Podcast
The Lindahl Letter
Hosted by Dr. Nels Lindahl · EN
11episodes
Episodes
Newest firstAll episodes
Welcome to 2026 and beyond
Jan 300:04:26Tap to summarizeThank you for tuning in to week 220 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “Welcome to 2026 and beyond.”This last week has been about being a reflective practitioner and thinking about where we have been throughout the last year of the Lindahl Letter. This last year we covered research notes numbered from week 175 to 219. Back in June I did acknowledge a 56 day posting break in 2025 which is interesting to look back on now as an opportunity to reflect and build something substantial going forward. Toward the end of the year we got back into the groove of quality weekly missives which is good and something to continue. My focus on quantum, robotics, and AI seems to hold true to my roots of being generally interested in technology.Overall, my general interest in technology is what drives my interest in lifelong continuous learning. With that context being set it is probably easy enough to set the expectation that in 2026 and beyond the Lindahl Letter will be targeted toward the production of weekly research notes that are accessible, targeted, and focused. These missives will require less than 10 minutes of a reader’s time and should be a clear value add in terms of gaining knowledge, understanding, and context for complex technical content.Let’s establish the theoretical home base of this writing enterprise for 2026 which will be set on the foundation of digging into the edge of realized technology. That topic might sound familiar from week 212 of the Lindahl Letter. During that writing project we took a look at what technology is likely to be realized in the next 30 years. That coverage included looking at the metaverse, robotics, climate tech, space economy, biotech, synthetic biology, neurotech, and even fusion. I do believe that we will see quantum, robotics, and some AI mixed into that soup of potentially realized technology.All of that technology will see advancement and it will certainly be moving toward the edge of becoming realized technology. That is fundamental where it goes from being exploratory and research driven to being in production out in the wild where it will eventually become commoditized unless a clear winner breaks away and can hold onto a real advantage. I’m pretty skeptical about any of these technologies having a clear moat that allows that advantage. For the most part once a group of people know how to do these things the technology will be realized and break out into wider use.My primary weekly writing focus will be the Lindahl Letter and this is the place you will be able to find out what topics grab my attention and I consider to be worth sharing. My focus in the last 90 days has been heavily on quantum computing which is understandable due to how close it is getting to be a realized technology. We are on the edge of people figuring out how to demonstrate quantum supremacy for use cases and building these things into data centers as a clear value add for corporate customers and research labs that can afford to be a part of the journey. Outside of that, most of the major quantum computers that will be part of the early wave demonstrating the technology will be tied to either a research lab or corporate R&D group.Those early systems are starting to really scale up focus on specific advances in the quantum space. My research project in that space helped me to focus on open-access nanofabs, national laboratories, commercial foundry services, and captive industrial fab sites. Each of those groups has different advantages and research interests. We will see where the ultimate breakthroughs end up coming from as the story unfolds toward realized quantum technology.That is where we are heading throughout 2026. Thank you for being here for the journey and I look forward to learning more about technology and digging into the frontier of what will be realized this year. Overall the state of the Lindahl Letter is strong and we should be able to continue moving forward on our weekly journey of exploration into technology.What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
2025 End of Year Recap
Dec 2700:01:07Tap to summarizeThank you for tuning in to week 219 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “2025 End of Year Recap.”Thank you for being here! The Lindahl Letter this week started out as a Merry Christmas and Happy Holidays post and ended up just being an end of year recap. As the year comes to a close, I am taking a brief pause from publishing this week to spend time with family, recharge, and reflect on the remarkable conversations and ideas we have explored together throughout the year. If you are reading this one, then you certainly learned about AI/ML/AGI, robotics, and quantum computing this year. The Lindahl Letter will return to its regular schedule next year, and I am grateful for your continued readership, curiosity, and engagement. I wish you and yours a happy holiday season and a thoughtful, restorative start to the new year.My top 5 posts of 2025 included:What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
Nested learning and the illusion of depth
Dec 2000:07:34Tap to summarizeThank you for tuning in to week 218 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “Nested learning and the illusion of depth.”Just for fun with this nested learning paper we are evaluating today, I downloaded the 52 page PDF and uploaded it to my Google Drive to have Gemini create an audio overview of the paper. That is just a one button request these days. We have reached a point where we can easily listen to a paper recap with very little friction. It’s actually harder to get a complete reading of the PDF as an audio file. I had tried the Adobe Acrobat read aloud feature and I don’t really like the robotic output. Sometimes, I would rather listen to a paper than read it when I am trying to really think deeply about something. The 5 minutes of podcast audio Gemini spit out about the paper are embedded below. It’s interesting to say the least how quickly Gemini turned that paper into a short podcast. It’s entirely possible that my analysis might be less entertaining than the podcast Gemini created on the fly. You will be the judge of that one. This is a paper I actually printed out 2 pages per page using the double sided setting. That is how I used to read papers during graduate school. This paper had a few color elements that is something my graduate school papers never really had. They were all monochromatic. I had to put on my reading glasses and hold the paper a little closer than I used to with the 2 pages per page printing. I’ll have to remember to just print using single page spacing next time around. I really only print out papers I want to keep in my stack of stuff. This one certainly fits that criteria.Trying to make content that is accessible is one of the reasons that I have been