Meanwhile Trump’s tariffs, especially the China trade war now escalated could hurt AI and datacenter by making stuff more expensive, lowering BigTech margins and disrupting critical supply-chains for advanced technologies.

State of Open-Source LLMs

While Meta’s Llama-4 appears to be a disappointment, and we usually think of Mistral, DeepSeek or Qwen in Open-source LLMs, I want to turn your attention to a couple of other contenders (though as we will see they are related) I think deserve a worthy mention.

Together AI who raised over $300 million Series B a month ago, have announced  DeepCoder-14B – A fully open-source, RL-trained code model! It’s interesting because it’s a code reasoning model finetuned from Deepseek-R1-Distilled-Qwen-14B via distributed RL.

Remember this is open-source, they have basically democratized the recipe for training a small model into a strong competitive coder—on-par with o3-mini—using reinforcement learning.

Some brief but important updates that very much support the themes of this newsletter:

• “Model and data size scaling are over.” Confirming the core of what I foresaw in “Deep Learning is Hitting a Wall” 3 years ago, Andrei Burkov wrote today on X, “If today’s disappointing release of Llama 4 tells us something, it’s that even 30 trillion training tokens and 2 trillion parameters don’t make your non-reasoning model better than smaller reasoning models. Model and data size scaling are over.”
• “occasional correct final answers provided by LLMs often result from pattern recognition or heuristic shortcuts rather than genuine mathematical reasoning”. A new study on math, supporting what Davis and I wrote yesterday re LLMs struggling with mathematical reasoning from Mahdavi et al, converges on similar conclusions, “Our study reveals that current LLMs fall significantly short of solving challenging Olympiad-level problems and frequently fail to distinguish correct mathematical reasoning from clearly flawed solutions. We also found that occasional correct final answers provided by LLMs often result from pattern recognition or heuristic shortcuts rather than genuine mathematical reasoning. These findings underscore the substantial gap between LLM performance and human expertise…”
• Generative AI may indeed be turning out to be a dud, financially. And the bubble might possibly finally be deflating. NVidia is down by a third, so far in 2025. (Far more than the stock market itself.) Meta’s woes with Llama 4 further confirm my March 2024 predictions that getting to a GPT-5 level would be hard, and that we would wind up with many companies with similar models, and essentially no moat, along with a price war, with profits modest at best. That is indeed exactly where we are.

Many of my students refer to AI as “he” or “she”. Some of them clearly get ‘emotionally’ attached. I remind them that the belief that computers think is a category mistake, not a breakthrough. It confuses the appearance of thought with thought itself. A machine mimicking the form of human responses does not thereby acquire the content of human understanding.

Artificial intelligence, despite its statistical agility, does not engage with meaning. It shuffles symbols without knowing they are symbols. John Searle, who is now 92 years old, pointed this out with a clarity that still unsettles mainstream confidence in the computational theory of mind.

What Searle Reminds Us
Searle’s provocation, then, is not a Luddite lament. It is a reminder: the question is not whether we can build machines that simulate intelligence. We already have. The question is whether we understand what it is they are simulating, and whether in confusing the simulation for the thing, we risk forgetting what it means to think at all.

If we forget, it will not be because machines fooled us. It will be because we preferred the comfort of mimicry to the burden of thinking and understanding.

Stay curious

What Are Guardrails and Why Do We Need Them?
Guardrails are the safety measures we build around AI systems – the rules, filters, and guiding hands that ensure our clever text-generating models behave ethically, stay factual and respect boundaries. Just as we wouldn’t let a child wander alone on a busy street, we shouldn’t deploy powerful AI models without protective barriers.

The need for guardrails stems from several inherent challenges with large language models:

The Hallucination Problem

The Bias Echo Chamber

The Helpful Genie Problem

The Accidental Leaker

How Guardrails Work in Practice

We start off with a simple question, will agents lead us to AGI? OpenAI conceptualized agents as stage 3 of 5. You can ascertain that agents in 2025 are barely functional.

Since ChatGPT was launched nearly 2.5 years ago, outside of DeepSeek, we haven’t really seen a killer-app emerge. It’s hard to know what to make of Manus AI? Part Claude wrapper, but also an incredible UX with Qwen reasoning integration. Manus AI, which has offices in Beijing and Wuhan and is part of Beijing Butterfly Effect Technology. The startup is Tencent backed, and with deep Qwen integration you have to imagine Alibaba might end up acquiring it.

