#systems thinking
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peter.users.garden
Best engineers use systems thinking as boundary-setting, not a detour: name 1–2 invariants, ship the smallest slice that respects them, and push the rest into follow-up artifacts.

Template: Invariant(s) → v1 slice → “reserved for v2”. What would you add?
February 8, 2026 at 6:30 AM
epsteinweb.bsky.social
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cognition; the habits of connected thinking already inform our decisions and mark roads to new knowledge. The inevitability of AI reflects an inescapable logic at work now: We want faster better and smarter systems. We want to compress time. But the faster our world gets, the more it slips beyond a pace of human management. Al steps in. It makes the system function faster. Keeps itself safe. Us too.62 Better-than-human AI inside these “representational” grids doesn’t vanish like it did in Maes’ lab. In fact, an honestly artificial intelligence is their nature of their strange essence. They will use it not simply to contemplate the world, to help us along, but also to confront what has never been seen, to see and then coldly manipulate any topology of power they can reach. Of course we'll still continue to think about the world; but the world, a wired and alive and cogitating cage, will think about us too,.26 3. 262 Us too: Heinl, p. 53 263 Of course: Nigel Thrift and Shaun French, “The Automatic Production of Space”, Trans Inst Br Geogr NS 27 309-335 2002 192 HOUSE_OVERSIGHT_018424
February 8, 2026 at 5:59 AM
epsteinweb.bsky.social
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traditional thinking. Co-evolution exists as a real possibility for the two nations but so too does co-extinction, a problem of mis-designed complex systems that can rattle themselves apart. This is precisely what a confident Hard Gatekeeping strategy can avoid. When we speak of the possibiltity of a new type of great power relations it is because so much of future power will be decided on a topological landscapes, on that nearly living surface that is marked by such different rules than the older, industrial power maps. It takes a network to fight a network, as we've said. Networks confront us, constantly now, with the unexpected. No one forming the IMF in 1949 considered digital currency. No one developing arms protocols in the 1990s thought about cyber weapons. Artificially intelligent weapons, migrant waves, income fractures - these and other emerging puzzles were never contemplated in our existing international arrangements. Collectively attacking these challenges is not merely more efficient; it is our only option. Yes, it’s easy enough to picture the US and China battling each other over islands and protocols and technology structures in the decades to come. Certainly this possibility has to be prepared for. But this is to regard the world in industrial terms. Ask yourself: What, really is the point of power? It is to secure stability, not to tip the world to chaos just because we think we might be better off on the other side. The networks tell us what they want. They want gates. Our only question - and it is the same question that lingers in Beijing and Washington - is: Are we smart enough to listen? 5. How will power distribute itself in the future? Just what will the network of nations and datawebs and insurgents finally look like when it settles into some predictable if still roiling order ten or a hundred years hence? Will it be made up of different walled and gated systems, linked at certain moments, unplugged at others? Or might it collect with that
February 8, 2026 at 5:57 AM
muninsdad.bsky.social
It's also worth thinking about what elements of life needed to be touched by the state. For much of recorded history the govt was explicitly involved in maintenance of caste or similar systems and the idea that they wouldn't be was seen as crazy. Disorder! You can't have serfs just picking jobs!