recording audio for the Lindahl Letter. Sometimes listening to something is a great unlock. Other times due to complexity and the diagrams included you just have to read academic papers. I try to bring things forward without complex charts in a highly consumable way. My take on research notes is that they need to be generally understandable and communicate a clear take on whatever topic is being covered. The content has to be condensed into something that can be considered in 5-10 minutes. To that end I’m going to do my best to bring this paper on nested learning to life today.This paper matters, it really does, because the research presented undermines one of the core assumptions driving modern AI investment and the endless LLM building and training that has been occurring, namely that stacking more layers reliably produces qualitatively better intelligence [1]. The mantra to just keep scaling maybe will fade away. If many so-called deep models collapse into shallow equivalents during training, then reported gains attributed to architectural depth may instead be artifacts of data scale, regularization, or optimization heuristics rather than true representational progress.This has direct implications for benchmarking, since comparisons that reward parameter count or depth risk overstating advances that do not translate into more robust reasoning or generalization. It also affects hardware and infrastructure strategy, because enormous resources are being allocated to support depth that may not deliver proportional returns. At a deeper level, the result forces a reconsideration of what meaningful learning progress actually looks like, shifting attention from surface complexity toward mechanisms that introduce genuinely new inductive structure and adaptive behavior.Maybe the long term impact of this call out is likely to be gradual rather than abrupt, but it meaningfully shifts the intellectual ground beneath current AI narratives [1]. The paper in question provides a formal vocabulary for a concern many researchers have held intuitively, that architectural depth has become a proxy metric for progress rather than a principled design choice. Over time, this reframing may influence how serious research groups evaluate models, placing more weight on identifiably distinct learning mechanisms, training dynamics, and robustness properties instead of raw scale.It is unlikely to immediately change the minds of investors or vendors whose incentives favor larger systems, but it can shape academic norms, reviewer expectations, and eventually benchmark construction. Historically, results like this matter most not because they halt a paradigm, but because they constrain it, narrowing the space of credible claims and forcing future advances to justify themselves on grounds other than appearance and size.This argument intersects directly with my broader concerns about interpretability and generalization. I am still curious about creating a combiner model, but this might change the mechanics of how that might ultimately work. If performance gains arise primarily from optimization dynamics rather than architectural expressivity, then claims about learned representations should be treated with caution. Apparent abstraction may not correspond to stable semantic structure but to transient equilibria shaped by training order, learning rates, and implicit regularization. This aligns with growing skepticism about whether large models truly learn hierarchical concepts or merely approximate them through iterative adjustment [2].The implications extend beyond theory. Nested learning reframes debates about model scaling, architectural novelty, and transfer learning. It suggests that progress may come less from ever deeper networks and more from better understanding and controlling learning dynamics. This has practical consequences for reproducibility, safety, and deployment, since nested optimization can introduce path dependence and sensitivity to training regimes that are difficult to observe or audit.In the broader context of the AI marketplace, this work reinforces a recurring theme. Fluency and performance do not necessarily imply understanding. As with recent neuroscience critiques of language models, nested learning highlights how impressive outputs can emerge from mechanisms that lack stable, interpretable internal structure [3]. That gap matters when systems are deployed in high stakes environments where reliability, robustness, and reasoning are essential.We will see how this plays out in 2026 and what new research will ultimately shift the landscape.Footnotes:[1] Behrouz, A., Razaviyayn, M., Zhong, P., & Mirrokni, V. “Nested learning: The illusion of deep learning architectures.” Advances in Neural Information Processing Systems 39 (2025). https://abehrouz.github.io/files/NL.pdf[2] Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., & Dickstein, J. “On the expressive power of deep neural networks.” Proceedings of the 34th International Conference on Machine Learning (2017). https://arxiv.org/abs/1606.05336[3] Riley, B. “Large language mistake: Cutting edge research shows language is not the same as intelligence.” The Verge (2025). https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problemsWhat’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
The great 2025 LLM vibe shift
Dec 1300:05:56Tap to summarizeThank you for tuning in to week 217 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “The great 2025 LLM vibe shift.”Vibe shifts came and went. People are certainly adding the word vibe to all sorts of things as the initial meaning has ironically faded. Casey Newton in the industry standard setting Platformer newsletter wrote about a big silicon valley vibe shift in 2022 [1]. It was a big thing; until it wasn’t. The really big completely surreal LLM shift has happened toward the tail end of 2025. We went from extreme AI bubble talk to very clear, rational, and thoughtful perspectives on how LLMs won’t realize the promises that have been made. Keep in mind the market fears of an AI bubble are different from the understanding that LLMs might be the technology that ultimately wins. All of the spending in the marketplace and the academic argument may get reconciled at some point, but we have not seen that happen in 2025.The backward linkages of how potential technological progress regressed may not have been felt just yet, but the overall sentiment has shifted. The ship has indeed sailed. Let that sink in for a moment and think about just how big a shift in sentiment that really happens to be and how it just sort of happened. As OpenAI and Anthropic move toward inevitable IPO, that shift will certainly change things. Maybe the single best written explanation of this is from Benjamin Riley who wrote a piece for The Verge called, “Large language mistake: Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it” [2]. I owe a hat tip to Nilay Patel for recommending and helping surface that piece of writing.I was