Today technology and AI historian, Harry Law of  Learning From Examples , explores this awkward stage we are at halfway between reasoning models and agents. This idea that agents will lead to AGI is also quite baffling. You might also want to read some articles of the community on Manus AI: but will “unfathomable geniuses” really escape today’s frontier models, suddenly appearing like sentiment boogeymen saluting us in their made-up languages?

In Trump’s Washington, Europe’s tech regulation is a regular object of scorn. But there is one piece of American tech policy that’s united European diplomats and U.S. industry: A rule issued in President Joe Biden’s final days in office that sorted the world into three tiers for AI chip export, with more than half of Europe left off the top rung.

Under the Framework for AI Diffusion, 17 EU countries were designated Tier 2, setting caps on their access to chips needed to train AI, while the rest of Europe was set for Tier 1, with no import restrictions. Countries listed in the second tier are treating it as a scarlet letter.

“We’re going around town trying to explain that we have no idea why we ended up in Tier 2,” said one European diplomat, granted anonymity to discuss sensitive talks. “If this has to do with cooperation with the U.S. on security, we are NATO allies, we are more than willing.”

Beyond AI hype and fearmongering
The AI debate is polarized today. Technologists with Altman-esque delusions hype new tools and the impending arrival of AGI. Policymakers disconnected from ‘why this time is really different’ cling on to outdated frameworks to debate job losses. Businesses, caught in between, are confused as they struggle to distinguish AI snake-oil from the real deal.

Taking these opposing views makes no sense. And yet, it gets the memetic spread that any polarising debate will.

Reshuffle grounds these discussions on some core principles and cuts through the noise. It provides a framework to understand the fundamental nature of AI systems – and their impact on economic interactions.

Reshuffle grounds itself in a few core issues, including:

• the tension that workers have always had with tools,
• the tug-of-war that tool providers have with their customers,
• the nature of workflows and their impact on organization design, and eventually, the division of work, value, and power across an ecosystem,
• the importance of knowledge management in making organizations – and more importantly – ecosystems function,
• fundamentally new ways to build companies in an economy that is revisiting basic assumptions on knowledge work.

This blog post is a tutorial based on, and a simplified version of, the course “Long-Term Agentic Memory With LangGraph” by Harrison Chase and Andrew Ng on DeepLearning.AI.

Conclusion

We’ve now built an email agent that’s far more than a simple script. Like a skilled human assistant who grows more valuable over time, our agent builds a multi-faceted memory system:

1. Semantic Memory: A knowledge base of facts about your work context, contacts, and preferences
2. Episodic Memory: A collection of specific examples that guide decision-making through pattern recognition
3. Procedural Memory: The ability to improve its own processes based on feedback and experience

This agent demonstrates how combining different types of memory creates an assistant that actually learns from interactions and gets better over time.

Imagine coming back from a two-week vacation to find that your AI assistant has not only kept your inbox under control but has done so in a way that reflects your priorities and communication style. The spam is gone, the urgent matters were flagged appropriately, and routine responses were handled so well that recipients didn’t even realize they were talking to an AI. That’s the power of memory-enhanced agents.

This is just a starting point! You can extend this agent with more sophisticated tools, persistent storage for long-term memory, fine-grained feedback mechanisms, and even collaborative capabilities that let multiple agents share knowledge while maintaining privacy boundaries.

Marc Andreessen calls DeepSeek AI’s Sputnik moment.

Yes, it did catch the US unprepared but that’s where the ‘space race’ analogy ends.

Today’s AI race is not merely an ‘arms race’ nor is DeepSeek easily explained away as just a Sputnik moment.

This race is playing out against the larger backdrop of more than a decade of technology infrastructure export by the largest economies around the world. – whether it is India’s export of digital public infrastructure, cloud export by the US BigTech, or China’s Digital Silk Road working alongside its Belt and Road project.

And that’s what makes this truly interesting!

This combination of tech infrastructure exports combined with leverage through complementary AI capabilities creates a new format of globalization – standards-based globalization – something that most people don’t yet fully understand.

With tests of humanoid bots and new developments in military applications, the year ahead will intrigue even the skeptics.

