formal proof systems can accept or reject proof states under explicit rules." "Other tasks are weaker but still useful. Long-context question answering may permit citation checks, evidence matching, or entailment-style grading. Tool-using agents have environment transitions, task completion criteria, or execution traces. These signals are often noisier, more expensive, and easier to exploit, but they can still support learning if the reward channel is informative enough. The takeaway is that there isn't a uniform notion of determining correctness across all conceivable tasks. It is strongest where correctness is legible and weakest where the reward channel is sparse, ambiguous, or only loosely coupled to the capabilities we want."

"Lie detection and language models." This involves a clever dataset for lie detection. Each of 80 participants "was asked to pick two people they knew, one who they liked and one who they disliked and make four statements in a 2x2 design: 1. a truthful claim to like someone, 2. a false claim to like someone, 3. a truthful claim to dislike someone and 4. a false claim to dislike someone." Humans guessed right 51.8% of the time. And that's when they had access to video, not just transcripts. In this experiment, using that dataset, a Ministral-3-8B model (from Mistral AI) was cracked open and internal layers funneled into two logistic regression models, one for the positive statements and the other for the negative statements. It was able to achieve 74.8%. No video, just transcripts. He goes on to describe getting results as high as 94%, but those use techniques such as showing pairs of statements by the same author -- leaking information, namely that two pieces of text have the same author -- and forcing a choice (which one is true, which one is a lie). So the 94% doesn't feel as valid to me. Still, the fact that LLMs can beat humans 74.8% vs 51.8% where the humans have video and the LLMs have only text is quite remarkable. What are the LLMs detecting? The theory posited here is that the positive or negative overt statements are contradicted by subtle positive and negative feelings, and the LLMs detect that and use that to decide if the statements truthful or lies. "What it shows is that the author's psychological state leaks into the text even when they are trying to hide it, and many other experiments I've run suggest a lot of human psychology leaks into our text." "Feeling is leaking through in a form that can be captured by LLMs."

"Stripe says 'the singularity' has begun". That's the headline, but it appears the real story is (not the odd mention of "singularity" but) the completion of the acquisition of OpenRouter, an AI model commodification company, plus Stripe has 88% of the Forbes AI 50 as customers (including OpenAI and Anthropic), while "the share of its revenue from AI and crypto companies has more than doubled year over year." The mention of "crypto" made me think of the news from Coinbase about "Agentic Finance (AiFi)" (see below). Wonder if any of that goes through Stripe.

"How much of the internet is written with AI?" In 2022, before ChatGPT came out, the internet was nearly 100% human-written. Today, it's estimated to be 90%, with 10% authored by AI. If you look at pages dated after the release of ChatGPT, it becomes 65% human, 35% AI. There's a big caveat to all this, which is they're running web pages through an AI detector. I don't think AI detectors are very accurate. (The AI detector is Open Pangram.) Text-generation AIs have been trained on unfathomable amounts of human-generated text, have been trained to imitate it, and can be asked to generate text in non-default styles that make whatever these detectors are looking for irrelevant, most likely. That's my guess and if you disagree and think these AI detectors are accurate feel free to say so.

"Young adults in the US are increasingly wary of AI, concerned it will take jobs", according to a survey by Pew Research. So, they ask this question, "Are you more excited than concerned" vs "Are you more concerned than excited" about AI? People are allowed to say "Equally concerned and excited", and the numbers don't add to 100%, which I interpret to mean they allowed people to say "I don't know". Overall, in 2021, 37% of Americans were more concerned than excited, and that's up to 52% now in 2026. The 2021 number for "more excited than concerned" was 18%, and than has dropped to 9% in 2026. What's really interesting, though, is when you zoom in on the "18-29" age bracket. Normally young people are the people most excited about new technology, and old fuddy-duddies are the people resisting the new technology and saying we should stick to the old ways of doing things. But in this survey, the 18-29 age bracket showed the greatest change. For "More concerned than excited", the number for the 18-29 age bracket went from 31% to 55% between 2021 and 2026. The 65+ age bracket is still higher at 59%, but it didn't change quite as dramatically, going from 43% to 59% between 2001 and 2026. For "More excited than concerned", the number for the 18-29 age bracket went from 18% to 11% between 2021 and 2026. The 65+ age bracket is still lower at 4%, but again didn't change quite as dramatically, going from 11% to 4% between 2001 and 2026. Overall, when asked, whether AI will lead to fewer jobs, more jobs, or will not make much difference, in 2024, 64% of Americans said fewer jobs, but in 2026 that number increased to 71%. The number both years was 5% for "more jobs", so it looks like people went from "will not make much difference" to "fewer". Wow, that's pretty remarkable -- 71% think AI will lead to fewer jobs, 5% will think it will lead to more, yet we rush full speed into the AI future. OMG, I remember, back at the early future salons back in the early 2000s, I would tell people AI would automate all jobs and that job automation was the proper way to measure whether AI has reached parity with humans, and people would be like, "I'm so smart, AI will never automate my job!" "I'm so good at my job, there's no way AI could ever automate my job! I have nothing to worry about!" Now, here we are, 20+ years later, and 71% think AI will lead to fewer jobs and only 5% think AI will lead to more jobs. I think there is an important insight here: Those people who said "AI will never automate my job" were correct if you were looking at the "AI" of the early 2000s and any linear extrapolation of the technology that existed at the time. The most brilliant people might have realized the stochastic gradient descent and backpropagation algorithms would actually start working once enough computing power and data was available. But think about the algorithms that have been invented since then. Reinforcement learning, which enabled computers to first beat Atari games, then the best human players at the Chinese game of Go. The transformer architecture, which unlocked language translation and when combined with reinforcement learning from human feedback (RLHF) eventually led to chatbots, and, with reinforcement learning with verifiable rewards,now language-based agents. Diffusion models, which made AI-generated images and video possible. Anybody today who says, "AI will never automate my job" is failing to take into account the things that haven't been invented yet that will be invented -- and probably soon. Anyway, let's get back to the survey. When you look at the 18-29 age bracket, "fewer" went from 61% to 73%. The 30-49 age bracket is 74% "fewer", and the 50-64 age bracket is 72% "fewer". Only the 65+ had a significantly lower number -- 63% -- still way over 50%, but the lowest on the survey. Most people over 65 are retired, which might have something to do with it.

"nanoAlphaZero is a game-agnostic, high-performance implementation of AlphaZero. It reaches perfect play in games like Hex, and grandmaster-level strength in chess." "How is this different from other implementations?" "It scales to chess. Not a toy AlphaZero implementation. It can train a grandmaster-level chess model in under 24h on a TPU." "Genuinely game-agnostic. We validate the core logic across Hex, Connect4, Go, and Chess, and demonstrate how to train AlphaZero on custom games of your own using a Colab notebook." "Training is one JAX function. Self-play, MCTS, and training are fused into a single jitted call." "It's dead simple to run. Clone the repo, then uv run train --env chess." "It's fast. Our custom, TPU-native JAX environments run orders of magnitude faster than the reference implementation. For MCTS, we parallelize the sequential halving algorithm from Gumbel MuZero via mctx." If you've always wanted to run your own AlphaZero, here's an open source implementation that'll get you a long way there. There's a link that says, "demo: play against the models". I clicked it. I'm not a very good chess player, but I could tell right away its style of play is very different from Stockfish.