skeptical at first, but then realized it was a really interesting and well reasoned read. I’ll admit at the same time, I was also reading a 52 paper from the Google Research team, “Nested Learning: The Illusion of Deep Learning Architecture” around the same time which was interesting as a paired reading assignment [3]. More to come on that paper and what it means in a later post. I’m still digesting the deeper implications of that paper.Maybe to really sell the shift you could take a moment and listen to some of the recent words from OpenAI cofounder Ilya Sutskever. I’m still a little shocked about the casual way Ilaya described how we moved from research and the great AI winter, to the age of scaling, and finally back to the age of research again. The idea that scaling based on compute or size of corpse won’t win the LLM race is a very big shift and Ilya makes it pretty casually during this video.You will notice I have set the video to play about 1882 seconds into the conversation:Maybe a video with a really sharp looking classic linux Red Hat fedora in the background featuring a conversation between Nilay Patel and IBM CEO Arvind Krishna can help explain things. Don’t panic when you realize that the CEO of IBM very clearly argues with some back of the envelope math that all the data center investment has no real way to pay off in practical terms or an actual return on investment. Try not to flinch when it is described that within 3-5 years the same data centers could be built at a fraction of the current cost. Technology does just keep getting better. The argument makes sense. It is no less shocking based on the billions being spent.I set the video to start playing 502 seconds into the conversation.The argument that I probably prefer in the long run is how quantum computing is going to change the entire scaling and compute landscape [4]. The long-term argument that may end up mattering the most suggests that quantum computing will transform the economics of scale and ultimately reset expectations about what is computationally feasible. Former Intel CEO Pat Gelsinger recently framed quantum as the force likely to deflate the AI bubble by altering the fundamental relationship between compute and capability, a claim that is gaining analytical support across the research community. We may see it be an effective counter to the billions being spent on data centers for a late mover willing to make a prominent investment in the space or it could just end up being Alphabet who is highly invested in both TPU and quantum chips [5].What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead!Footnotes:[1] Newton, C. (2022). The vibe shift in Silicon Valley. Platformer. https://www.platformer.news/the-vibe-shift-in-silicon-valley/[2] Riley, B. (2025). Large language mistake: Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it. The Verge. https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems[3] Behrouz, A., Razaviyayn, M., Zhong, P., & Mirrokni, V. (2025). Nested learning: The illusion of deep learning architectures. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. https://abehrouz.github.io/files/NL.pdf[4] Shrivastava, H. (2025). Quantum computing will pop the AI bubble, claims ex-Intel CEO Pat Gelsinger. Wccftech. https://wccftech.com/quantum-computing-will-pop-the-ai-bubble-claims-ex-intel-ceo-pat-gelsinger/[5] Yahoo Finance, “Alphabet CEO just said quantum computing could be close to a breakthrough,” https://finance.yahoo.com/news/alphabet-ceo-just-said-quantum-155229893.html This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
The 5 biggest unsolved problems in quantum computing
Dec 600:09:46Tap to summarizeThank you for tuning in to week 216 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “The biggest unsolved problems in quantum computing.”The field of quantum computing has accelerated rapidly during the last decade, yet its most important breakthroughs remain incomplete. The core research challenges that stand between today’s prototypes and large scale, industrially relevant systems are now visible with unusual clarity. I think we are on the path to seeing this technology realized. These challenges are increasingly framed not as incremental milestones but as structural bottlenecks that shape the entire trajectory of the field. This week’s analysis focuses on the five most critical problems that must be solved for quantum computing to reach fault tolerant, economically meaningful operation. These gaps define where research investment, national strategy, and competitive advantage will be determined in the coming decade.1. A fully fault tolerant logical qubit with logical error rates below thresholdThe first and most fundamental problem is the absence of a fully fault tolerant logical qubit. I know, I know, people are getting close, but this technology is not fully realized just yet. Theoretical thresholds for fault tolerance are well studied, and progress has been reported through surface codes, low density parity check codes, and recent advances in magic state distillation. Several groups have demonstrated logical qubits whose performance exceeds their underlying physical qubits, and some trapped-ion experiments now show better than break-even behavior under repeated rounds of error correction. However, no team has yet realized a logical qubit that maintains below-threshold logical error rates in a fully integrated setting that combines encoding, stabilizer measurement, real time decoding, and continuous correction across arbitrarily deep circuits. Experiments such as the University of Osaka’s zero level magic state distillation results and Quantinuum’s recent logical circuit demonstrations illustrate meaningful progress, yet a complete fault tolerant logical qubit build rolling off the assembly line has not been achieved [1]. This missing element prevents reliable execution of deep circuits and stands as the central research challenge of the field. I am also tracking a leaderboard of efforts aimed at increasing the number and stability of logical qubits as new systems emerge [2].2. A scalable and manufacturable quantum architecture that supports thousands of high fidelity qubitsThe second unsolved problem is the absence of a scalable, manufacturable quantum architecture capable of supporting thousands of high fidelity qubits. Superconducting platforms continue to face wiring congestion, cross talk, and fabrication variability across large wafers, which limits reproducibility at scale. Trapped-ion systems achieve some of the highest gate fidelities reported, but their physical footprint, control volume, and relatively slow gate speeds constrain system growth. Neutral atom arrays offer large qubit counts, yet they have not demonstrated uniform, high fidelity two qubit gates across arrays large enough to support fault tolerant codes. Photonic and spin qubits continue to advance but remain earlier in their development for universal, gate based architectures. Across all platforms, the