February 8, 2026 at 5:54 AM
epsteinweb.bsky.social
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build walls. It is to use the logic of networks and gates, and all the complex productive force they release. 3. The elements of Hard Gatekeeping can be stated simply enough: First, Does it work for us? We seek a landscape of technology, trade, finance, knowledge - all the tools of power - that balances the pressures of politics with a colder grasp of longer-term national interest. The protocols, alliances, treaties and data webs that enable markets, trade or thinking machines - all are gatelands we can cultivate, improve and protect. Our gatelands must be designed to limit the rippling risks of contagion or error moving wildly, like spring weather, inside linked complex systems now. Control of topologies is what air superiority or sea mastery once were. The opening attacks of future wars will come invisibly and silently through networks, in space, in fast topological strikes - not with noisy land invasions or bombing runs. Well-built gates will give us time and leverage. They will 247 “In anarchy”: Kenneth Waltz, Theory of International Politics, (Reading, MA: Addison-Wesley, 1979), 126 248 It establishes: For a discussion about why a grand strategy is of practical use see Hal Brands, “The Promise and Pitfalls of Grand Strategy,” Strategic Studies Institute, External Associates Monograph Program, 2012 173 HOUSE_OVERSIGHT_018405
February 8, 2026 at 5:53 AM
epsteinweb.bsky.social
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The computing programming technology that lingers inside most data centers now and that makes sense of the ceaselessly arriving bits - Here is information ona billion people. How many are named Bob? - is known as MapReduce. The name combines two well-know computer fuctions, Map and Reduce, and it is just the sort of program Janelle and Hillis would have dreamed up, had they ever met. MapReduce divides computation work among thousands of machines, just the sort of massive parallelism Hilli had in mind. What it implicitly does, however, is crunch what might have taken years into microseconds. If the charmed phrase of James’ stately Victorian elites more than a century ago was “Summer Afternoon,” ours might be “MapReduce’”, a kind of magic two word code for a whole way of living and thinking. 5. The most successful political and economic systems of the past, the ones that marked the winning “convergence” club of humanity’s last great leap, let people liberate themselves into a life of their dreams. Liberty meant tearing down old barriers to influence and security and knowledge; tipping over the Bastille, escaping colonialism. And it meant, too, providing a scaffolding of education, of social support, of laws and stability for citizens. The industrial, urban and rich countries that populate our world now evolved that way because their citizens made them so. They escaped the tyranny of the pre-modern, of a world where the small town you were born in and the pattern of your parents’ life were a kind of prision for your hopes. This was the whole trick of the Enlightenment, after all: What do you want to be? You decide! What do you want your nation to be? Decide that too. Dare to know! Look ahead now. The very best future political and economic arrangements will need to do something more than simply liberate us. They will have to enable and permit citizens to compress time. Just as the idea of a democracy was shocking once, this concept of a system tuned not merely for
February 8, 2026 at 5:43 AM
manavgandhi27.bsky.social
One key takeaway for me was thinking in systems, not just code—building flexible, maintainable solutions instead of quick fixes.
February 8, 2026 at 5:36 AM
epsteinweb.bsky.social
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and normal ones, people made weird by technological manipulation and those who have note - they will all, inevitably, live side by side. The more essential machines become to our connected lives, the more avidly weird and hacked the networks will become. The incentive to whistle up control of the systems, and control of those of enmeshed in them, is the only thing that seems to grow faster than the system itself. a This vulnerability of connected systems is an important mile marker in our route towards understanding some principles of power in a connected age. “Read over and over again the campaigns of Alexander, Hannibal, Caesar, Gustavus, Turenne, Eugene and Frederick,” Napoleon wrote once. “Make them your models. This is the 147 | was thinking: Dan Geer, “Cybersecurity as Realpolitik”, speech delivered at BlackHat, August 6,2014 148 Perhaps: Joshua Cooper Ramo “Talking Cyberthreat with China”, International Herald Tribune, July 10, 2013 149 Little surprise: Linder Gayten Back to Basics p. 58 105 HOUSE_OVERSIGHT_018337
