Digifesto

Tag: technology

September 10, 2026

the potential of AI to cause human extinction (or similarly bad outcomes)

AI X-risk is in the news cycle again. This time, it comes when AI companies have imminent IPOs and when their products are taking off. So while concerns about AI X-risk have been around for quite a while, and were part of the motivating ideologies of many of the AI lab founders, we are being told (again) that this problem is especially urgent, now.

The discourse on this topic is frustrating. I am going to write a few counterpoints here, mainly for catharsis.

First, what is the risk? Risk is the probability of loss. One way to think, systematically, about risk is that it is the probability of a Hazard times the value of something that is put at risk (the Exposure), times the Vulnerability of the Exposure to the Hazard.


R=H×E×VR = H \times E \times V


The Exposure here seems to be, maximally, all human lives after some prematurely arriving point. That point in time t is, notionally, in the next decade, and the value of human life lost is all that would happen between t and the burn-out of the sun. That’s a lot of exposure!

It is not the only exposure that matters though, and what many, many people have said before is that the far more likely exposures are much more mundane. There’s something grimly funny about Samuel Marks’s phrase “human extinction (or similarly bad outcomes)” because… well… what outcomes are similarly bad to human extinction? What are we even talking about here?

Then, what is the Hazard, exactly? “AI”. As many, many people have said before, “AI” as a term of art today is a weird signifier doing a lot of different kinds of political work, but it is not precise. And we should care about precision if we are talking about potential human extinction. Cal Newport has a nice post correctly pointing to the main issue being a specific kind of technology, “long-horizon, dangerously equipped unsupervised LLM-powered agents“. I’ll call this a LHDEULLMPA for short. But this still doesn’t, I think, totally cut it. How long a horizon? How dangerously equipped? We’ll return to this.

What about Vulnerability? What is the threat vector? One narrative is “vibe-coded supervirus“. The idea is that AI systems lower the cost of building superviruses, which then kill all of humanity. A subtle point here is that the Hazard we are looking at now is superviruses, not “AI”. The idea is that “AI” indirectly causes human extinction by reducing the cost of/increasing the probability of supervirus creation.

Are there other threat vectors for human extinction? Maybe, rogue AI swarm setting off nuclear weapons in order to destroy humanity? Again, here, the direct hazard is the nukes, not the AI. We could go on.

I venture that it’s weird, then, that these calls about AI and human extinction are creating consternation about “AI” rather than consternation about, say, our under-investment in supervirus epidemiology or nuclear disarmament. I say this as somebody who is all for AI regulation. I perhaps benefit from fear about AI X-risk. But I do wonder what is going on here; there’s something irrational about it. These X-risk arguments all depend on what would be results in entirely different fields of study that are not “AI”. There are independent scientific facts about the feasibility and danger of superviruses, or the feasibility and danger of nuclear war, which are not, as far as I know, verifiably established by “AI” or AI safety workers.

Let’s suppose the facts were in favor of X-risk and superviruses could be engineered by a bad actor given sufficient knowledge. Why is “AI” the thing that would, or would not, enable humans to, eventually, design this virus? Hey, I expect that any engineering of computer viruses will use a lot of computation. But is a LHDEULLMPA even the best way to go about developing a supervirus?

One premise is that no matter what intellectual work is needed to do something, an LHDEULLMPA will do it, and more cheaply than it would otherwise have been done. So, with LHDEULLMPA, somebody might invent a supervirus at time t. But we’ve already supposed that supervirus invention is feasible. So, without LHDEULLMPA, maybe superviruses are invented, instead, at time t+1, for some value of 1.

Which means that if we’re serious about X-risk from superviruses, we should be urgently serious about global public health institutions and liability regimes that disincentivize the creation of superviruses more generally. Those rules and institutions should include the actions of LHDEULLMPA, but should not be exclusive to them!

To the extent that there is effective international law (which there isn’t), this is what we do with all sorts of dangerous behaviors. One of the things that makes law ineffective is lack of enforcement, which is certainly an issue because of the lack of international cooperation around anything important. We are slow-walking into World War 3, after all. World War 3 may well be a Hazard with existential implications for the world, but is “AI” the cause of World War 3? I do not think that LHDEULLMPA are the causes of World War 3. The wars are more attributable to stupidity than intelligence, artificial or not.

Fundamentally, the fears of AI X-risk are not, I think, due to specific threats which have little to do with AI per se. Rather, it has to do with the fear that AI will accomplish something (such as supervirus design) that we would otherwise fundamentally not be able to do, or, otherwise, something else I haven’t mentioned yet.