transition from laboratory systems to repeatable, wafer scale manufacturing has not occurred. Most resource estimates indicate that tens of thousands of physical qubits will be required for practically useful, error corrected applications, and no architecture is yet positioned to deliver this scale with consistent fidelity. I am tracking universal gate based physical qubit leaders closely, and I expect to see significant shifts in 2026 as fabrication strategies evolve [3].3. Integrated cryogenic classical control systems capable of real time decoding at scaleThe third unsolved problem concerns the integration of classical control systems capable of operating efficiently at cryogenic temperatures. Quantum processors rely on classical electronics to generate precise control pulses, read measurement outcomes, and perform real time decoding. As devices grow, these classical requirements become a dominant engineering bottleneck. Current systems depend on extensive room temperature hardware and thousands of coaxial lines, an approach that is not viable for scaling beyond a few hundred qubits. Research into cryogenic CMOS, multiplexed readout architectures, and fast low noise routing has shown meaningful progress, and prototype decoders have demonstrated sub microsecond performance. However, the field still lacks a fully integrated classical to quantum control stack that can operate near the device, support large scale decoding throughput, and eliminate the wiring overhead required for million channel systems. Solving this challenge is as essential as improving qubit fidelity, because fault tolerant computation will require tightly coupled classical and quantum subsystems functioning in real time at cryogenic depths.4. A modular, networked quantum architecture with reliable chip to chip entanglementThe fourth major unsolved problem involves modularity and quantum networking. Large scale quantum computers will not be monolithic systems. They will require distributed architectures in which multiple chips or modules exchange entanglement to support error corrected computation across larger systems. Research groups have demonstrated chip to chip photonic links, heralded entanglement generation, and short range coupling between trapped-ion and superconducting devices, but these demonstrations remain small scale and experimental. No team has yet produced a modular architecture capable of sustaining reliable inter module entanglement rates, routing operations, and error corrected logical circuits across networked components. A practical quantum interconnect, whether photonic or microwave based, would redefine system design by enabling large logical qubit counts without relying on a single monolithic wafer. Developing these networked architectures is now seen as one of the highest value targets for national research programs, because modularity is likely the only viable path to systems with millions of physical qubits.5. A verified quantum advantage tied to a real scientific or industrial workloadThe fifth unsolved problem is the absence of a widely accepted, independently verified quantum advantage tied to a real scientific or industrial workload. Quantum supremacy experiments have demonstrated that certain random circuit sampling tasks are exceptionally difficult for classical systems to simulate, but these tasks do not translate into chemistry, materials, optimization, or cryptography workloads. Several vendors have recently reported domain specific quantum advantages, including applications in quantum navigation and narrow optimization tasks, but these demonstrations have not yet achieved broad community validation or independent replication under strict verification and resource accounting. A robust demonstration of advantage requires a computation that is infeasible for classical systems within realistic time and energy constraints, produces an output that can be meaningfully verified, and operates using real hardware error rates rather than idealized gates. Achieving this milestone would mark a decisive shift in the strategic landscape of the field and would accelerate commercial investment into fault tolerant platforms.Together, these five problems outline the most important questions I’m tracking that are facing quantum computing today. This is based on my research interests. Please feel free to let me know if something else jumps out when you read this list. Each topic represents an opportunity for technical leadership, research investment, and industrial strategy. That does not mean my list is complete. It’s directionally accurate for late 2025, but things in the quantum computing space are changing rapidly. These elements called out also define the hurdles that stand between early laboratory demonstrations and the large-scale quantum platforms required for transformative scientific progress.What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead!Links I’m sharing this week!You may not have watched Linus Torvalds build a computer on your watch list for 2025, but I’m sharing that link anyway. I truly enjoyed watching this video.This video made me chuckle several times and was delightful.Footnotes:[1] Itogawa, T., Takada, Y., Hirano, Y., & Fujii, K. (2024). Even more efficient magic state distillation by zero-level distillation. arXiv preprint arXiv:2403.03991. http://arxiv.org/pdf/2403.03991[2] Top quantum computers by logical qubit[3] Updating my top 10 quantum computer leaderboard This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
Process capture and the future of knowledge management
Nov 2900:04:19Tap to summarizeThank you for tuning in to week 215 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “Process capture and the future of knowledge management.”The history of knowledge management has been shaped by repeated attempts to store, retrieve, and reuse organizational insight. So much institutional knowledge gets lost and discarded as organizations change and people shift roles or exit. People within organizations learn through the every day practice of getting things done. It’s only recently that systems are augmenting and sometimes automating those processes. Early systems focused on document repositories, and later platforms emphasized collaboration, tagging, and collective intelligence. We now find ourselves in a period where knowledge management converges with automated workflows and computational assistants that can observe, extract, and generalize decision patterns. We are seeing a major change in the ability to observe and capture processes. Systems are able to capture and catalog what is happening. This creates an interesting inflection point where the system may store the knowledge, but the users of that knowledge are dependent on the system. That does not mean the process is understood in terms of the big why question. Scholars have noted that the operational layer of organizational memory is often lost because it resides in informal practices rather than formal documentation. The