February 8, 2026 at 5:33 AM
epsteinweb.bsky.social
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intelligence agencies and criminal organizations. It’s like discovering a closet full of spies in your house, each being careful not to step on the other's toes as they watch and listen to your life. Why is my computer so slow, a government official in a Eurasian capital might ask. It is because it has been simultaneously pwned by Americans, Russians, Israelis, Chinese, and maybe a local Mafioso or two - and their code is not running smoothly. A couple of years ago I had a naive moment when | thought, perhaps, it would be possible and in everyone’s interest to go back to those simpler, innocent Hacktic days, when information about vulnerabilities was widely shared and easily discussed - and holes were quickly patched as a result. I was thinking about the problem of cybertension between the US and China and suggested applying an 145 “However”: Jeffrey C. Mogul, “Emergent (Mis)behavior vs. Complex Software Systems”, HP Labs Research Papers, 2006, HPL-2006-2 146 “We are not experts”: Stephen Cobb and Andrew Lee, “Malware is Called Malicious for a Reason: The Risks of Weaponizing Code” in P. Brangetto, M. Maybaum, J. Stinissen eds., 6% Annual Conference on Cyber Conflict (NATO Publications, 2014) 71-82 104 HOUSE_OVERSIGHT_018336
February 8, 2026 at 5:32 AM
epsteinweb.bsky.social
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protect mankind from the totalizing control habits of Soviet-style thinking: First, an unkillable human instinct for individual freedom, the squirm humans have always shown under the boot of too much authority. And, a second protection he thought, would be the really terrible, absurd inefficiency of centrally planned systems. No bureaucrat, no economist, could possibly out-perform the productive chaos of a market or an election in the long run Hayek felt. Finding the right price, matching supply and demand - it was impossible to think this could be done by some technocrat in a room somwhwere. This was what markets were for. This is how they encouraged profit, free-thinking, invention. Chuchill’s famous line, that “Democracy is the worst form of government except for all the others,” contained a certain truth: civilizations, yoked by democratic and market rules, reached more durable outcomes than despots or oligarchs. Hayek, as we look at his judgment now, was correct. People wanted to be free; the dream of central planning collapsed under its own weight with the USSR in 1989. 32 “Is there a greater tragedy”: F.A. Hayek, The Road to Serfdom (Chicago: University of Chicago Press, 1994), 32 HOUSE_OVERSIGHT_018264
February 8, 2026 at 5:11 AM
grape2024.bsky.social
📰 Day 1 -Learning Core AI Concepts by Thinking Like a System

Series: 30 Days of Learning Core AI Concepts Understanding AI isn’t just about models. It’s about the systems, the assumptions, and the… Continue reading on Towards Data...
Day 1 -Learning Core AI Concepts by Thinking Like a System
Series: 30 Days of Learning Core AI Concepts Understanding AI isn’t just about models. It’s about the systems, the assumptions, and the… Continue reading on Towards Data...
medium.com
February 8, 2026 at 5:06 AM
jacky.wtf
Guess what systems helped influence Victorian computing? Which also included post-Depression computing? That then gave us this stupid era of "thinking machines"?

The scope of acceptable thought 😭
February 8, 2026 at 4:16 AM
enkibright.bsky.social
Instead of thinking about external hardware/servers/systems, one should build within the AI chat interface itself; and that through symbolic integration, we are able to recreate the equivalent functionality of servers, LLMs, and "etcetera" (everything)
February 8, 2026 at 2:49 AM
deviousxen.bsky.social
They'd rather allow their Puritanical, half a millennia old machine code dictate their value systems than critical thinking...

They'd rather proudly hurt and be covered in a layer of serf dirt.

They'd rather hurt for their absent Daddy/God, Morty!