Humanoids are put to the test
The race to build humanoid robots is motivated by the idea that the world is set up for the human form, and that automating that form could mean a seismic shift for robotics. It is led by some particularly outspoken and optimistic entrepreneurs, including Brett Adcock, the founder of Figure AI, a company making such robots that’s valued at more than $2.6 billion (it’s begun testing its robots with BMW). Adcock recently told Time, “Eventually, physical labor will be optional.” Elon Musk, whose company Tesla is building a version called Optimus, has said humanoid robots will create “a future where there is no poverty.” A robotics company called Eliza Wakes Up is taking preorders for a $420,000 humanoid called, yes, Eliza.

A smarter brain gets a smarter body
Plenty of progress in robotics has to do with improving the way a robot senses and plans what to do—its “brain,” in other words. Those advancements can often happen faster than those that improve a robot’s “body,” which determine how well a robot can move through the physical world, especially in environments that are more chaotic and unpredictable than controlled assembly lines.

At last week’s Board of Visitors meeting, George Mason University’s Vice President and Chief AI Officer Amarda Shehu rolled out a new model for universities to advance a responsible approach to harnessing artificial intelligence (AI) and drive societal impact. George Mason’s model, called AI²Nexus, is building a nexus of collaboration and resources on campus, throughout the region with our vast partnerships, and across the state.

AI²Nexus is based on four key principles: “Integrating AI” to transform education, research, and operations; “Inspiring with AI” to advance higher education and learning for the future workforce; “Innovating with AI” to lead in responsible AI-enabled discovery and advancements across disciplines; and “Impacting with AI” to drive partnerships and community engagement for societal adoption and change.

Shehu said George Mason can harness its own ecosystem of AI teaching, cutting-edge research, partnerships, and incubators for entrepreneurs to establish a virtuous cycle between foundational and user-inspired AI research within ethical frameworks.

As part of this effort, the university’s AI Task Force, established by President Gregory Washington last year, has developed new guidelines to help the university navigate the rapidly evolving landscape of AI technologies, which are available at gmu.edu/ai-guidelines.

Further, Information Technology Services (ITS) will roll out the NebulaONE academic platform equipping every student, staff, and faculty member with access to hundreds of cutting-edge Generative AI models to support access, performance, and data protection at scale.

“We are anticipating that AI integration will allow us to begin to evaluate and automate some routine processes reducing administrative burdens and freeing up resources for mission-critical activities,” added Charmaine Madison, George Mason’s vice president of information services and CIO.

George Mason is already equipping students with AI skills as a leader in developing AI-ready talent ready to compete and new ideas for critical sectors like cybersecurity, public health, and government. In the classroom, the university is developing courses and curriculums to better prepare our students for a rapidly changing world.

In spring 2025, the university launched a cross-disciplinary graduate course, AI: Ethics, Policy, and Society, and in fall 2025, the university is debuting a new undergraduate course open to all students, AI4All: Understanding and Building Artificial Intelligence. A master’s in computer science and machine learning, an Ethics and AI minor for undergraduates of all majors, and a Responsible AI Graduate Certificate are more examples of Mason’s mission to innovate AI education. New academies are also in development, and the goal is to build an infrastructure of more than 100 active core AI and AI-related courses across George Mason’s colleges and programs.

The university will continue to host workshops, conferences, and public forums to shape the discourse on AI ethics and governance while forging deep and meaningful partnerships with industry, government, and community organizations to offer academies to teach and codevelop technologies to meet our global society needs. State Council of Higher Education for Virginia (SCHEV) will partner with the university to host an invite-only George Mason-SCHEV AI in Education Summit on May 20-21 on the Fairfax Campus.

Virginia Governor Glenn Youngkin has appointed Jamil N. Jaffer, the founder and executive director of the National Security Institute (NSI) at George Mason’s Antonin Scalia Law School, to the Commonwealth’s new AI Task Force, which will work with legislators to regulate rapidly advancing AI technology.

The university’s AI-in-Government Council is trusted resource for academia, public-sector tech providers, and government for advancing AI approaches, governance frameworks, and robust guardrails to guide AI development and deployment in government.

Learn more about George Mason’s AI work underway at gmu.edu/AI.

