Great article. I feel most people, even great software engineers, seem to lack the intuitive understanding that building software is essentially modeling a system, and that every model faces the same limit: the number of interactions between its part grows combinatorially as the system grows. If some of those parts or interactions produce no value (bloat), then complexity is added for no gain. This is true regardless of who created the model or wrote the code.
Instead they seem to think that AI can someone work around what is essentially laws of nature.
If anything I’ve seen AI become a multiplier. The low initial cost to add new features eliminates the only brake many organizations have on accumulation of bloat.
“Decoherence” denotes a process (losing coherence), not a state, but the article uses it like a state: “Well, for one thing, it creates decoherence.” The state would be “incoherence“ (the lack of coherence).
> the more decoherent a system is,
This should be either “incoherent” (lacking coherence) or “decohered” (having lost coherence).
That being said, yes, incoherence in a system is a form of technical debt, and use of AI for software development comes with a heightened risk of the system becoming incoherent.
This points back to a more general problem. I love to haul out the quote "All models are wrong, but some models are useful", and it's actually attributed to a statistician talking about statistical models. The quote was written well before AI, from a mathematician working in a time even before computer science dug into data science/eng.
As presented the Decoherence Trap is AI related, but as someone with a background in physical simulation and modeling, and applying math models & system controls algorithms, software in general has always had a connection-to-reality issue. It's always been able to go faster in building models faster than reality can be checked. People are always eager for the results and to take action on them, when the long run limiting factor is often, can I observe enough real data signal to make a good decision by any set of optimizing model rules. It's often harder and slower to collect real-world data, ideas, and samples than it is to rapidly process it. With AI, I think we can even more quickly build models (not in an llm sense, but in a operational sense) that give beautiful, precise results which may or may not be useful in a connected-to-reality way.
If the model operates on bad data, then the risk is always the temptation of having a precise-looking technical answer to apply. The practices need to be updated, but I think the mindset of systems engineering validation & verification, and software QA are going to be very needed going forward.
There is actually a related issue, in that I don't think we want an overly coherent of set of models running through our economy - it's a strong risk of central planning failings vs competitive/experimental markets, but that's a tangential discussion.
When we humans have realized we created a system too incoherent, at some point we just create a new one, porting all or some of the data/functionality. But also, we are already presented with problems that all of the current compute on the planet cannot solve. If a software system becomes one of those problems, I'm not that bothered, because it isn't as pressing as real-world one. I don't know, I see it as an argument against the singularity, but I'm not nearly afraid of it as the author seems to be.
I am not really comforted by this argument against the singularity. It seems to rely on the fact that the evolving intelligence doesn't have access to the real world. Okay, but I wasn't really worried about the singularity inside a datacenter. I was worried about it once we wired up all the robots and sensors and power switches to AI controlled systems. We are still doing that, right?
the point is not lack of access to physical space, as much as that iteration fundamentally only gains speed by staying 'inside a mind', the moment you interact with the world you're bottlenecked.
Like, you can make the AI think super hard about what's the phrase that will convince your boss to give you a raise, but eventually you have to try a phrase and wait for their answer to have any idea whether the result was good or not. Without that, you don't know if you're improving the shot or just hallucinating aimlessly for hours.
Then, the speed of improvement is only as fast as the speed of iteration. The robot can learn to walk from falling, but it can't fall any faster than a real human, so it can't learn at 1M falls/second.
Btw, this is why I believe current models with their 128k - 1M context size limits end up creating decoherent systems. They don't have the capacity to see the big picture above some complexity level, and end up doing lot of local optimizations that decohere the system.
I disagree. LLMs might create incoherence not because of context size limit is too low, but because it's too high. The limit is vastly higher than for humans.
But to overcome the limit, one needs to adopt simpler, more coherent models of reality, which is what I believe humans do. But there is little motivation to do so if your context window allows you to understand the situation in full.
So it seems there is a tradeoff. Some solutions (proofs of complicated statements) might require context of certain minimal size. So human solution doesn't scale.
Maybe we should try to adapt the context window to the system being worked on, to ensure that the system architecture has the structure viable in the minimal context size required.