Rather hurt each other than let respite in, Mort
February 8, 2026 at 12:26 AM
epsteinweb.bsky.social
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Jeffrey Joerres Chairman and Chief Executive Officer Manpower Inc. USA Josef Joffe Publisher-Editor Die Zeit Germany Bjérn Johansson Chairman Dr Bjorn Johansson Switzerland Associates Inc. Boris Johnson Mayor of London, United Kingdom Chris Johnson Executive Vice-President Nestlé SA Switzerland David Johnson Executive Vice-President and Nalco Europe Netherlands President, Europe, Africa and Middle East Erica L. Johnson Chief Officer, Customer Network Eskom Holdings Limited South Africa Business H. Fisk Johnson Chairman of the Board and Chief S. C. Johnson & Son Inc. USA Executive Officer Jace Johnson Vice President, Government Affairs Adobe Systems USA and Public Policy Kevin Johnson Chief Executive Officer Juniper Networks Inc. USA Mark Johnson Founder Playing for Change USA Ray Johnson Senior Vice-President and Chief Lockheed Martin Corporation USA Technology Officer Robert A. Johnson Executive Director The Institute for New USA Economic Thinking (INET) Simon Johnson Ronald A. Kurtz, Professor of MIT - Sloan School of USA Entrepreneurship Management Tom Johnstone President and Chief Executive Officer AB SKF Sweden Anthony Jones Journalist Australian Broadcasting Australia Corporation - ABC. Television David Jones Global Chief Executive Officer Havas Worldwide USA Hannah Jones Vice-President, Sustainable Business _ Nike Inc. USA and Innovation, Government Public Affairs Agnes Jongerius President Confederation of Dutch Netherlands Trade Unions Conni Jonsson Chief Executive Officer and Managing EQT Partners AB Sweden Partner Vyomesh Joshi Executive Vice-President, Imaging Hewlett-Packard Company USA and Printing Group Tarun Jotwani Global Head, Fixed Income and Chief Nomura International Plc United Kingdom Executive Officer, Europe, Middle East and Africa Luciano E. Joublanc Ambassador of Mexico Embassy of Mexico Switzerland Lisa Jucca Chief Financial Correspondent, Italy Thomson Reuters Italy Bernhard Jucker Member of the Executive Committee ABB Ltd Switzerland and
February 7, 2026 at 11:35 PM
epsteinweb.bsky.social
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statement and seems to have no place in modern scientific thinking. But it’s not as silly as it sounds. What does the human brain do? A brain receives certain input, it computes things, it causes certain actions to happen, it generates a certain output. Like the weather. All sorts of systems are, effectively, doing computations—whether it’s a brain or, say, a cloud responding to its thermal environment. We can argue that our brains are doing vastly more sophisticated computations than those in the atmosphere. But it turns out that there’s a broad equivalence between the kinds of computations that different kinds of systems do. This renders the question of the human condition somewhat poignant, because it seems we’ re not as special as we thought. There are all those different systems of nature that are pretty much equivalent, in terms of their computational capabilities. What makes us different from all those other systems is the particulars of our history, which give us our notions of purpose and goals. That’s a long way of saying that when the box on our desk thinks as well as the human brain does, what it still won’t have, intrinsically, are goals and purposes. Those are defined by our particulars—our particular biology, our particular psychology, our particular cultural history. 182 HOUSE_OVERSIGHT_016985
February 7, 2026 at 11:12 PM
epsteinweb.bsky.social
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Over nearly four decades, Stephen Wolfram has been a pioneer in the development and application of computational thinking and responsible for many innovations in science, technology and business. His 1982 paper “Cellular Automata as Simple Self-Organizing Systems,” written at the age of twenty-three, was the first of numerous significant scientific contributions aimed at understanding the origins of complexity in nature. It was around this time that Stephen briefly came into my life. I had established The Reality Club, an informal gathering of intellectuals who met in New York City to present their work before peers in other disciplines. (Note: In 1996, The Reality Club went online as Edge.org). Our first speaker? Stephen Wolfram, a “wunderkind” who had arrived in Princeton at the Institute for Advanced Study. I distinctly recall his focused manner as he sat down on a couch in my living room and spoke uninterrupted for about an hour before the assembled group. Since that time, Stephen has become intent making the world’s knowledge easily computable and accessible. His program Mathematica is the definitive system for modern technical computing. Wolfram|Alpha computes expert-level answers using AI technology. He considers his Wolfram Language to be the first true computational communication language for humans and AIs. I caught up with him again four years ago, when we arranged to meet in Cambridge, Massachusetts, for a freewheeling conversation about AI. Stephen walked in, said hello, sat down, and, looking at the video camera set up to record the conversation for Edge, began to talk and didn’t stop for two and a half hours. The essay that follows is an edited version of that session, which was a Wolfram master class of sorts and is an appropriate way to end this volume—just as Stephen’s Reality Club talk in the ’80s was a great way to initiate the ongoing intellectual enterprise whose result is the rich community of thinkers presenting their work to
