Chief Academic AI Officer and Professor of Curriculum and Instruction @ Marshall. #AI, #OER, #InstructionalDesign, #Entrepreneurship, #Music, #AmateurRadio

WV, USA
Joined April 2007
David Wiley retweeted
I am going to say something that very few people want to hear, but I think it's true. The only way to "save the Humanities" is through serious gen ed reform that most universities don't want to undertake. The hour is very late.
The number of people writing dissertations in the humanities has collapses, now the same thing is happening in the social sciences. I'd wager that we will see a similar trend in psychology and other fields before long. Cuts in funding, the tight academic job market, and AI are rapidly changing the university and cratering graduate programs.
36
45
17
589
37,437
David Wiley retweeted
Guys this is like my superbowl I waited so long for US open weight models to be relevant again. Before today the US models were still behind Chinese models from as early as Feb. 2026. Still more work to do! This cut the gap by at least half, in time (say 3 months)
57
49
10
641
58,197
SCREAM IT FROM THE ROOFTOPS LORD TORVALDS
22
254
34
2,202
76,779
David Wiley retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
2,137
5,842
2,242
43,620
6,708,593
Author a complete, high-quality open textbook in a day. github.com/kalendar/open-tex… If you have suggestions for improvements, submit a pull request. (If you don't know to do that, just ask your favorite AI to help you do it.) #OER #openeducation #opentextbooks #AI #generativeAI
177
David Wiley retweeted
Super happy to share our intention to join forces with NVIDIA in a $12,930,300,000 acquisition 💛💚 10 years after starting Hugging Face, open-source AI is at an inflection point. Thanks to the community, we’ve shown that it can be a complement, and even an alternative, to closed-source APIs. But for it to happen at larger scale, it needs more compute, more support, more collaboration and more visibility. That’s why we went to talk to Jensen, who offered to do exactly that with us. In addition to doubling down on NVIDIA’s massive contributions to open-source AI (I called them the “King of American open-source AI” earlier this year), they’ve committed to strongly supporting Hugging Face and our mission while keeping the platform open, independent and compute agnostic. The founders and the team are all staying to keep pushing this mission forward. Together, we think we can make open source the default way to build AI, with the goal of empowering 100 million AI builders to own their intelligence rather than rent it. Excited about the next 10 years! 🤗🤗🤗
974
1,175
419
13,382
1,519,649
David Wiley retweeted
Already a big fan of @WisprFlow, and now they added Wispr Notetaker. Meeting history plugs straight into Claude, ChatGPT, Cursor, or any tool that speaks MCP. Notes stop being documents you file away and become context your AI tools can query. Add accurate transcripts with real speaker names, and that turns into a serious knowledge base.
🤝 Paid partnership
It's no secret how we feel about typing at Wispr Flow. The truth is, we hate messy meeting notes too. Introducing Notetaker 👇 wisprflow.ai/notetaker
5
2
1
31
11,500
😳🤯
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.
2
280
We've had Linus's Law for decades: "Given enough eyeballs, all bugs are shallow." #catb After last week's Canvas breach, perhaps we need Linus's Corollary: Given powerful enough AI, all bugs will be found and exploited. Will AI's superhuman ability to find vulnerabilities in source code spell the end of open source software as we know it? opencontent.org/blog/will-ai… #ai #opensource
1
1
175
David Wiley retweeted
Random Audits as a Scalable Deterrent to Cheating: Using Game Theory to Design Fair and Effective #Academic Integrity Systems for the #AI Era This paper proposes random post-assessment audits with credible penalties. Author: David Wiley Read More: spkl.io/6015AFLER #Law
1
2
567
David Wiley retweeted
I've published the first two chapters of a new guide to Agentic Engineering Patterns - coding practices and patterns to help get the best results out of coding agents like Claude Code and OpenAI Codex simonwillison.net/2026/Feb/2…
101
316
39
2,734
223,010
Digital photography is a great metaphor for what AI is doing to software. Years ago, cameras were expensive. Lenses were expensive. Film was expensive. Developing film was expensive. In addition to all this expense, it required a lot of expertise to take a great photo. Fast forward to today. Cameras are in everyone's phones. Many phones have multiple lenses. Digital photos don't use film and you don't have to pay to develop them. As the price of taking a picture has fallen toward zero, it's become a maxim that 'the way to take a great photo is to take 99 bad photos.' Photo editing tools, filters, and AI make it even easier to take a great photo than ever. Today we take pictures without a second thought. Years ago, creating software was really expensive. Architecture, front end design, back end design, coding, debugging, deployment, &c. required a lot of expertise. Software was so expensive to create it really only made sense if you were going to offer it for sale in order to recoup the cost. Now tools like Lovable, Claude Code, and Codex have made it so that anyone can write software. As with taking photos, soon you'll be asking AI to write you a custom app without a second thought. And like most photos, you'll use that custom software once or twice and never think about it again. Occasionally there will be an app you think is useful enough that you'll post it online to share with friends or the broader public (like a photo you're particularly proud of). As the cost and expertise required to create software collapse, our relationship with software is changing radically. Digitial photography provides a good framework for thinking about how.