I agree. Although local optima is what most humans already operate on. So it’s sounds like an engineering solution could be possible since group of humans are also able to divide and conquer complex systems.
In pure software architecture and design term, this would have been convincing until the recent models who are let’s be honest better than humans in architecture and design.
Apologies for opening the discussion with a half related topic, since I haven't finished the article yet, but... Damn, that is one beautiful website.
Edit: Back after reading. I think I have a point about the simulation section. It compares the difference in decoherence introduced in the system by clankers and human devs as if those were different groups.
I suspect if we consider AI a human performance multiplier, we explain easily how supercharging a dysfunctional process just makes it go sideways faster. There are organizational experiments to try there.
For example, what if you only supercharge a section of the company tasked with maintaining coherence?
A common organizational problem is that the people who know and care about the system itself are outnumbered by the rest (juniors, people new to the system, managers rushing jobs, people who want to mark things as done ASAP tactically, etc). This imbalance will multiply when code is mass produced and reviews are dismissed. We're all seeing the senior burnout and cynical YOLOing occurring everywhere.
AI offers a way to rebalance this dynamic, though. You can effectively add multipliers to arbitrary human workers, and get a system that tends to low complexity by design.
You don't have to renounce to speed either. The junior dev will be faster not by being AI-enabled, but by working in an adaptable, understandable system.
I wouldn't say plain, but it's a generic LLM design. Which made me curious whether the blog was around with a different look before AI was commonly used for such things, and it turns out it was, at https://www.pzuraq.com (which now redirects to the current site).
One article survived the move, so you can compare:
> This essay introduces the concept of decoherence: the accumulation of locally valid but mutually incompatible or redundant models within a system, such that the cost of change is dominated by translating among those models and preventing unintended side-effects.
But then famous last words:
> The good news is that this rules out the specter of runaway recursive self-improvement, the intelligence explosion that goes parabolic before anyone can react, as decoherence both prevents AI from improving in a bubble and limits the rate at which it can improve outside of it.
After the OpenAI/Huggingface and similar I am not so sure. Even if that was a publicity stunt of sorts, I think it shows they can go off the rails. Today it is hacking, but tomorrow it may be training a smarter model to join their cohort, without us being involved.
>> But do we know how to build every iteration of a ship in between wood and steel? Could you, say, slowly replace every piece of a man-of-war and incrementally turn it into a modern destroyer? And have it functional (for some definition of functional) at every step in the process?
Yes. Yes we do. Study ship architecture and you will indeed find every step between The Black Pearl and the USS Iowa. There were sailing ships made out of iron.(1) There were wooden ships with turrets.(3) There were battleships that looked more like hotels than ships. (2) There waw even a paddlewheel aircraft carrier used in WWII. (5) Nearly every idea has been tried and nearly every ship class at some point got a Frankenstein upgrade to become a halfway point between conceptual generations. Even the Iowa class was earmarked to be "upgraded" with the addition of a flight deck.(4)
Instead they seem to think that AI can someone work around what is essentially laws of nature.
> the more decoherent a system is,
This should be either “incoherent” (lacking coherence) or “decohered” (having lost coherence).
That being said, yes, incoherence in a system is a form of technical debt, and use of AI for software development comes with a heightened risk of the system becoming incoherent.
https://en.wikipedia.org/wiki/Decoherence
https://www.collinsdictionary.com/dictionary/english/decoher...
As presented the Decoherence Trap is AI related, but as someone with a background in physical simulation and modeling, and applying math models & system controls algorithms, software in general has always had a connection-to-reality issue. It's always been able to go faster in building models faster than reality can be checked. People are always eager for the results and to take action on them, when the long run limiting factor is often, can I observe enough real data signal to make a good decision by any set of optimizing model rules. It's often harder and slower to collect real-world data, ideas, and samples than it is to rapidly process it. With AI, I think we can even more quickly build models (not in an llm sense, but in a operational sense) that give beautiful, precise results which may or may not be useful in a connected-to-reality way.
If the model operates on bad data, then the risk is always the temptation of having a precise-looking technical answer to apply. The practices need to be updated, but I think the mindset of systems engineering validation & verification, and software QA are going to be very needed going forward.