shift toward embedded and automated capture offers a remedy to that problem.The rise of agentic AI and workflow-integrated assistants alters the knowledge landscape by making it possible to synthesize procedural knowledge in real time. Instead of relying on teams to manually update wikis or define operating procedures, modern systems can extract key steps from repeated actions, identify dependencies, and flag anomalies that deviate from observed patterns. This transforms knowledge management from a static library into a dynamic computational environment. What exactly happens to this store of knowledge over time is something to consider going forward. Supervising the repository will require deep knowledge of the systems which are now being maintained systematically. Maintaining and refining it will be the difference between sustained institutional knowledge or temporary model advantages that drop with the next update. Recent studies on digital trace data argue that high fidelity observational streams can significantly improve the accuracy of organizational models. When this data flows into agents capable of modeling tasks, predicting outcomes, and recommending actions, the role of knowledge management shifts from storage to orchestration.Process capture also introduces new opportunities for long-horizon learning systems. This is the part I’m really interested in understanding. The orchestration layer has to have some background learning and storage that runs periodically. When workflows are automatically translated into structured representations, organizations can run simulations, perform optimization, and enable higher levels of task autonomy. These capabilities begin to resemble continuous improvement environments that merge human judgment with machine-refined operational insight. Researchers have observed that structured process models can improve downstream automation and decision support, particularly in complex enterprise settings where procedures evolve rapidly. This suggests that the next phase of knowledge management will involve systems that not only store information but also refine it through computational analysis and real world feedback. It’s in that refinement that the magic might happen in terms of real knowledge management.What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead!Links I’m sharing this week!https://www.computerworld.com/article/4094557/the-world-is-split-between-ai-sloppers-and-stoppers.htmlThis video is a super interesting look at a number we don’t normally question on a daily basis. The delivery style is a bit bombastic, but the fact check on the argument is interesting. You know I enjoy numbers and was really curious how this was calculated. That video referenced this widely shared analysis from Michael W. Green on Substack. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
The great manufacturing reset
Nov 2200:07:20Tap to summarizeThank you for tuning in to week 214 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “The great manufacturing reset.”Boston Dynamics captured public imagination when they introduced Spot the dog-like robot back in 2016. Things have changed. Robots that walk around are beginning to enter the commercial landscape, and new entrants continue to appear. A humanoid robot product from Russia built by the company Idol surfaced last week [1]. Other companies such as Agility Robotics (USA), Figure AI (USA), Boston Dynamics (USA), UBTECH (China), and 1X Technologies (Norway/USA) are all working toward delivering humanoid robots. Optimus, the Tesla bot introduced conceptually in 2021 and now in its third-generation prototype which remains part of an internal program and has not yet reached commercial deployment is also being talked about.The stage is now set, and we are at a point where robotics, autonomous fabrication systems, and advanced materials are converging into a new industrial baseline. The last decade brought low-cost filament printers into hobbyist and commercial spaces at massive scale, and the next decade is poised to move far beyond that early wave. Industrial additive manufacturing has already expanded into metals, composites, and high-performance polymers, with global revenue expected to accelerate over the coming years. At the same time, the field is absorbing rapid advancements in AI-enabled calibration, defect detection, and real-time optimization, allowing machinery to tune production parameters autonomously. That capability shifts what it means to operate a modern fabrication workflow. Things are changing rapidly.Alongside these developments, humanoid and semi-autonomous industrial robots are transitioning from research demonstrations to contract manufacturing deployments. Several builders are scaling up pilot programs in which general-purpose robots support assembly, materials handling, and repetitive manufacturing tasks. These systems benefit from advances in reinforcement learning, enhanced sensors, and cloud-based model updates. Industrial robotics shipments are increasing rapidly, driven by global demand for flexible production lines and labor-augmentation strategies. The supply side of robotics is not only expanding but also becoming modular and more interoperable across fabrication environments.The most significant shift may come from the emergence of machines that build machines. That is a topic I’m focused on understanding. Historically, tooling design required long lead times, significant manual labor, and specialized expertise. Today, automated CAM pipelines, printable tooling, adaptive CNC systems, and robotically tended fabrication cells allow factories to generate and regenerate their own production processes. Some aerospace and automotive facilities already deploy these closed-loop systems to create fixtures, jigs, and replacement components internally. This form of self-manufacturing reduces dependency on external suppliers and removes friction from engineering iteration cycles. We are moving toward a world where design, testing, and tooling are all integrated within an AI-guided, robotics-driven feedback loop. That integration is the foundation of the great manufacturing reset.For the United States, these technologies open a realistic path to reshoring custom and small-batch manufacturing in ways that were not economically viable during the offshoring wave of the late twentieth century. Rising labor costs in traditional manufacturing hubs, geopolitical risk, and supply chain disruptions have already encouraged firms to reconsider where they build things. Additive manufacturing and flexible robotics change the cost structure by reducing reliance on large minimum-order quantities, expensive hard tooling, and long logistics chains. A factory that can print tooling on demand, deploy modular robots, and run AI-optimized production scheduling can serve shorter runs and more specialized designs while remaining geographically close to end customers. In effect, the United States can replace scale-driven