David Wiley

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

[Nathan Lambert @natolambert]

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)

[John Capobianco @John_Capobianco]

SCREAM IT FROM THE ROOFTOPS LORD TORVALDS

[Andrej Karpathy @karpathy]

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.

[David Wiley @opencontent]

@gsiemens @DonaldClark You should share that prompt. Looks like fun. 😂

[David Wiley @opencontent]

Author a complete, high-quality open textbook in a day.

https://t.co/vvJLFoPjPe

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

[clem 🤗 @ClementDelangue]

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! 🤗🤗🤗

[elvis @omarsar0]

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.

[David Wiley @opencontent]

@gsiemens Here we come!

[David Wiley @opencontent]

Great things are happening in the Marshall University Center for Innovation and Entrepreneurship (iCenter)!

https://t.co/RvWS98tbYA

[David Wiley @opencontent]

😳🤯

[Andrej Karpathy @karpathy]

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.

[David Wiley @opencontent]

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?

https://t.co/yO8dl909ww

#ai #opensource

[SSRN @SSRN]

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: https://t.co/7fdpaeAYhn
#Law

[Simon Willison @simonw]

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 https://t.co/XIskcgeBFE

[David Wiley @opencontent]

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.

[David Wiley @opencontent]

A handful of us worked really hard from about 2015 - 2018 to help publishers see the benefits of being open. Several started sponsoring the OpenEd Conference, attending the conference, and even announcing fledgling #OER initiatives at the conference. But in the conference hallways and at their sponsor booths, publisher representatives were told - in no uncertain terms - that the first, small steps they were taking toward openness were unacceptable, and that they weren't welcome at the event or in the community.

A ton of time and effort - and even more good will - went up in a puff of smoke. In my opinion it was a massive "own goal" by the community and set them back at least a decade.

So to answer your question, you would need two things. First, you'd need new leadership at the publishers who don't remember how badly they got burned by the community. Second, you would need a community that was willing to admit the massive amount of value commercial publishers could add as members, that would do the hard work of helping publishers understand the value publishers would gain by being open, and that nurtured and supported the publishers as they struggled to find their way into the community.

[David Wiley @opencontent]

In this new episode of the Speaking of Higher Ed podcast, Arthur Takahashi interviews me about #OER, #GenerativeAI, and how the integration of these might change the future of education. I've been interviewed a number of times in the past, but no one has ever been as well prepared for our interview as Arthur. This was really fun conversation and provides a great overview of my current thinking on these topics.

https://t.co/gGuCwnvpzw

[David Wiley @opencontent]

@EdTechAly I would be super happy about how successfully the open source software community engaged commercial software companies and super depressed about how the OER community has utterly and completely failed to engage commercial course materials companies.

[Aakash Gupta @aakashgupta]

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.

[UCF Digital Learning @UCFDigitalLearn]

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: https://t.co/O7LuCbbURv

[David Wiley @opencontent]

Mood.

[Amanda Askell @AmandaAskell]

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.