Geoffrey Hinton was on BBC this morning warning about AI X-risk saying, “If you want to know what not being the apex intelligence is like, go ask a chicken.” He was making the case that an AI system would not be used as a tool by humans to destroy other humans, but rather that it would, out of its own survival instinct, dominate humanity. This is where the “similarly bad outcomes” really start to come out of the woodwork. It’s a different Exposure.

So, the question is, to what extent are LHDEULLMPA (as Hazard) going to dominate humanity (the Exposure)? The question is, again, what is the Vulnerablity?

Once again, this seems like a question for a kind of scientist that is not an LLM benchmarker. Humanity is quite volatile, diverse, and resilient. We might look to the history of inter-human domination, including domination using technical means, to see how successful this has been in the past. If we were serious about this kind of risk, we should be investing more in the social and complexity sciences that would test and develop strategies for resilience to this problem. That would necessarily involve looking at, for example, supply chains of energy and components for LHDEULLMPA and their vulnerability to sabotage by humans. It would also require a hard look at the actual costs of operating an LHDEULLMPA and who would be paying those costs as one of them goes about its domineering way.

For the record, I do care about this kind of science, it’s one of the reasons I’ve been working on scikit-agent. Scikit-agent is a toolkit for studying large, complex, multi-agent systems, some of which might by “AI agents”, but others might be nation states, or individuals, or firms, or whatever. The idea is to develop an expressive modeling framework for this kind of problem. The models will support analytic evaluation, as well as simulation-based analysis. There will be algorithms for fitting models to available data. My goal is for this to be a good tool for institution design, including international AI governance design. But I digress.

The deeper frustration here is that while LHDEULLMPA may be the “AI” du jour, LHDEULLMPA are perhaps only one way to spend billions of dollars in unaccountable, unsupervised computing for potentially nefarious ends. I spoke about the problems of law enforcement — how even if we have good laws, under-enforcement makes them ineffective. Just as an example, the European Union, which is very good at passing laws about technology and data, is notoriously uneven in their enforcement of these laws, because the companies hop into the most friendly enforcement jurisdiction, which is Ireland. In both the EU and the US, enforcement of, say, privacy law comes down to very slow and sporadically applied ex post fines which become a “cost of doing business” for the companies that are held liable. That’s not an effective regime.

“AI” regulation has a lot in common with privacy regulation. “AI” regulation could, if we wanted it to be, about corralling the otherwise unaccountable information flows that are sometimes harmful. This is currently an unsolved problem. Or, “AI” regulation could be about guaranteeing that the LHDEULLMPA are aligned with — loyal to — their principals (who are most often not genocidal bioterrorists). There is a legal framework for incentivizing agents to be loyal — fiduciary duties. But interestingly, among those that have written about fiduciary AI, there’s a sizable contingent that are pessimistic about using them as real legal requirements with teeth, in part because of pessimism about the technology industry allowing those regulations to take hold. This is despite the fact that some sectors already have these duties in place, and companies operating in those spaces are working hard for their agents to be compliant!

Which gets us, once again, to the frustrating impasse. The AI X-riskers are telling us that there’s a 10% chance of human extinction because of advanced AI and “want to slow down”. There are policy tools available that would bind the actors that are building and furnishing the LHDEULLMPA that are available on the market. But those same actors are doing their best to crush substantive regulation which would slow down development. Once again, the crux of the problem is not LLM benchmarking, but political economy. We do not (yet) have benchmarks for how well LHDEULLMPA perform on political economy. If we did, we might have a much better sense of how much of a hazard they pose to human domination. On the other hand, it may well be that the political economy of AI regulation is after all partly observable, in so far as the contributions to super PACs that oppose it are public. And it may not, at the end of the day, be a particularly hard computational problem to understand. The roots seem to have already been outlined in social theory written in the 1940s. And yet, for some reason, we keep trying to make this about the LHDEULLMPA.

1 Comment
July 22, 2026

The Open Silicon Fallacy: How Panic Over Chinese Open-Weight Models Is Distorting American AI Policy

The following article was written with Gemini Flash 3.6 “in the style of The Economist’s Bagehot”. It is an experiment in AI writing. The arguments and structure are mine. – S

I. The Dragons in the Weights

In the summer of 2026, the fashionable anxiety in Washington and Silicon Valley is that Western technological supremacy has been undermined by a collection of downloadable matrix files. When Chinese research labs like DeepSeek, Moonshot AI, and Z.ai released the weights for their latest systems, the initial response from American technology executives was a nervous cough about benchmark scores. When developers discovered these open-weight models could execute multi-step reasoning at a fraction of the cost of renting access to proprietary American cloud endpoints, the nervous cough turned into a geopolitical emergency.