arbitrage with speed, customization, and resilience. That is why we are at the inflection point for the great manufacturing reset.Policy and infrastructure are beginning to support this transition. Federal programs such as Manufacturing USA and its associated network of advanced manufacturing institutes are working to diffuse next-generation production technologies across domestic firms and regions [2]. Investments in semiconductor fabrication, battery plants, and clean-energy hardware have already catalyzed billions of dollars in new onshore manufacturing commitments. The same capabilities that support large facilities can extend to mid-market and smaller manufacturers through shared tooling libraries, regional robotics integrators, and standardized digital design pipelines. Universities and community colleges can align curricula with this reset by emphasizing mechatronics, robotics programming, and design-for-additive principles that translate directly to a modern factory floor.If the United States leans into this transition, the great manufacturing reset will not simply re-create legacy industrial capacity. It will establish a distributed network of automated, digitally coordinated micro-factories specializing in custom work, rapid prototyping, and short-run production. The strategic advantage will be the ability to move from concept to physical part in days instead of months, while retaining critical capabilities within domestic borders. The risk is that other regions may scale faster and capture the integrator role that coordinates robots, additive systems, and AI platforms across global supply chains. The next few years will determine whether the United States treats these technologies as incremental enhancements or as foundational infrastructure for a new manufacturing baseline. Ideally, this reset will create conditions for a new wave of startups delivering smaller manufacturing runs, bespoke development cycles, and entirely new product categories.Things to consider:* The economics of reshoring depend as much on automation and design speed as on wage differentials.* Policy support for advanced manufacturing will matter most where it connects directly to tooling, robotics, and workforce upskilling.* Custom, short-run production could become a core competitive advantage for regions that adopt additive and robotics early.* The integrators that connect robots, printers, and AI software may end up more powerful than any single hardware vendor.* Manufacturing resilience will increasingly be measured by how quickly domestic systems can reconfigure to new designs and shocks.What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead!Links I’m sharing this week!Footnotes:[1] Mesa, J. (2025, November 11). Russia ‘human’ robot falls on stage during debut. Newsweek. https://www.newsweek.com/russia-human-robot-falls-stage-during-debut-11031104[2] Manufacturing USA. (n.d.). Home. https://www.manufacturingusa.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
Why a “combiner model” might someday work
Nov 1500:05:42Tap to summarizeThank you for tuning in to week 213 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “Why a “combiner model” might someday work.”Open models abound. Every week, new open-weight large language models appear on Hugging Face, adding to a massive archive of fine-tuned variants and experimental checkpoints. Together, they form a kind of digital wasteland of stranded intelligence. These models aren’t all obsolete; they’re simply sidelined because the community lacks effective open source tools to combine their specialized insights efficiently. The concept of a “combiner model” offers one powerful path to reclaim this lost potential. Millions of hours of training, billions of dollars in compute, and so much electricity have been spent. Sure you can work by distillation to capture outputs from one model into another, but a combiner model would be different as it overlays instead of extracts.A combiner model represents a critical shift away from the assumption that AI progress requires ever-larger single systems. Instead of training another trillion-parameter monolith, we can learn to combine many smaller, specialized models into a coherent whole. The central challenge lies in making these models truly interoperable. The challenges form from questions around how to merge or align their parameters, embeddings, or reasoning traces without degrading performance. The combiner model would act as a meta-learner, adapting, weighting, and reconciling information across independently trained systems, unlocking the latent knowledge already encoded in thousands of open weights. Somebody at some point is going to make an agent that works on this problem and grows stronger by essentially eating other modals.This vision can be realized through at least three technical routes. The first involves weight-space merging. Techniques such as Model Soups and Mergekit show that when models share a common base, their weights can be effectively averaged or blended. More advanced methods, like TIES-Merging, learn adaptive coefficients that vary across layers, turning model blending into a trainable optimization process rather than a static recipe. In this view, the combiner model becomes a universal optimizer for reuse, synthesizing the gradients of many past experiments into a single, functioning network.The second approach focuses on latent-space alignment. When models differ in architecture or tokenizer, their internal representations diverge. Even so, a smaller alignment bridge can learn to translate between their embedding spaces, creating a shared semantic layer, or semantic superposition. This allows, for example, a legal-domain model and a biomedical model to exchange information while their original knowledge weights remain frozen. The combiner learns the translation rules, effectively building a common interlingua for neural representations that connects thousands of isolated domain experts.The third approach treats the combiner not as a merger but as a controller or orchestrator. In this design, the combiner dynamically decides which expert model to invoke, evaluates their outputs, and fuses the results through its own learned inference layer. This idea already appears in robust multi-agent frameworks. A true combiner model or maybe combiner agent would internalize this orchestration as a core part of its reasoning process. Instead of running one model at a time, it would simultaneously select and synthesize outputs from many experts, producing complex, context-aware intelligence assembled on demand. This approach is the most immediately viable and is already being used in sophisticated production systems today.If such systems mature, the economics of AI will fundamentally change. Rather than concentrating resources on a few massive, proprietary models, research will shift toward modular ecosystems built from reusable parts. Each fine-tuned checkpoint on Hugging Face will become a potential building block, not an obsolete artifact. The combiner would turn the open-weight landscape into an evolving lattice of knowledge, where specialization and reuse replace the endless cycle of frontier retraining. This vision is demanding, but the promise remains compelling: a world where intelligence is assembled, not hoarded; where the fragments of past experiments contribute directly to future understanding. The combiner model might not exist yet, but its underlying logic already dictates the future of open source AI.What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead!Links I’m sharing this week!This is the episode with Sam Altman that everybody was talking about. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
