technology | Digifesto

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

Widder, D. G., Whittaker, M., & West, S. M. (2024). Why ‘open’ AI systems are actually closed, and why this matters. Nature, 635(8040), 827–833. https://doi.org/10.1038/s41586-024-08141-1

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

L: \Sigma^* \rightarrow \Sigma^*

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

Assuming that $L$ 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 $\theta$ are weights of the neural network.

L_\theta: \Sigma^* \rightarrow \Sigma^*

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

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 $L_\theta(c, i) \rightarrow o$
Fine-tuning: $T(L_D, d) \rightarrow L_{D + d}$ — further compresses additional data $d$ into the model weights
Reasoning: $L^n_\theta(i) \rightarrow L_\theta(L_\theta(…(L_\theta(i))) \rightarrow o$ applies the model recursively $n$ times

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

(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

December 8, 2024

Complex systems and ethics according to contextual integrity

Complex systems theory is a way of thinking about systems with many interacting parts and functions. It draws on physics and the science of modeling dynamic systems. It’s a trans-disciplinary, quantitative science of everything. It often and increasingly gets applied to social systems, often through the methods of agent-based modeling (ABM). ABM has a long history in computational sociology. More recently, it has made inroads into economics and finance (Axtell and Farmer, 2022). That’s important intellectual territory to win over because, of course, it is vitally important for both private and public interests. Its progress there is gradual but steady. ABM and complex systems methods have no dogma besides mathematical and computational essentials. Their eventual triumph is more or less assured. As I’ve argued, ABM and complex systems theory are thus an exciting frontier for legal theory (Benthall and Strandburg, 2021). For these reasons, one line of my research is involved in developing computational frameworks (i.e. software libraries, mathematical scaffolding) for computational social scientific modeling.

Contextual Integrity (CI) is an ethical theory developed by Helen Nissenbaum. It is especially applicable to questions of the ethics of information technology and computation. Central to the theory is the idea of “appropriate information flow”, or flows of (personal) information which conform with “information norms”. According to CI, information norms are legitimized by a balance of societal values, contextual purposes, and individual ends. The work of the CI ethicist is to wrestle with the alignments and contradictions between these alignments, purposes, and ends to identify the most legitimate norms for a given context,. When the legitimate norms are identified, it is then in principle possible to design and deploy technology in accordance with these norms.

CI is a philosophy grounded in social theory. It has never been robustly quantified and many people think this is impossible to do. I’m not among these people. In fact, much of my work is about trying to quantify or model CI. It should come as no surprise, then, that I now see CI in terms of complexity theory. It has struck me recently that what this amounts to, more or less, is a computational social theory of ethics! This idea is exciting to me, and one day I’ll want to write it down in detail. For now, I have some nice diagrams and notes from a recent presentation I wanted to share.

CI is a theory of ethics that is ultimately concerned with the way that values, purposes, and ends legitimize socially understood practices. The ethicist’s job, for CI, is to design legitimate institutions. A problem for the ethicist is that some institutions can be legitimate, but utopian, in that they are not stable behavioral patterns for the sociotechnical system. Complex systems theory, as a descriptive science, is well adapted to modeling systems and identifying the regular behaviors within them, under varying conditions. Borrowing a notion from physics, a system can exhibit many regular behavioral states, which we might call phases. For example, it is well known that water has many different phases, depending on the temperature: ice, the liquid water, steam, etc.

Norms have both descriptive and (ahem) normative dimensions. (This confusing jargon is part of why it’s so hard to make progress in this area.) In other words, for there to be an actually existing norm, it has to be both regular and, to be ethical according to CI, legitimate.

There are critics of CI who argue that one problem with it is that it assumes an apolitical consensus of information norms without addressing how norms might be distorted by, e.g., power in society. This is not terribly fair to Nissenbaum’s broader corpus of work, which certainly acknowledges political complexity (see for example the recent Nissenbaum, 2024). Suffice it to say here that not all individual end up being ‘legitimized’ when ethicists assess things, and that legitimization is always political. Moreover, individual ends and politics can, of course, often be the driver of system behavior away from legitimate institutions. We can’t always have nice things.

