Hey, I have read or rather researched a bit on this paper already. It is definitely a very interesting take on the so-called basic CS algorithms. I am personally not fully sold on calling these "hidden agencies", as these algorithms are still based on set rules and in a way deterministic to get the output, and not really "thinking". It does get me thinking now on how some of these basic principles could be applied or reformed with AI, but a very solid step in connecting CS with Biology.
Building AI tools has shown me how simple rules, applied over and over, can create things that look surprisingly complex and intentional. It makes me think intelligence isn't about a fancy algorithm, but about how things adapt - pixels, in our case - when given consistent, basic guidance. Our Video-to-Video model sometimes comes up with edits I wouldn't have thought of, just from learning simple transformations. It's a good reminder that basic, repeating steps are still a great way to solve creative problems.
This study's value isn't in computer science; it's in strategy. It exposes how 'hollow' our popular definition of 'intelligence' has become - we've been 'performing' a version of agency that we believe requires a centralized, top-down brain. Levin's work is the 'proof over polish' : it shows that problem-solving and emergent 'competencies' are a bottom-up property of the system itself, not a ghost in the machine. For leaders, the takeaway is stark: stop trying to build a singular 'brain' for your AI, your brand, or your company, and start learning to resonate with the 'basal intelligence' that's already distributed everywhere.
These classical sorting algorithms indeed provide a compelling illustration of an intelligent system in which small agents-cells can autonomously find solutions and thereby bring order to the overall system. However, the process remains strictly deterministic, and any modification in the underlying logic would require rewriting the code itself. Although the algorithms demonstrate a high level of performance (comparable to or even exceeding that of traditional implementations), the paper notes that the cell-sort approach relies on a multithreaded system. This implies that efficiency scales primarily when the number of CPU cores is comparable to the number of elements (cells) being sorted. Consequently, this does not confer a clear advantage over neural-style artificial intelligence systems, which also rely heavily on parallel computation but can adapt their behavior more flexibly without code-level modification. Nonetheless, the practical use of such algorithms may be justified in systems with a large number of otherwise idle cores, where traditional sorting algorithms cannot be efficiently adapted to a multithreaded model. In such contexts, cell-based sorting could potentially offer significant performance improvements. Comment by Kashintsev Georgii, with multiple publications on data structures and extensive experience implementing various algorithms, including classical sorting algorithms.
The paper is very interesting as it provokes the way we consider intelligence on the very core level. The question which the researchers are fundamentally posing then is thus: what happens in the event that even the sorting is not merely a mechanical implementation, but rather some more independent activity? When years of my experience have taught me to write about these algorithms and teach them to thousands of students, I have always identified them with deterministic, stepwise processes. You write the code, it runs, that is all. However, that mind-set is inverted in this piece of research because the elements of the array are considered to be agents with local rules and they observe patterns form that were not programmed. What comes to my mind is the analogy of biological systems. The authors compare it to cellular organization and morphogenesis where simple cellular rules are involved in creating complexity at the micro level. In my experience with algorithmic problem solving I have found that students would find it difficult due to the tendency to manage everything on the top-down aspect of the problem-solving. But distributed systems have demonstrated that coordination will exist without control. The offensive assertion, in this case, is the basal intelligence in sorting algorithms. That's bold language. It is a matter of definitions whether you want to call what is going on the intelligence or simply emergent behavior. Nonetheless, it does provoke us to re-evaluate in what point agency starts and what exactly I mean by stating when systems organize themselves.
The idea that even basic algorithms can exhibit "intelligent" behavior is interesting for business. In link building, we see something similar—simple automation systems gradually "learn" to find effective patterns in a data set. This is not true intelligence, but it is similar to the evolution of efficiency through adaptation. If even simple sorting algorithms show signs of adaptation, it raises an interesting question—where does the "mechanical" end and the "intelligent" begin? For me, as an entrepreneur working with algorithms in digital marketing, this is a reminder: don't underestimate the power of simple systems. They can create complexity, even without the developer's intention.
Hello, My name is Zeel Jadia, and I'm the CEO of ReachifyAI. Before stepping into this role, I served as an executive across multiple companies, typically as CTO, where I built and scaled software platforms in industries ranging from logistics to event management. At ReachifyAI, I bring that experience to the restaurant technology space, where we deliver AI-powered phone and automation solutions that help restaurants run more smoothly. Today, ReachifyAI supports restaurant locations nationwide and handles more than 6 million calls every year. As a press note: I have experience, as ReachifyAI has been mentioned in restaurantbusinessonline.com, techround.co.uk, and more. Regarding the referenced research, it is indeed very interesting. It demonstrates decentralized coordination solving a task as opposed to a top-down algorithmic approach. Each element only interacts with its neighbors according to simple local rules, and these local interactions collectively produce the global sorting behavior. One of the aspects I find most interesting is the inherent robustness, and how elements can operate around "frozen" elements to still complete the sort. In a traditional top-down algorithmic approach, corruption or failure in a single component can derail the entire process, whereas here the distributed model can continue functioning. From what I understand, the elements are not provided with rules to handle a damaged element scenario, yet with the basic rules available they are still able to work around this more complex scenario. Keep in mind that more complex, real-world scenarios would require significantly more complicated local rules at the element level. Sorting algorithms are Intro to Software Engineering level algorithms, but that does not mean that this is not extremely interesting work in decentralized systems.
This paper on sorting algorithms hit home, especially working in AI health tech. We see the same effect. In our risk detection work, stacking a few basic biomarker signals reveals trends long before they're obvious. Makes me think intelligence is less about complex design and more about letting simple parts interact.
As someone who has studied AI and human biology and biochemistry extensively, my initial response is a little shock and awe at NO central CONTROL, and yet there seems to be PURPOSE: spooky. Makes for great hot tub philosophy sessions about the emergency of AI agents from a base soup of code. It is though a nice conceptual bridge from the world of computer code to biological morphogenesis, whereas it shows how minimal conditions can prompt adaptation, but should more actively avoid implying agency or any kind of intentionality. And, recasting simple sorting algorithms might help probe basal intelligence, but there needs to be stronger controls and statistical repeatability to rule out dynamical explanations. A good analogy here is Conway's famous Game of Life, where gliders "move" across the grid -- this complex behavior arising from a set of simple rules generate this dynamic as output, producing the illusion that the gliders want to preserve themselves, just as we do when the larger tribe splits up in time of famine searching for nutritional cohesiveness.