The narrative is simple, compelling, and decidedly panicked. Chinese open-weight architectures are closing the benchmark gap with closed Western labs. Engineering teams weary of driving a metaphorical Ferrari to Whole Foods for basic data transformations are migrating toward open models hosted locally or behind private firewalls.

To visit Capitol Hill today is to encounter three distinct pillars of alarm regarding these releases. First comes the fear of lost technological primacy, as the arrival of competitive Chinese models shatters the illusion that GPU export restrictions would maintain a multi-generational lead. Second comes the specter of standards hegemony, with strategists dreading a world where global software standardizes on Chinese-developed open architectures. Third, and most loudly invoked, comes the proliferation of dual-use capabilities, as downloadable intelligence primitives escape centralized censorship and safety filters.

Yet what is being distributed from Beijing is not open source in the classic sense of transparent codebases and reproducible pipelines. These are open weights—the pre-computed mathematical parameters of deep neural networks whose underlying data and alignment routines remain closely guarded secrets. That they are nevertheless transforming global software architecture says far more about the economics of general computing than about the ideological triumph of open-source idealism.

II. Bootleggers, Baptists, and Moats

It is a sound rule of political economy that whenever a commercial sector and a national security agency begin using identical language to describe a threat, one should look closely at who stands to profit from the proposed remedy.

What Washington is currently witnessing is a classic demonstration of the bootleggers and baptists dynamic. The baptists are the national security hawks, genuinely concerned with technological primacy and cyber-resilience. The bootleggers are the dominant proprietary API vendors, whose staggering valuations depend entirely on convincing the market that intelligence can only be safely consumed as a paid subscription utility.

When proprietary labs lobby for mandatory model licensing, pre-deployment government safety audits, or outright bans on foreign open-weight distributions, they do so under the pious banner of national defense. Yet the operational effect of such proposals is unmistakable, erecting regulatory moats that entrench an oligopolistic duopoly. By framing open-weight distribution as an inherent national security threat, incumbents seek to achieve through regulatory capture what they struggle to maintain through market competition: the enclosure of the AI stack. The public interest is conflated with the profit margins of cloud providers, while the broader software ecosystem is instructed to accept API lock-in as a patriotic duty.

III. The Halloween Documents Revisited

History does not repeat itself, but software executives certainly recycle their memo templates. In the late 1990s, when Microsoft felt its desktop monopoly threatened by Linux, its executives authored internal strategic assessments—the famous Halloween Documents—warning that open software presented a systemic threat to software stability, intellectual property, and commercial viability.

The current rhetoric against open-weight AI models reproduces this playbook line for line. Once again, open distribution is framed as an irresponsible hazard; once again, security through obscurity is held up as the only responsible posture.

Yet incumbent resistance follows a predictable lifecycle, beginning with initial ridicule, progressing to intense alarmism, moving to lobbying for legal restriction, and ultimately settling into a quiet, pragmatic pivot to co-optation. Two decades after penning memos declaring open software an existential cancer, Microsoft spent 7.5 billion dollars to acquire GitHub, transforming itself into the world’s largest host of open-source code. The very proprietary companies that once swore open software was a menace today run their cloud empires on open infrastructure.

IV. How the Defense State Learned to Love the Kernel

The irony of the current policy panic is that the national security establishment has already solved this problem once before. In the early days of networked computing, defense agencies viewed open-source software with profound suspicion, assuming closed, proprietary systems were superior because their source code was hidden behind non-disclosure agreements and commercial firewalls.

By the early 2000s, however, military and intelligence strategists realized that proprietary vendors could not patch vulnerabilities or adapt to new threats as rapidly as a global community of developers inspecting open code. The intelligence community embraced the doctrine of security through visibility. In 2000, the National Security Agency took an open-source Linux kernel, added mandatory access controls directly into its architecture, and handed Security-Enhanced Linux back to the public.

National security interests accommodated open source not by suppressing it, but by co-opting, hardening, and building on top of it. They recognized that controlling an open, auditable standard offered greater agility and defense-in-depth than relying on a commercial black box.