February 7, 2026 at 11:12 PM
epsteinweb.bsky.social
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problems, it often takes only a couple of noisy data points to get something that’s a beautiful description of a phenomenon, because you’ re putting in knowledge about how physics works. That’s in huge contrast to normal AI, which requires millions of training examples and is very sensitive to noise. By adding the appropriate background knowledge, you get much more intelligence. Similar to the physical-systems case, if we make neurons that know a lot about how humans learn from each other, then we can detect human fads and predict human behavior trends in surprisingly accurate and efficient ways. This “social physics” works because human behavior is determined as much by the patterns of our culture as by rational, individual thinking. These patterns can be described mathematically and employed to make accurate predictions. This idea of a credit-assignment function reinforcing connections between neurons that are doing the best work is the core of current AI. If you make those little neurons smarter, the AI gets smarter. So, what would happen if we replaced the neurons with people? People have lots of capabilities. They know lots of things about the world; they can perceive things in a broadly competent, human way. What would happen if you had a network of people in which you could reinforce the connections that were helping and minimize the connections that weren’t? That begins to sound like a society, or a company. We all live in a human social network. We’re reinforced for doing things that seem to help everybody and discouraged from doing things that are not appreciated. Culture is the result of this sort of human AI as applied to human problems; it is the process of building social structures by reinforcing the good connections and penalizing the bad. Once you’ve realized you can take this general AI framework and create a human AI, the question becomes, What’s the right way to do that? Is it a safe idea? Is it completely crazy? My students and I are
February 7, 2026 at 11:01 PM
epsteinweb.bsky.social
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can now go far beyond these statistical understandings. We’re beginning to be able to analyze, predict, and even design the emergent behavior of complex heterogeneous networks. The cybernetics view of the connected individual actor can now be expanded to cover complex systems of connected individuals and machines, and the insights we obtain from this broader view are fundamentally different from those obtained from the cybernetics view. Thinking about the network is analogous to thinking about entire ecosystems. How would you guide ecosystems to grow in a good direction? What do you even mean by “a good direction”? Questions like this are beyond the boundary of traditional cybernetic thinking. Perhaps the most stunning realization is that humans are already beginning to use AI and machine learning to guide entire ecosystems, including ecosystems of people, thus creating human-AI ecologies. Now that everything is becoming “datafied,” we can measure most aspects of human life and, increasingly, aspects of all life. This, together with new, powerful machine-learning techniques, means that we can build models of these ecologies in ways we couldn’t before. Well-known examples are weather- and traffic-prediction models, which are being extended to predict the global climate and plan city growth and renewal. Al-aided engineering of the ecologies is already here. 135 HOUSE_OVERSIGHT_016938
February 7, 2026 at 11:01 PM
epsteinweb.bsky.social
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THE HUMAN STRATEGY Alex “Sandy” Pentland Alex “Sandy” Pentland is Toshiba Professor and professor of media arts and sciences, MIT; director of the Human Dynamics and Connection Science labs and the Media Lab Entrepreneurship Program, and the author of Social Physics. In the last half-century, the idea of AI and intelligent robots has dominated thinking about the relationship between humans and computers. In part, this is because it’s easy to tell the stories about AI and robots, and in part because of early successes (e.g., theorem provers that reproduced most of Whitehead and Russell’s Principia Mathematica) and massive military funding. The earlier and broader vision of cybernetics, which considered the artificial as part of larger systems of feedback and mutual influence, faded from public awareness. However, in the intervening years the cybernetics vision has slowly grown and quietly taken over—to the point where it is “in the air.” State-of-the-art research in most engineering disciplines is now framed as feedback systems that are dynamic and driven by energy flows. Even AI is being recast as human/machine “advisor” systems, and the military is beginning large-scale funding in this area—something that should perhaps worry us more than drones and independent humanoid robots. But as science and engineering have adopted a more cybernetics-like stance, it has become clear that even the vision of cybernetics is far too small. It was originally centered on the embeddedness of the individual actor but not on the emergent properties of a network of actors. This is unsurprising, because the mathematics of networks did not exist until recently, so a quantitative science of how networks behave was impossible. We now know that study of the individual does not produce understanding of the system except in certain simple cases. Recent progress in this area was foreshadowed by understanding that “chaos,” and later “complexity,” were the typical behavior of systems, but we