The edge of realized technology
Nov 800:05:08Tap to summarizeThank you for tuning in to week 212 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “The edge of realized technology.”Welcome to the start of season 5. Don’t panic, we are still covering advancing technology including quantum, robotics, and artificial intelligence within the Lindahl Letter. I’ll be writing about the intersection of technology and modernity until the singularity. For better or worse, modernity’s shadow will continue to be the edge of realized technology. We are on the path to seeing a bunch of different technologies end up being realized in the not so distant future. That is why I’m so focused on the path toward realizing robotics, quantum, and agentic. That is where season 5 of the Lindahl Letter is going to pick up and start to dig into those topics at the edge of realized technology. To that end, I started to make a graphic of the timeline of major financial bubbles and extended it out to emerging technologies expected to deliver before 2045 [1]. You can modify the Python visualization code for this one if you want, I shared an executable version of it on GitHub.Within that visualization I started to sketch out the next 10 most likely technologies we will see realized. Within each path toward realization is where private investment and ultimately retail investors will crowd into the market before it gets commoditized to the point where the initial leaders in the space have no first mover advantage and some type of bubble ensues. That does not mean these things won’t be game changing. I’m just expecting some type of financial crowding followed by pressure against expected profits that won’t be realized. Resulting from that would be some type of financial bubble which might very well be led by a huge windfall of some sort. People made money on tulips and pepper before those markets crashed out.* 2026, “AI Bubble”, “Tech”* 2028, “Metaverse and XR Bubble”, “Tech/Speculative”* 2029, “Robotics Bubble”, “Tech”* 2031, “Climate Tech Bubble”, “Climate Tech”* 2032, “Space Economy Bubble”, “Space Economy”* 2033, “Biotech and Longevity Bubble”, “Biotech/Longevity”* 2034, “Synthetic Biology and Food Tech Bubble”, “Synthetic Bio/Food Tech”* 2035, “Quantum Bubble”, “Tech”* 2035, “Neurotech and BCI Bubble”, “Neurotech/BCI”* 2040, “Fusion Energy Bubble”, “Energy”These edges of technology realization might not be in the right order or tied exactly to the right year, but I do think that directionally this list will prove to be an accurate prediction of when technology will be achieved and we will see meaningful changes to modernity. Futurist considerations abound for what might end up happening. This was my swing at predicting what’s next. Only time will tell if it was an accurate swing or it will be disrupted by some other emerging technology.Going forward you are going to see my weekly writing efforts get split into 4 distinct buckets. My general weekly think pieces will stay here within the relative safety of the standard Lindahl Letter publication, writing about civics, civility, and civil society will be over on the Civic Honors domain, blogging will be done within the Functional Journal, and my hope is to resume daily posting back over on the nels.ai domain. Ideally, enough content will be generated in the major domains that only a small amount of blogging will occur. Going forward it is far better to produce meaningful work than to complete passages of extended navel gazing. Sure being a reflective practitioner and blogging has its place, but sometimes all that writing about the process ends up being more circular than forward looking.What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead!Links I’m sharing this week!Footnotes:[1] https://github.com/nelslindahlx/Data-Analysis/blob/master/TimelineofMajorFinancialBubbles.ipynb This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.nelsx.com
Transcribe →
Spooky Halloween edition: When Satoshi-Era Wallets Wake Up
Oct 3100:06:56Tap to summarizeHappy Halloween everybody! Thank you for tuning in to week 211 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “When Satoshi-Era Wallets Wake Up.”Seriously, Bitcoin is weird. It has an enigmatic and anonymous founder. The origin story of how this cryptocurrency came to be is pretty much ineffable. Roughly a third of all bitcoin has never moved [1].These dormant or maybe abandoned coins shape both the scarcity and the psychology of the network. Now, some of those early wallets are coming alive again, and their reawakening reveals a deeper story about profit, security, and the bleeding edge of quantum cryptography. Maybe some of these cutting edge massive quantum computers are being used to run Shor’s algorithm and factor some of these older wallet keys [2]. That seems more likely to me than somebody remembering they had some old bitcoin after a decade and moving it around. We could write a really spooky short story about people waking up to old bitcoin wallets getting cracked by quantum computers running Shor’s algorithm. That is the type of short story that could move from fiction to non-fiction with one scientific breakthrough. It’s even possible it has already started to happen. By possible, I think it probably already is happening.Speculation aside, it’s true that an estimated thirty percent of all mined bitcoin has been untouched for more than five years [3]. That is shocking. About seventeen percent of bitcoins have not moved in a decade [4]. Those figures mean that even as mining nears completion, a huge fraction of the network’s supply remains functionally absent or potentially abandoned. This long-term dormancy amplifies Bitcoin’s scarcity, turning lost or forgotten coins into a silent deflationary force. Yet in 2025, something shifted. Several ancient wallets, first active during Bitcoin’s infancy, have begun to stir after twelve to fourteen years of silence. Their movements are rare, deliberate, and full of meaning.Some of these wallets trace back to 2010 and 2011, a time when bitcoin traded for less than a dollar. In July, eight early addresses moved roughly eighty thousand bitcoin in a coordinated set of transfers [5]. That is wealth that once totaled a few thousand dollars but is now worth