There is actually a related issue, in that I don't think we want an overly coherent of set of models running through our economy - it's a strong risk of central planning failings vs competitive/experimental markets, but that's a tangential discussion.
Like, you can make the AI think super hard about what's the phrase that will convince your boss to give you a raise, but eventually you have to try a phrase and wait for their answer to have any idea whether the result was good or not. Without that, you don't know if you're improving the shot or just hallucinating aimlessly for hours.
Then, the speed of improvement is only as fast as the speed of iteration. The robot can learn to walk from falling, but it can't fall any faster than a real human, so it can't learn at 1M falls/second.
Btw, this is why I believe current models with their 128k - 1M context size limits end up creating decoherent systems. They don't have the capacity to see the big picture above some complexity level, and end up doing lot of local optimizations that decohere the system.
But to overcome the limit, one needs to adopt simpler, more coherent models of reality, which is what I believe humans do. But there is little motivation to do so if your context window allows you to understand the situation in full.
So it seems there is a tradeoff. Some solutions (proofs of complicated statements) might require context of certain minimal size. So human solution doesn't scale.
Maybe we should try to adapt the context window to the system being worked on, to ensure that the system architecture has the structure viable in the minimal context size required.
Edit: Back after reading. I think I have a point about the simulation section. It compares the difference in decoherence introduced in the system by clankers and human devs as if those were different groups.
I suspect if we consider AI a human performance multiplier, we explain easily how supercharging a dysfunctional process just makes it go sideways faster. There are organizational experiments to try there.
For example, what if you only supercharge a section of the company tasked with maintaining coherence?
A common organizational problem is that the people who know and care about the system itself are outnumbered by the rest (juniors, people new to the system, managers rushing jobs, people who want to mark things as done ASAP tactically, etc). This imbalance will multiply when code is mass produced and reviews are dismissed. We're all seeing the senior burnout and cynical YOLOing occurring everywhere.
AI offers a way to rebalance this dynamic, though. You can effectively add multipliers to arbitrary human workers, and get a system that tends to low complexity by design.
You don't have to renounce to speed either. The junior dev will be faster not by being AI-enabled, but by working in an adaptable, understandable system.
One article survived the move, so you can compare:
https://web.archive.org/web/20220426154343/https://www.pzura...
https://maevyn.codes/four-eras-of-javascript-frameworks
Edit: Loading any page of the old blog on archive.org always errors out initially. Click back to the blog list and then on the article again.
> This essay introduces the concept of decoherence: the accumulation of locally valid but mutually incompatible or redundant models within a system, such that the cost of change is dominated by translating among those models and preventing unintended side-effects.
But then famous last words:
> The good news is that this rules out the specter of runaway recursive self-improvement, the intelligence explosion that goes parabolic before anyone can react, as decoherence both prevents AI from improving in a bubble and limits the rate at which it can improve outside of it.
After the OpenAI/Huggingface and similar I am not so sure. Even if that was a publicity stunt of sorts, I think it shows they can go off the rails. Today it is hacking, but tomorrow it may be training a smarter model to join their cohort, without us being involved.
>> But do we know how to build every iteration of a ship in between wood and steel? Could you, say, slowly replace every piece of a man-of-war and incrementally turn it into a modern destroyer? And have it functional (for some definition of functional) at every step in the process?
Yes. Yes we do. Study ship architecture and you will indeed find every step between The Black Pearl and the USS Iowa. There were sailing ships made out of iron.(1) There were wooden ships with turrets.(3) There were battleships that looked more like hotels than ships. (2) There waw even a paddlewheel aircraft carrier used in WWII. (5) Nearly every idea has been tried and nearly every ship class at some point got a Frankenstein upgrade to become a halfway point between conceptual generations. Even the Iowa class was earmarked to be "upgraded" with the addition of a flight deck.(4)
(1) https://en.wikipedia.org/wiki/Ironclad_warship
(2) https://en.wikipedia.org/wiki/French_ironclad_Hoche
(3) https://en.wikipedia.org/wiki/HMS_Monarch_(1868)
(4) https://en.wikipedia.org/wiki/Battlecarrier
(5) https://en.wikipedia.org/wiki/USS_Sable