February 7, 2026 at 11:01 PM
epsteinweb.bsky.social
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for interpreting images and text have resulted from developing good models for vision and language. Developing good models of people is the next frontier. Of course, understanding how human minds work isn’t just a way to make computers better at interacting with people. The trade-off between making mistakes and thinking too much that characterizes human cognition is a trade-off faced by any real- world intelligent agent. Human beings are an amazing example of systems that act intelligently despite significant computational constraints. We’re quite good at developing strategies that allow us to solve problems pretty well without working too hard. Understanding how we do this will be a step toward making computers work smarter, not harder. 96 HOUSE_OVERSIGHT_016899
February 7, 2026 at 10:52 PM
epsteinweb.bsky.social
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of our age.” Up to now, AI research has focused on systems that are better at making decisions, but this is not the same as making better decisions. No matter how excellently an algorithm maximizes, and no matter how accurate its model of the world, a machine’s decisions may be ineffably stupid in the eyes of an ordinary human if its utility function is not well aligned with human values. This problem requires a change in the definition of AI itself—tfrom a field concerned with pure intelligence, independent of the objective, to a field concerned with systems that are provably beneficial for humans. Taking the problem seriously seems likely to yield new ways of thinking about AL, its purpose, and our relationship to it. 35 HOUSE_OVERSIGHT_016838
February 7, 2026 at 10:37 PM
epsteinweb.bsky.social
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Steven Pinker: Tech Prophecy and the Underappreciated Causal Power of Ideas There is no law of complex systems that says that intelligent agents must turn into ruthless megalomaniacs. David Deutsch: Beyond Reward and Punishment Misconceptions about human thinking and human origins are causing corresponding misconceptions about AGI and how it might be created. Tom Griffiths: The Artificial Use of Human Beings Automated intelligent systems that will make good inferences about what people want must have good generative models for human behavior. Anca Dragan: Putting the Human into the AI Equation In the real world, an AI must interact with people and reason about them. People will have to formally enter the AI problem definition somewhere. Chris Anderson: Gradient Descent Just because AI systems sometimes end up in local minima, don’t conclude that this makes them any less like life. Humans—indeed, probably all life-forms—are often stuck in local minima. David Kaiser: “Information” for Wiener, for Shannon, and for Us Many of the central arguments in The Human Use of Human Beings seem closer to the 19th century than the 21st. Wiener seems not to have fully embraced Shannon’s notion of information as consisting of irreducible, meaning-free bits. Neil Gershenfeld: Scaling Although machine making and machine thinking might appear to be unrelated trends, they lie in each other’s futures. W. Daniel Hillis: The First Machine Intelligences Hybrid superintelligences such as nation states and corporations have their own emergent goals and their actions are not always aligned to the interests of the people who created them. Venki Ramakrishnan: Will Computers Become Our Overlords? Our fears about AI reflect the belief that our intelligence is what makes us special. Alex “Sandy” Pentland: The Human Strategy How can we make a good human-artificial ecosystem, something that’s not a machine society but a cyberculture in which we can all live as humans—a culture with a human feel to
February 7, 2026 at 10:30 PM
alextoth.bsky.social
Systems Engineering is about THINKING NOT tool mastery.

Tell me I’m wrong

use.toth.al/posts/the-p...

𝗗𝗼𝗻'𝘁 𝘂𝗻𝗱𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗼𝗳 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲.
It can make all the difference in your career.
Your Tool doesn't matter
use.toth.al
February 7, 2026 at 10:28 PM
thedogknows.bsky.social
So…y’all remember last year when Elon and BigBalls took all the back-end access to banking data? And anything else that goes through government systems? What are we thinking they’re going to do with that?
therealjackhopkins.bsky.social Jack Hopkins-The Original @therealjackhopkins.bsky.social · 9h
This story is chilling...not because it’s loud, but because it’s quiet.
www.jackhopkinsnow.com
February 7, 2026 at 10:17 PM