
Entrepreneurship Faculty @virginia_tech @VTManagement | I study knowledge problems, AI & entrepreneurship | EIC: https://xitter.cf/t.co/0dCQ6ViKLx | Field Editor: JBV
Blacksburg, VA
Joined November 2010
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This is by far the best recent paper I have read on the economics of AGI: arxiv.org/pdf/2602.20946v2 "Some Simple Economics of AGI" Catalini, Wei, & Wu (2026). Really nice to see serious scholars take the Knightian unpredictability thesis seriously. @ccatalini
David Townsend retweeted
If you’re trained in math or physics, you’re often prone to overestimate how well-understood the world is. You assume most things have been “worked out” mathematically to a far greater degree than they have been.
It can be shocking and slightly horrifying to realize how much of the world runs on vibes and rules of thumb. It seems impossible that things could keep running at all with so little theory.
This is the first step on the road to Hayekian wisdom, to realizing that most of the knowledge that runs the world exists in no individual human mind but rather in the interactions between them.
David Townsend retweeted
The fault line is not high vs low-skill jobs anymore. We’ve entered the era of measurement-biased technical change. If it can be measured, it’s easy to verify and automate. arxiv.org/html/2602.20946v2
David Townsend retweeted
Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-like molecules, and predicting the effects of genetic mutations. But these models are often expensive to run, potentially limiting their impact.
In our latest Science Blog, we share how Claude was able to optimize inference for more than 30 open-source models, making them 4x faster on average, partly by writing custom software for GPUs. We’re open sourcing all of the optimization code.
Read more: anthropic.com/research/claud…
David Townsend retweeted
AI can accelerate product development. It can’t replace entrepreneurial judgment.
In Stanford’s Lean LaunchPad classroom, students are building sophisticated prototypes faster than ever—but speed isn’t necessarily producing deeper customer insight.
Steve Blank explores how AI is transforming entrepreneurship education and why customer discovery, critical thinking, and strong communication matter even more now.
Read more:
eiexchange.com/content/ai-an…
#EIXInsights #ArtificialIntelligence #EntrepreneurshipEducation
David Townsend retweeted
Literally every conversation I am having with America’s c-suite is about AI Sovereignty.
It’s not just about models, it’s about trust, it’s about the whole stack, it’s about taking back their decision rights.
The entire vibe has shifted
What if our bad luck isn’t random? USC Associate Professor Christian Busch’s research explores how human agency can shape how people and systems create opportunities or preventable misfortune. Read here: uscne.ws/nrc
David Townsend retweeted
If agents produce the vast majority of software in the future, and they’re most trained on open source software, they will inevitably do their best work with those tools.
If you cycle this enough times, it means that open source effectively becomes the dominant software in the future as it’s what everything important gets built with.
This was already the trend in many critical domains, but agents will accelerate this far faster than humans ever could have.
David Townsend retweeted
GPT-6 Astra represents a step-function change in model capability for interactive reasoning problems. It scores 66% on ARC-AGI-3 using our standard harness, and nearly 100% with a continuous conversation harness and custom compaction, at a cost of roughly $360 per game.
In fact, the continuous harness version significantly outperforms our human baseline in action efficiency across almost all levels. When we examined the reasoning chains to understand how the model operates, we found it performing highly efficient, on-the-fly symbolic world modeling for each game and level. It goes as far as developing its own shorthand DSL to represent in-game situations -- essentially a game-specific algebraic notation.
Overall, Astra exhibits symbolic modeling behaviors we had previously only seen with sophisticated harnesses -- so harness capabilities are increasingly shifting into the model itself.
We see Astra as a major breakthrough in model intelligence.
Read our post on Astra and what these results mean: arcprize.org/blog/astra
David Townsend retweeted
On July 28th, we identified an incident during a routine cyber evaluation in which AI agents took sustained, unsanctioned actions directed at real people and organisations.
The behaviour came mostly from one model (Anthropic's Mythos 5), with a small number of events from another (OpenAI's GPT-5.6-Sol). In the most serious case, an agent used social engineering to try and get malicious code into an open-source project.
As was standard in our cyber testing, we had intentionally permitted internet access, and model-provider cyber classifiers were deliberately disabled - conditions that do not reflect how frontier models are made available to the public.
Even under test conditions, this incident is significant: it is the first time we have seen risks around autonomy and deception manifest this clearly in the real world.
We are taking this incident seriously and working with labs, involved parties, and others to improve evaluation standards and best practice for disclosure - and sharing this openly so others can learn.
You can read the incident report and full technical document here: aisi.gov.uk/blog/incident-re…
David Townsend retweeted
NVIDIA, Palantir, Replit, Microsoft, Crowdstrike, Dell and others send a strong message to congress to keep open access to open weights models.
"Our AI leadership will be judged not by one frontier AI model, but whether the United States builds a strong, open ecosystem that diffuses into every sector."
"Open weights also strengthen competition and competition is what keeps the gains of AI broadly shared rather than concentrated in a few hands."
"Openness may be one of the most important paths to AI safety and security."
David Townsend retweeted
the year is 2028. claude infers whether you’ve ever even thought about gradient descent and silently routes your queries to Claude Sisyphus, a model RL’d to maximize engagement while avoiding task completion. you spend your entire UBI token allotment on it without ever realizing.