V. Compilers, Not Missiles

The current attempt to govern AI safety by restricting model weights rests on a fundamental misapprehension of what a large language model actually is. Policy makers consistently treat probabilistic language models as if they were self-contained, autonomous products or guided weapons systems that can be aligned at the factory and locked in a box. In reality, a foundation model is a general-purpose computing primitive, serving as the statistical equivalent of a C compiler or an arithmetic logic unit for natural language and code.

Trying to enforce safety at the weight level is as ineffective as trying to secure an operating system by banning specific sequences of assembly language instructions. Raw inference is inherently difficult to control at the parameter level; a model that can write a Python script for a database query can, with minimal prompting, write a script to probe a network port.

Opponents of open weights often argue that publicly downloadable parameters allow offline execution, rendering traditional hardware tracking obsolete. This argument, however, confuses hardware tracking with runtime deployment governance. The true execution boundary is not the local matrix multiplication happening on a graphics card, but the point where an AI system interacts with the real world through API keys, database access, tool-use privileges, and execution environments. Smart policy does not attempt to police raw matrix math in memory; it enforces strict sandboxing, zero-trust permissions, and identity verification at the application layer where actions occur.

VI. Critiquing “Openness”

To defend the availability of open weights is not to be naive about their limitations. Indeed, neural network weights occupy a strange conceptual middle ground. They are not traditional open source code, nor are they merely untrusted software binaries; they are dense, highly compressed mathematical encodings of vast cultural, technical, and linguistic corpora. They cannot be read in any conventional human sense like C instructions, yet they contain whole libraries of distilled human data.

This epistemic opacity is precisely why open weights are necessary. Having direct access to raw model parameters is the strict prerequisite for mechanistic interpretability, local safety probing, and post-hoc red-teaming. A proprietary API offers zero visibility into what lies behind the endpoint, whereas an open weight file allows security researchers to inspect internal activation patterns, trace knowledge representations, and strip out toxic behaviors.

Similarly, open-weight models originating from authoritarian states are indisputably shaped by domestic censorship and potential state alignment. The correct operational response, however, is to treat them with the same caution accorded to foreign-built infrastructure. Western developers can download, audit, strip out state-imposed guardrails, and repurpose foreign base parameters for independent domestic use, turning foreign releases to Western defensive advantage.

As scholars David Gray Widder, Meredith Whittaker, and Sarah Myers West point out in Nature (2024), tech giants frequently engage in openwashing—releasing model weights as a public relations gesture while keeping training datasets, filtering pipelines, and compute infrastructure firmly closed. This critique is vital, but its policy conclusion must be drawn carefully. That open-weight releases represent an incomplete form of openness is an argument for demanding greater transparency and public investment in shared compute and datasets. It is emphatically not an argument for retreating into the arms of proprietary API monopolies.

VII. The Open Security Imperative

The present impulse to restrict, license, or ban open-weight AI models repeats the classical errors of past technological panics. It mistakes corporate rent-seeking for national defense, confuses general computing primitives with finished weapons, and trades long-term systemic resilience for the illusion of central control.

A pragmatic blueprint for AI policy must start from a posture of realism. General-purpose reasoning parameters will circulate globally across open networks regardless of administrative bans. The defense of critical infrastructure relies on open access to model parameters, enabling global researchers to discover vulnerabilities and build defensive countermeasures faster than adversaries can exploit them. Policy must focus its regulatory instruments on the environment where software acts—governing identity verification, agentic tool permissions, data access, and sandboxed execution layers—rather than attempting to criminalize the distribution of general-purpose math.

To lock down American AI within proprietary walled gardens out of fear of foreign open-weight competition would be a historic miscalculation. In the long struggle for technological adaptability and national security, open systems remain, as they have always been, the ultimate line of defense.

References

Leave a comment
June 13, 2026

LLMs as computation

LLMs are now”doing” a lot of technical system design and are the object of a great deal of computer science research. However, I’ve surprised by much of the research that crosses my way (admittedly likely not a great sample) treats LLMs as a general form of intelligence without treating it as a form of computation. I expect that some combination of theory of computation (such as algorithmic information theory) and structural economics is needed to get a rigorous handle on the AI economy. This blog post contains some notes toward this end.

As we all know, an LLM is a collection of neural network weights, trained on a massive amount of information, which consumes tokens and emits predicted next tokens. Simplifying a bit, we can model an LLM as a machine that, given a string of tokens, emits a string of tokens.