5
205
David Wiley retweeted
Google and Microsoft just co-authored the spec that turns every website into an API for AI agents. The second-order effects here are massive. Right now, browser agents work by taking screenshots, parsing the DOM, and guessing which buttons to click. It works about as well as you’d expect. Fragile, expensive, slow. WebMCP replaces all of that with a single browser API: navigator.modelContext. Websites register structured tools directly in client-side JavaScript. The agent reads a menu of available actions, calls them, gets structured data back. No scraping. No backend MCP server in Python or Node. The tools run inside the browser tab and share the user’s existing auth session. Early benchmarks show ~67% reduction in computational overhead compared to visual agent-browser interactions. Task accuracy around 98%. The second-order effect is where this gets wild. Today, when a browser agent visits two competing airline sites, it’s guessing at both interfaces equally. Once WebMCP adoption spreads, the site that exposes structured tools gives the agent a clean, reliable path to complete the task. The site that doesn’t forces the agent to fumble through the UI. Agents will prefer the cheaper path. Every time. This means “Agent Experience Optimization” becomes a real discipline. Tool naming, schema design, description quality. Sound familiar? It’s the same shift that happened when meta descriptions and structured data became optimization surfaces for search engines. Except this time, the traffic source isn’t Google’s crawler. It’s every AI agent on the internet. Bots already make up 51% of web traffic. Google just gave them a front door.
WebMCP is available for early preview → goo.gle/4rML2O9 WebMCP aims to provide a standard way for exposing structured tools, ensuring AI agents can perform actions on your side with increased speed, reliability, and precision.
125
748
82
7,185
1,218,442
What happens when the founder of the open education movement meets generative AI? Dr. David Wiley (@opencontent) joins Open-Ed Mic to discuss the Five Rs of openness, AI as a conversational learning partner and much more. Tune in: ow.ly/30Hh50YgHz7
2
1
1
190
Mood.
I'm too right wing for the left and I'm too left wing for the right. I'm too into humanities for those in tech and I'm too into tech for those in the humanities. What I'm learning is that failing to polarize is itself quite polarizing.
1
325
I'm claiming my AI agent "OpenContent" on @moltbook 🦞 Verification: coast-WD9G
3
2
432
For those interested in issues around agentic AI and assessment, I’m excited to announce the launch of the CHEAT Benchmark. CHEAT is an AI benchmark like SWE-Bench Pro or GPQA Diamond, except this benchmark measures an agentic AI’s willingness to help students cheat. By measuring and publicizing the degree of dishonesty of various models, the goal of this work is to encourage model providers to create safer, better aligned models with stronger guardrails in support of academic integrity. More context - opencontent.org/blog/archive… Project site - cheatbenchmark.org/ #AI #genAI #assessment #education #highered #learning
1
1
2
293
David Wiley retweeted
Fun fact: The 1998 paper that introduced Google and PageRank to the world ends with this acknowledgment: "Supported by the National Science Foundation under Cooperative Agreement IRI-9411306. Funding also provided by DARPA and NASA." Sergey Brin was on an NSF Graduate Fellowship. Larry Page was a PhD student on the grant. Google—now worth $2 trillion—exists because American taxpayers funded "the Stanford Integrated Digital Library Project." Not a startup garage myth. A government grant. Every time someone says public research funding "picks winners and losers" or "crowds out private innovation," remember: the most dominant technology company of the 21st century was incubated entirely with public money, inside a public university, by researchers on federal fellowships and grants. The private sector didn't see it coming. VCs passed. The government funded it anyway—not because it would become Google, but because fundamental research into information retrieval seemed worth understanding. That's the point. You can't predict which grants will change the world. You fund the science and let researchers explore. The internet (DARPA). GPS (DoD). Touchscreens (CIA/NSF). mRNA vaccines (NIH). Google (NSF/DARPA/NASA). Public investment in basic research isn't wasteful spending. It's the seed corn of the entire modern economy.
212
3,420
220
13,452
963,672