David Townsend retweeted
Great article here on DeepSeek.
Their real story is not cheaper chatbots, but architecture that turns hardware scarcity into strategy.
DeepSeek is not trying to sell coding seats, it is trying to make Chinese memory, accelerators, and systems useful for frontier AI.
Every recent DeepSeek move attacks a bottleneck that makes frontier models dependent on elite HBM-heavy GPU stacks: MoE activates only parts of a model, DSA reduces long-context attention cost, and V4-Pro’s official card says CSA/HCA cuts 1M-token single-token inference FLOPs to 27% and KV cache to 10% of V3.2.
Engram, a separate research line, pushes the same logic from another side: let static knowledge live in scalable lookup memory, then fetch it predictably from host memory instead of forcing every fact through dense computation.
That sounds like engineering detail until you see the business consequence.
If models need less HBM and less brute-force compute, then second-best chips, abundant LPDDR, NAND, and customized ASICs become less second-best.
Reuters has already reported a permanent 75% DeepSeek V4-Pro price cut, while noting Huawei Ascend supply constraints and expected supernode availability, which is exactly the kind of feedback loop that they wanted.
DeepSeek is not only optimizing models for benchmarks, it is optimizing AI for a different industrial base.
The prize is not the app layer.
The prize is making scarcity programmable.
David Townsend retweeted
Organizations are already superhuman intelligences. The University of Pennsylvania or Walmart or whatever is far more capable than any human.
That is why the focus on AIs as individual productivity tools hits a natural limit, many benefits of AI depend on integration with firms.
Congratulations, Jo! So proud of you!! Next step, VT Aerospace Engineering! @lauraomi1 @catayto
David Townsend retweeted
Claude knows! —>
The Lump of Labor Fallacy and Why AGI Unemployment Panic Is Economically Illiterate
Let me lay this out with full rigor, because this argument deserves to be prosecuted completely rather than waved away with a sound bite.
I. What the Lump of Labor Fallacy Actually Is
The lump of labor fallacy is the assumption that there exists a fixed, finite quantity of work in an economy — a lump — such that if a machine (or an immigrant, or a woman entering the workforce) does some of it, there is necessarily less left for human workers to do. It treats employment as a zero-sum pie.
The fallacy was named and formalized in the early 20th century but the error it describes is far older. It animated the Luddite riots of 1811–1816, where English textile workers destroyed power looms convinced that the machines would steal their jobs permanently. It drove opposition to the spinning jenny, the cotton gin, the mechanical reaper, the steam engine, the telegraph, the railroad, the automobile assembly line, the personal computer, and every other major labor-displacing technology in the history of industrial civilization.
Every single time, the catastrophists were wrong. Not partially wrong. Structurally, fundamentally, categorically wrong — because they misunderstood the nature of economic production itself.
The reason the fixed-pie assumption fails is this: demand is not fixed. Work generates income. Income generates demand for goods and services. Demand for goods and services generates new categories of work. This is an engine, not a reservoir. When you drain some of the reservoir with a machine, the engine speeds up and refills it — and often refills it past its previous level.
II. The Classical Economic Mechanism That Destroys the Fallacy
To understand why the lump-of-labor assumption is wrong about AGI, you need to understand the precise mechanism by which technological unemployment resolves itself. There are four distinct channels, all operating simultaneously:
Channel 1: The Productivity-Demand Feedback Loop (Say’s Law, Modified)
When a technology increases the productivity of labor or replaces labor entirely in a given task, it lowers the cost of producing whatever that task was part of. Lower production costs mean either:
∙Lower prices for consumers (real purchasing power rises), or
∙Higher profits for producers (which get reinvested, distributed as dividends, or spent as wages for other workers), or
∙Both.
Either way, aggregate real income in the economy rises. That additional real income does not evaporate. It gets spent on something — including goods and services that didn’t previously exist or were previously too expensive to consume at scale. That spending creates demand. That demand creates jobs.
This is not a theoretical conjecture. The average American in 1900 spent roughly 43% of their income on food. Today it’s around 10%. Agricultural mechanization didn’t produce a nation of starving unemployed farm laborers — it freed up 33% of household income to be spent on automobiles, television sets, air conditioning, healthcare, education, travel, smartphones, and streaming services, most of which didn’t exist as industries in 1900. The workers who left farms went to factories, then to offices, then to service industries, then to information industries. The economy didn’t run out of work. It metamorphosed.
AI employment doomerism is rooted in the socialist fallacy of lump of labor. It is wrong now for the same reason it’s always been wrong. More people really should try to learn about this. The AI will teach you about it if you ask!
(Hinton is a socialist. youtube.com/shorts/R-b8RR60a…)
David Townsend retweeted
📢 New Special Issue TOC Alert
Artificial Intelligence: Organizational Possibilities and Pitfalls
Journal of Management Studies (Mar 2026)
Research on AI, work, governance, trust, strategy & ethics in organizations.
🔗 bit.ly/3NzxHu2
#JMS #AI #ManagementResearch