Let Σ\Sigma be the set of tokens, Σ∗\Sigma^* be the space of token strings of any length. Perhaps an LLM is a function:

L:Σ∗→Σ∗L: \Sigma^* \rightarrow \Sigma^*

Really, this is LLM “inference”. I’m omitting the inherent stochasticity of LLMs — more realistically, LL would be a conditional probability distribution. But leave that aside for now.

Assuming that LL can consume as input any string, and in principle produce as output any string, what we have here is a class of “universal programming language”, another formal mathematical construct. “universal programming languages” appear in algorithmic information theory.

The simplest form of “universal programming language” is the print function. It repeats as output anything put into it. People (including myself) once joked that LLMs are glorified autocomplete; they clearly do more than this. The weights must matter.

Really, LLMs are parameterized functions — the parameters θ\theta are weights of the neural network.

Lθ:Σ∗→Σ∗L_\theta: \Sigma^* \rightarrow \Sigma^*

The weights are a compression of a great deal of training data 𝐃\mathbf{D}. Let’s assume training has converted this data to a set of weights T(𝐃)→𝛉T(\mathbf{D}) \rightarrow \mathbf{\theta}. We can refer to this foundation model as 𝐋𝛉\mathbf{L_\theta} or 𝐋𝐃\mathbf{L_D}.

What else can you do with these models? You can provide them ‘context’ — additional strings as input. You can fine-tune them on more data. And you can use them for ‘reasoning’ by chaining inputs and outputs.

  • Context: allow multiple string inputs Lθ(c,i)→oL_\theta(c, i) \rightarrow o
  • Fine-tuning: T(LD,d)→LD+dT(L_D, d) \rightarrow L_{D + d} — further compresses additional data dd into the model weights
  • Reasoning: Lθn(i)→Lθ(Lθ(...(Lθ(i)))→oL^n_\theta(i) \rightarrow L_\theta(L_\theta(…(L_\theta(i))) \rightarrow o applies the model recursively nn times

So if we want to look at the data and computation pipeline of an LLM based system, we get something like:

(Tn(D,d1,...dn))m(c,i)→o(T^n(D,d_1, …d_n))^m(c,i) \rightarrow o

I.e., we train on a base data set and several fine-tuning data sets, pick context and an input, and run inference some number of times. Each of these steps has a cost function, and we can then computer the average costs of solving various sets of problems given the available data, and other statistics. This then can be used to design the most efficient pipelines and markets.

I would be interested in hearing from anybody about whether and how this faithfully captures the essentials of LLMs as a form of computation. This is my ‘mental model’. I have left out tool use and interactivity, among other things, but those can be added in easily.

Why am I writing this? Because I think that clearly articulating the formal properties of LLMs brings a number of issues to light.

First, it foregrounds the importance of training data. Famously, the transformer architecture is very general, and early innovation in LLMs was largely about scaling it up to greater amounts of data. If we are interested in the behavior of LLMs, the training data and the training algorithm are the parts that are not “black boxes” to the model creators.

As we look at the future of LLMs in the economy, we will be looking at the results of differential access to data, as well as what data is commonly available. This shares a lot of patterns with previous iterations of concerns over “big data”, but this is obscured today because of the charisma of the models themselves.

Second, it makes explicit how information can flow and transform into a system output. The information comes first from training and fine-tuning data, then from context, then from system input. If the training and inference algorithms are general enough, none of the information relevant to a specific task comes from those parts of the system. Those algorithms are ‘general computing’.

Third, it breaks up training and inference. While training and inference are not so different in terms of information flow, they are in practice quite different because of their physical and economic costs. Currently, training is more expensive than inference. So, we see a race to, expensively, train general models with more and more data, so that less and less data is needed in context at inference time, and fewer steps are needed during reasoning. A structural model that distinguishes these can discriminate between several investment hypotheses in this space.

Fourth, by revealing LLMs as a form of general data processing and computation, it deflates (in what I think is a good and necessary way) the tendency to see ‘model evaluations’ as the best way to enforce AI accuracy, fairness, privacy, and so on. My general frustration with the model evaluation literature is that LLMs are that if they are a flavor of universal programming language by design, then there will, by definition, always be a jailbreak or a hallucination available to them. A lot of work on ‘guardrails’ at the model level seems to be about making certain kinds of outputs more difficult or expensive to get. As we’ve seen, there will be open models, and they will get fine-tuned by hobbyists and others to get around the guardrails, and so that’s not going to be an effective strategy long term.