Nevertheless, it remains useful to consider how and under what conditions a system could remain legitimate despite technological change. This is what the original CI design heuristic is: a procedure for evaluating what to do when a new technology creates a disruptive change in societal information flows.

Ideally, for CI, when a new technology destabilizes the sociotechnical system’s behavior and threatens it with illegitimate practices, society reacts (through journalism, through ethics, through a political process, through private choices and actions, etc.) and returns the system to a regular behavioral pattern that is legitimate. This might not be the same behavior as the system started with. It might be even better. And that’s OK.

What’s bad, for CI, is if the system gets stuck in an illegitimate but still robust phase.

While there are some applications of CI that serve anodyne ends of parsing and implementing uncontroversial privacy rules, there are other uses of CI as a radical critique of the status quo. This is well exemplified by Ido Sivan-Sevilla et al.’s comments on the FTC ANPR on Commercial Surveillance and Lax Data Security Practices (2022), which is a succinct and to-the-point condemnation of the “notice and consent” practices in commercial surveillance. We live in a world in which standard, even ubiquitous, technology norms depend on “laughable legal fictions” such as the idea that users of web services are legitimate parties to contracts with vendors. It is well documented how these fictions have been enshrined into law by decades of pressure by the technology sector in courts and government (Cohen, 2019).

Together, CI and complex systems theory can show how society can be a winner, or loser, beyond the sum of individual outcomes. There are certainly those that have argued that, essentially, “there is no such thing as society”, and that voluntary, binary transactions between parties are all there is. An anarchic, libertarian, or laissez-faire system certainly serves the individual ends of some, and is to some extent stable until the lords of anarchy create new systems of rules that are in their interest. It is difficult to analyze the social costs of these political changes in terms of “individual harms”, because the true marginal cost is not measurable at the level of the individual, but rather at the level of the phase transition. A complex systems theory allows for this broader view of what is at stake.

This approach also, I think, helps convey the fragility of legitimate institutions. Nothing guarantees legitimacy. Legitimate institutions typically constrain the behavior of some actors in ways that they individually do not enjoy. There are social processes which can steer a system towards a more legitimate phase, but these will meet with resistance, sometimes fail, and can be coopted by bad faith actors serving their own ends.

Indeed, there are those who would say we do not live in a legitimate system and have not lived in one for a long time. “Legitimate for whom?” Even if this is so, CI invites us to have a productive dialog about what legitimacy would entail, by sorting out different motivations and looking at the options for balancing them out. This good faith search for resolutions is often thankless and unrewarded, but certainly we would be worse off without it. On the other hand, arguments about legitimate institutions that are divorced from realistic understandings of sociotechnical processes are easily deployed as propaganda and ideology to cover illegitimate behavior. Ethics requires a science of sociotechnical systems; sociotechnical systems are complex; complex systems theory is a solid foundation for such a science.

References

Axtell, R. L., & Farmer, J. D. (2022). Agent-based modeling in economics and finance: Past, present, and future. Journal of Economic Literature, 1-101.

Benthall, S., & Strandburg, K. J. (2021). Agent-based modeling as a legal theory tool. Frontiers in Physics, 9, 666386.

Cohen, J. E. (2019). Between truth and power. Oxford University Press.

Nissenbaum, H. (2024). AI Safety: A Poisoned Chalice?. IEEE Security & Privacy, 22(2), 94-96.

13 Comments

June 9, 2024

Envisioning the Future of Computer and Information Science Research: Some Ideas

A recent Dear Colleagues Letter from the National Science Foundation Directorate for Computer and Information Science and Engineering (CISE) calls for proposals for projects to envision research priorities. It is specifically not for research itself, but for promising ways to surface and communicate new R&D directions.

Essentially, the CISE directorate is asking for people to figure out a way to identify the future of computer and information science research. Just, you know, putting it out there.

The CISE Directorate is roughly 38 years old at the time of this writing, and computing and information science have, in that time, transformed pretty much everything.

At the same time, at this present moment, there’s a sense in which computer science feels… saturated. Maybe, indeed, lacking in future vision.