billions. Somebody made some shocking profits. Later, a miner-era wallet from 2010 moved four hundred bitcoin after twelve years of dormancy [6]. In October, an early 2011 wallet that had accumulated four thousand bitcoin sent a small test transaction of 150 coins before going quiet again [7]. None of these events caused market disruption, but each drew immediate attention. Every time an ancient wallet moves, it feels like a fragment of Bitcoin’s early history is stepping into the present.Why are these early coins moving now? The first reason is straightforward economics. With bitcoin surpassing one hundred thousand dollars, even small transfers yield generational wealth. Another reason is technological maturity. Over the past decade, wallet recovery methods have improved, and holders who once misplaced keys or old software backups can now retrieve them. Security has also evolved. Many early wallets were built with primitive address types that expose their public keys, leaving them theoretically vulnerable to a future cryptographic breakthrough. This leads to the third and most forward-looking motivation: the quantum threat. That is the part I’m super curious about. Some of the larger quantum systems that I shared in my leaderboard could be active here, but we don’t really know.Quantum computing is still developing, but progress is steady. Bitcoin relies on elliptic-curve digital signatures that would be mathematically vulnerable to sufficiently powerful quantum machines. The earliest wallets used formats that make this risk more immediate, because they reveal public keys on-chain once a transaction occurs. If quantum computing advances far enough, those exposed keys could allow attackers to derive private keys and spend the coins. Experts estimate that a quarter of all existing bitcoin resides in such legacy formats. That reality has not escaped early holders. Some of the recent awakenings may reflect quiet migrations of classic wallet cold coins being moved to SegWit, multi-signature, or even post-quantum-resistant wallets to protect them from future compromise. These reactivations might not be about profit at all. They could be acts of defensive foresight from people who understand how close technology may be to challenging the foundations of digital security.There are also practical motivations. Estate planning, custodial audits, and consolidation are all normal parts of managing large digital holdings. After more than a decade, early miners are updating their records, creating inheritance plans, and transferring assets to institutional custodians. The act of moving coins from an old address is sometimes less a financial maneuver and more a generational effort to ensure those digital fortunes survive their original owners.Each time these wallets awaken, the community reacts with fascination and unease. The first question is always the same: could this be Satoshi Nakamoto? So far, none of the reactivated wallets match known Satoshi mining patterns, but the mythology persists. Beyond the curiosity, there’s the market anxiety that large moves might signal selling pressure. Yet most transfers have not flowed into exchanges. They seem measured, intentional, and quiet. In a sense, this is the opposite of panic: the calm movement of old wealth into modern systems that have better security.What we are witnessing is also a potential generational handoff. The early experimenters who mined coins on laptops are now confronting questions of succession and security that mirror those of traditional wealth. Their coins, once symbols of rebellion against institutions, are being integrated into structured estates, custodial frameworks, and long-term trusts. As these coins move, they pass through new layers of infrastructure and oversight, becoming part of a global financial fabric that looks very different from the anarchic beginnings of Bitcoin.As quantum computing advances and Bitcoin’s price continues to rise, more early wallets are likely to move. Some of those transfers will be tests or migrations; others may represent the quiet liquidation of immense fortunes. Watching these awakenings provides a rare link between the network’s origin story and its future. The early holders are not gone, some of them may have abandoned some bitcoins, or lost access, but some of them are simply preparing for the next phase of digital permanence, one where Bitcoin must prove its resilience against both time and the very real likelihood of advancements in quantum based technology.Summary of the key points on the quantum threat to bitcoin:* Quantum computing is advancing, and although no system has yet cracked the relevant cryptography used by Bitcoin, experts estimate that within the next 5-10 years some wallets using legacy address types could become vulnerable.* A significant fraction of Bitcoin’s supply (in some estimates around 25 %) is held in addresses whose public key has been exposed (or in older formats such as pay-to-public-key) and thus are considered more at risk from a “Q-day” style attack.* The network is already responding: developers have floated proposals (e.g., a draft BIP‑360) to freeze coins in vulnerable legacy addresses and force migration to quantum-resistant formats, with multi-phase transition plans.* The fact that early “Satoshi-era” wallets are waking up now may reflect not just profit or estate planning motives but also pre-emptive security behaviour by holders who recognise the quantum risk and wish to migrate coins to safer custody.* From a scarcity and supply-dynamics perspective, the quantum threat adds another layer of complexity: dormant coins may not just be inert, they may be targeted or moved due to security fears, altering how one thinks about long-term supply, holder behaviour and concentration risk.What’s next for the Lindahl Letter? New editions arrive every Friday. If you are still listening at this point and enjoyed this content, then please take a moment and share it with a friend. If you are new to the Lindahl Letter, then please consider subscribing. Make sure to stay curious, stay informed, and enjoy the week ahead!Links I’m sharing this week!Footnotes:[1] Van Straten, J. (2023, November 15). Record 70% of Bitcoin supply lies dormant for a year or more. CryptoSlate. https://cryptoslate.com/insights/record-70-of-bitcoin-supply-lies-dormant-for-a-year-or-more/[2] Chojecki, P. (2025, May 20). Quantum computers threat to Bitcoin: Q-Day, post-quantum cryptography and Bitcoin. Medium. https://pchojecki.medium.com/quantum-computers-threat-to-bitcoin-e1b57b0da2aa[3] AInvest. (2025, July 5). 30.4% of Bitcoin supply dormant for over five years. https://www.ainvest.com/news/30-4-bitcoin-supply-dormant-years-2507/[4] Crypto News. (2025, June 18). Over 3.4 million BTC, more than 17% of the total supply, have not moved in at least a decade. https://crypto.news/bitcoin-dormant-supply-growth-outpaces-issuance-2025/[5] Malwa, S. (2025, July 5). Eight Bitcoin wallets move 80,000 BTC in large...
Transcribe →