This means that a lot of AI product design and regulation seems to be about shifting around the cost functions for achieving certain kinds of outputs with certain data. If ‘bad’ behaviors are expensive, and ‘good’ behaviors are cheap, then we have, in a sense, succeeded. But this means that the underlying economics must be part of the analysis for it to have forward-going relevance and replicability. Today’s model capabilities are a function of whatever the latest investment — at the training and inference level, as well as the data flow of context and inputs, which may go back into training — is. The entire pipeline produces ‘the intelligence’, and it does so at physical and economic cost. Computer science research, per se, with its focus on the currently available digital artifacts, is not going to achieve lasting results unless it expands its purview to these broader systems and considerations. Likewise, evaluations of models alone will not provide us the reliable theoretical knowledge needed to steer public policy. We must take into account production costs and data pipelines.

Leave a comment
February 17, 2026

updates and stubbornness about superintelligence

We seem to be in a new moment of media excitement about the implications of artificial intelligence. This time, the moment is driven by the experience of software engineers and other knowledge workers who are automating their work with ‘agents’. Clause Code etc. The latest generation of models and services is really good at doing things.

Does this change anything about my “position on AI” and superintelligence in particular?

I wrote a brief paper in 2017 about Bostrom’s Superintelligence argument. I concluded that algorithmic self-improvement at the software level would not produce superintelligence. Rather, intelligence group is limited by data and hardware.

In 2025, this conclusion still holds up, as we’ve seen that the recent impressive advances in AI has depended on tremendous capital expenditure on data centers, high-performing chips, and energy. It also depends on well-publicized efforts to collect all the text known to humankind for training data.

About 8 years ago when I was thinking about this, I wrote a bit about the connection between the Superintelligence argument and the Frankfurt School’s views on instrumental reason and capitalism. The alignment of AI with capital has born out, and has been written about by many others. What is striking about the current moment is just how on-the-nose that alignment is in the US, in terms of the full stack of energy, hardware, models, applications, and then some.

So, so far, no update.

In 2021 I published an article saying that we already had artificial systems with the capacity to outperform individual humans at many tasks. They were and still are called corporations or firms. We also had replaced markets with platform, which are similarly more performant in terms of reducing transaction costs. In that article, Jake Goldenfein and I argue that what ultimately matters are the purposes of the social system that operates the AI technology.

I believe this argument also continues to hold up. The successful models and service we are seeing are corporate accomplishments. The corporation is still the relevant unit of analysis when considering AI.

There are a number of interesting things happening now which I think are undertheorized:

  • What is the real economics of AI, given that the supply chains are so long and complex, consistent of both material and intellectual inputs, and the market for demand is uncertain? This is the trillion dollar question in terms of valuations, and it’s unanswered. The empirics here are not very good because things are far out of equlibrium.
  • Put another way: what does AI mean for the relationships between capital, corporations, labor, and consumers? Some of these relationships are mediated by rules about corporate law, intellectual property and data use, and so are determinable by law rather than technology. Information law therefore is a key point of political intervention in an economic system that is otherwise determined by laws of nature (energy, computation, etc.?

To put it another way: superintelligence has been happening and continues to happen. Some of this is due to laws of nature. But there is still a meaningful point of human intervention, which is the laws of humanity. Designing and implementing those laws well remains an important challenge.

One last thought. I’ve been inspired by Beninger’s The Control Revolution (1986) which is a historical account of the information economy in terms of cybernetics and information theory. You can ask an AI to tell you more about it, but one item comes to mind: that each new information technology first seems to threaten the jobs of people doing information work, and then leads to an expanded number of information jobs. This has to do with the way complexity is and is not managed by the technology. There’s an open question whether this generation of AI is any different. The question is truly open, but my hunch at the moment is that today’s AI systems are creating a lot more complexity than they are controlling. We will see.

Leave a comment
November 14, 2015

Marcuse on the transcendent project

Perhaps you’ve had this moment: it’s in the wee hours of the morning. You can’t sleep. The previous day was another shock to your sense of order in the universe and your place in it. You’ve begun to question your political ideals, your social responsibilities. Turning aside you see a book you read long ago that you remember gave you a sense of direction–a direction you have since repudiated. What did it say again?

I’m referring to Herbert Marcuse’s One-Dimensional Man, published in 1964.Whitfield in Dissent has a great summary of Marcuse’s career–a meteoric rise, a fast fall. He was a student of Heidegger and the Frankfurt School and applied that theory in a timely way in the 60’s.