Why do I feel this is so? At least two reasons:

a) In the 90’s and 00’s, so much of the potential of computer science was being discovered and unleashed by startups. Even the companies that are today Big Tech were, then, startups. Now, notoriously, a lot of startups are just weird offshoots of Big Tech companies designed to be absorbed back in when legal or market conditions are favorable. So the technical research agenda is being set by huge companies with in-house research, rather than by a loose network of innovators.

b) “Artificial Intelligence” has for a long time meant “anything that computers can’t do yet”, with the Turing Test as one of the examples of what was still an unsolved problem in computer science. Deep learning has been blasting these unsolved problems out out of the water for almost a decade now. I’d argue that the newish LLM-powered chatbots appear so ominously to be a form of “general” AI is because they command natural language so convincingly — the key challenge of the Turing Test. So, computer science is running out of unsolved problems.

c) At the same time, this so widely hyped and lauded generation of AI, which has been credited with potentially literally apocalyptic powers, has gotten over the hump of the Gartner hype cycle, and it still can’t get hands right. On the other hand, it is supposed to be making software engineering obsolete as a profession, which would in principle cut down on the demand for computer science research.

d) It is now very clear that the success of computing and information science basic research depends on its uptake in commercial and industrial settings, and that these economics depend on business, legal, and social logic that is outside the scope of computer science research per se. Computer and information science research is not successful in virtue of, but rather in spite of, its agnosticism about social context. And, increasingly, that social context is being included within the scope of computer and information science.

So, what is to be done?

One answer, which I intend seriously, is imperialism. By this I mean the expansion of computer and information science research into areas beyond its core. Another answer is that it can occupy itself by adapting to critique. I actually think a combination of both the the best answer.

By imperialism, I mean searching for unsolved problems in other sciences, and trying to crack them with computational methods. This has been done already with Go and protein folding. But most problems in the social sciences remain unsolved problems, computationally. There are indeed parts of the social sciences that are opaque to themselves and without the guiding light of computational theory.

By adapting to critique, I mean responding to the now ample critical literature, mainly produced by humanistic scholars (some legal, some STS, etc.) which aims to show the shortcomings of computer science methodology. Indeed, a lot of “information science” today operates at this critical or political level. Humanistic critique tends to stop at the level of anthropological observation.

What is not yet solved is the internalization of these critiques into computational and information theory and methods, which entail advances in the foundations of computational social science.

There are at least three research arenas that I know of which are getting at parts of these problems.

a) The Agent Foundations research agendas (e.g. PIBBSS, Causal Incentives) that have spun out of the AI Safety research communities. This work has come to understand that some foundational advances in what an agent is, in terms of computation and information, is needed to address longtermist AI safety concerns, and perhaps also more pressing problems of AI compliance in the short term. This has quite a bit of funding from Effective Altruist philanthropists.

b) Various computational institutional theory projects that can be found in the vicinity of Metagov. A lot of this is motivated by the idea of the truly self-governing digital community, a long-held Internet dream, one which got an influx of funding and interest from the blockchain boom. That blockchain/crypto flavor has left it, to some, with a funny smell. But some more academic avenues such as the Institutional Grammar Research Initiative have a more based academic stance.

c) Research into the computational foundations of agent-based modeling, such as that led by Michael Wooldridge and Anisoara Calinescu at Oxford University. Part of the interdisciplinary social science mix at the Institute for New Economic Thought, this research vein finds useful computational methods research that pushes the limits of what social systems can be modeled with computers.

The problem with social scientific problems is that they are extremely hard. They can involve multiple agents in intractable situations. Today, we have almost no social systems that are not also sociotechnical systems where the technology is creating complications, so modeling these systems is recursive and perhaps necessarily approximate. To me, these problems remain philosophically tantalizing, when so many issues seem already to be reducible to fundamentals. Maybe this is the direction of the future of computer and information science research.

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

Tag: technology

I. The Dragons in the Weights

II. Bootleggers, Baptists, and Moats

III. The Halloween Documents Revisited

IV. How the Defense State Learned to Love the Kernel

V. Compilers, Not Missiles

VI. Critiquing “Openness”

VII. The Open Security Imperative

References