My memory of Marcuse had been reduced to the Frankfurt School themes–technology transforming all scientific inquiry into operationalization and the resulting cultural homogeneity. I believe now that I had forgotten at least two important points.

The first is the notion of technological rationality–that pervasive technology changes what people think of as rational. This is different from instrumental rationality, which is the means ends rationality of an agent, which Frankfurt School thinkers tend to believe drive technological development and adoption. Rather, this is a claim about the effect of technology on society’s self-understanding. And example might be how the ubiquity of Facebook has changed our perception of personal privacy.

So Marcuse is very explicit about how artifacts have politics in a very thick sense, though he is rarely cited in contemporary scholarly discourse on the subject. Credit for this concept goes typically to Langdon Winner, citing his 1980 publication “Do Artifacts Have Politics?” Fred Turner’s From Counterculture to Cyberculture gives only the briefest of mention to Marcuse, despite his impact on counterculture and his concern with technology. I suppose this means the New Left, associated with Marcuse, had little to do with the emergence of cyberculture.

More significantly for me than this point was a second, which was Marcuse’s outline of the transcendental project. I’ve been thinking about this recently because I’ve met a Kantian at Berkeley and this has refreshed my interest in transcendental idealism and its intellectual consequences. In particular, Foucault described himself as one following Kant’s project, and in our discussion of Foucault in Classics it became discursively clear in a moment I may never forget precisely how well Foucault succeeded in this.

The revealing question was this. For Foucault, all knowledge exists in a particular system of discipline and power. Scientific knowledge orders reality in such and such a way, depends for its existence on institutions that establish the authority of scientists, etc. Fine. So, one asks, what system of power does Foucault’s knowledge participate in?

The only available answer is: a new one, where Foucauldeans critique existing modes of power and create discursive space for modes of life beyond existing norms. Foucault’s ideas are tools for transcending social systems and opening new social worlds.

That’s great for Foucault and we’ve seen plenty of counternormative social movements make successful use of him. But that doesn’t help with the problems of technologization of society. Here, Marcuse is more relevant. He is also much more explicit about his philosophical intentions in, for example, this account of the trancendent project:

(1) The transcendent project must be in accordance with the real possibilities open at the attained level of the material and intellectual culture.

(2) The transcendent project, in order to falsify the established totality, must demonstrate its own higher rationality in the threefold sense that

(a) it offers the prospect of preserving and improving the productive achievements of civilization;

(b) it defines the established totality in its very structure, basic tendencies, and relations;

(c) its realization offers a greater chance for the pacification of existence, within the framework of institutions which offer a greater chance for the free development of human needs and faculties.

Obviously, this notion of rationality contains, especially in the last statement, a value judgment, and I reiterate what I stated before: I believe that the very concept of Reason originates in this values judgment, and that the concept of truth cannot be divorced from the value of Reason.

I won’t apologize for Marcuse’s use of the dialect of German Idealism because if I had my way the kinds of concepts he employs and the capitalization of the word Reason would come back into common use in educated circles. Graduate school has made me extraordinarily cynical, but not so cynical that it has shaken my belief that an ideal–really any ideal–but in particular as robust an ideal as Reason is important for making society not suck, and that it’s appropriate to transmit such an ideal (and perhaps only this ideal) through the institution of the university. These are old fashioned ideas and honestly I’m not sure how I acquired them myself. But this is a digression.

My point is that in this view of societal progress, society can improve itself, but only by transcending itself and in its moment of transcendence freely choosing an alternative that expands humanity’s potential for flourishing.

“Peachy,” you say. “Where’s the so what?”

Besides that I think the transcendent project is a worthwhile project that we should collectively try to achieve? Well, there’s this: I think that most people have given up on the transcendent project and that this is a shame. Specifically, I’m disappointed in the critical project, which has since the 60’s become enshrined within the social system, for no longer aspiring to transcendence. Criticality has, alas, been recuperated. (I have in mind here, for example, what has been called critical algorithm studies)

And then there’s this: Marcuse’s insight into the transcendent project is that it has to “be in accordance with the real possibilities open at the attained level of the material and intellectual culture” and also that “it defines the established totality in its very structure, basic tendencies, and relations.” It cannot transcend anything without first including all of what is there. And this is precisely the weakness of this critical project as it now stands: that it excludes the mathematical and engineering logic that is at the heart of contemporary technics and thereby, despite its lip service to giving technology first class citizenship within its Actor Network, in fact fails to “define the established totality in its very structure, basic tendencies, and relations.” There is a very important body of theoretical work at the foundation of computer science and statistics, the theory that grounds the instrumental force and also systemic ubiquity of information technology and now data science. The continued crisis of our now very, very late modern capitalism are due partly, IMHO, by our failure to dialectically synthesize the hegemonic computational paradigm, which is not going to be defeated by ‘refusal’, with expressions of human interest that resist it.

I’m hopeful because recently I’ve learned about new research agendas that may be on to accomplishing just this. I doubt they will take on the perhaps too grandiose mantle of “the trancendent project.” But I for one would be glad if they did.

Leave a comment
August 8, 2012

on courage in the face of failure developing bluestocking

It would be easy to be discouraged by early experiments with bluestocking.

sb@lebenswelt:~/dev/bluestocking$ python factchecker.py "Courage is what makes us. Courage is what divides us. Courage is what drives us. Courage is what stops us. Courage creates news. Courage demands more. Courage creates blame. Courage brings shame. Courage shows in school. Courage determines the cool. Courage divides the weak. Courage pours out like a leak. Courage puts us on a knee. Courage makes us free. Courage makes us plea. Courage helps us flee. Corey Fauchon"
Looking up Fauchon
Lookup failed
Looking up shame
Looking up news
Looking up puts
Lookup failed
Looking up leak
Lookup failed
Looking up stops
Lookup failed
Looking up Courage
Looking up helps
Lookup failed
Looking up divides
Lookup failed
Looking up shows
Lookup failed
Looking up demands
Lookup failed
Looking up pours
Lookup failed
Looking up brings
Lookup failed
Looking up weak
Lookup failed
Looking up drives
Lookup failed
Looking up free
Looking up blame
Lookup failed
Looking up Corey
Lookup failed
Looking up plea
Lookup failed
Looking up knee
Looking up flee
Lookup failed
Looking up cool
Looking up school
Looking up determines
Lookup failed
Looking up like
Looking up us
Lookup failed
Looking up creates
Lookup failed
Looking up makes
Lookup failed
Building knowledge base
Querying knowledge base with original document
Consistency: 0
Contradictions: []
Supported: []
Novel: [(True, 'helps', 'flee'), (True, 'helps', 'us'), (True, 'determines', 'cool'), (True, 'like', 'leak'), (True, 'puts', 'knee'), (True, 'puts', 'us'), (True, 'pours', 'leak'), (True, 'pours', 'like'), (True, 'brings', 'shame'), (True, 'drives', 'us'), (True, 'stops', 'us'), (True, 'creates', 'blame'), (True, 'creates', 'news'), (True, 'Courage', 'shame'), (True, 'Courage', 'news'), (True, 'Courage', 'puts'), (True, 'Courage', 'leak'), (True, 'Courage', 'stops'), (True, 'Courage', 'helps'), (True, 'Courage', 'divides'), (True, 'Courage', 'shows'), (True, 'Courage', 'demands'), (True, 'Courage', 'pours'), (True, 'Courage', 'brings'), (True, 'Courage', 'weak'), (True, 'Courage', 'drives'), (True, 'Courage', 'free'), (True, 'Courage', 'blame'), (True, 'Courage', 'plea'), (True, 'Courage', 'knee'), (True, 'Courage', 'flee'), (True, 'Courage', 'cool'), (True, 'Courage', 'school'), (True, 'Courage', 'determines'), (True, 'Courage', 'like'), (True, 'Courage', 'us'), (True, 'Courage', 'creates'), (True, 'Courage', 'makes'), (True, 'us', 'knee'), (True, 'us', 'flee'), (True, 'us', 'plea'), (True, 'us', 'free'), (True, 'Corey', 'Fauchon'), (True, 'makes', 'plea'), (True, 'makes', 'free'), (True, 'makes', 'us'), (True, 'divides', 'weak'), (True, 'divides', 'us'), (True, 'shows', 'school')]

But, then again, our ambitions are outlandish. Nevertheless, there is a silver lining:

sb@lebenswelt:~/dev/bluestocking$ python factchecker.py "The sky is not blue."
Looking up blue
Looking up sky
Building knowledge base
Querying knowledge base with original document
Consistency: -1
Contradictions: [(True, 'sky', 'blue')]
Supported: []
Novel: []

Leave a comment