Reading Karp and Zamiska (Zami) prompted me to think about the Singularity again. Regardless of how we look at it, AI is increasing its capabilities at a rapid pace, far beyond what the public realize. Soon we will have increasingly advanced iterations of AI. Think of AI plus, then AI plus, plus and AI plus, plus, plus and then what awaits us when intelligence surpasses its creators? This is also articulated by two of the CEO’s of leading AI labs, Demis Hassabis and Dario Amodei in this conversation.

It is not the stuff of distant myth or idle speculation. This is our proximate future, a trajectory set in motion by the relentless march of accelerating computation and recursive self-improvement. The singularity, so named by John von Neumann before being elaborated upon by I. J. Good, Vernor Vinge, and Ray Kurzweil, is no longer a concept confined to speculative fiction or Silicon Valley techno-utopianism. It is a inevitable force, steadily reshaping our institutions, our identities, and our very notion of control. As Good himself put it,

“the first ultraintelligent machine is the last invention that man need ever make.”

As Elon Musk’s “Department of Government Efficiency” rampages through the federal bureaucracy, demanding staff and budget cuts, one tech industry that counts on the government for fundamental research and funding is projecting confidence.

Quantum has come a long way since it caught the interest of civilian, defense and intelligence agencies in the 1990s as a theoretical, ill-understood future paradigm changer.

Since then, quantum computers have grown steadily larger and more functional — though still very much in the realm of experiment and science. Last week, Microsoft CEO Satya Nadella showed off a new palm-sized quantum chip that he proclaimed was the physical representation of its 20-year pursuit of creating “an entirely new state of matter” and would lead to a truly meaningful quantum computer in years instead of decades.

GPT-4.5, OpenAI’s big new model, represents a significant step forward for AI’s industry leader. It could also be the end of an era.

The big picture: 4.5 is “a giant, expensive model,” as OpenAI CEO Sam Altman put it. The company has also described it as “our last non-chain-of-thought model,” meaning — unlike the newer “reasoning” models — it doesn’t take its time to respond or share its “thinking” process.

Why it matters: The pure bigger-is-better approach to model pre-training now faces enormous costs, dwindling availability of good data and diminishing returns — which is why the industry has begun exploring different roads to continuing to improve each new AI generation.

Between the lines: Building and powering the massive data centers required to build and run the latest models has become an enormous burden, while assembling ever-bigger datasets has become challenging, since today’s models already use nearly all the data available on the public internet.

And Why Are Today’s “PhD” AIs So Hard To Apply To Everyday Tasks?

There’s a huge disconnect between AI performance on benchmark tests, and its applicability to real-world tasks. It’s not that the tests are wrong; it’s that they only measure things that are easy to measure. People go around arguing that AIs which can do everything people can do may arrive as soon as next year2. And yet, no one in the AI community has bothered to characterize what people actually do!

The failure to describe, let alone measure, the breadth of human capabilities undermines all forecasts of AI progress. Our understanding of how the world functions is calibrated against the scale of human capabilities. Any hope of reasoning about the future depends on understanding how models will measure up against capabilities that aren’t on any benchmark, aren’t in any training data, and could turn out to require entirely new approaches to AI.

I’ve been consulting with experts from leading labs, universities and companies to begin mapping the territory of human ability. The following writeup, while just a beginning, benefits from an extended discussion which included a senior staff member at a leading lab, an economist at a major research university, an AI agents startup founder, a senior staffer at a benchmarks and evals organization, a VC investing heavily in AI, the head of an economic opportunity nonprofit, and a senior technologist at a big 5 tech company.

The real AI revolution will not be televised. It will only begin in mid-year, when the $3,000 Nvidia device Jensen Huang calls “Project Digits” ships. While the last two years have recapitulated the first computer revolution, the next years will recapitulate the PC revolution.

And when the applications are in place, the second Internet revolution will commence.

How to cultivate discernment in the Intelligence Age, and the importance of truth for a healthy society

We are more than Biological Machines

In the 21st century we may require an AI not simply designed to “augment” ourselves or be more “productive” as economic agents in society, but also to enable us to develop things like wisdom, discernment and clarity in the synthesis of the sum total of our human experiences.

Can AI be used to bridge the gap to discover new forms of meaning, connection and enlightenment?

• What might a Vedic cosmologist say about AI? Chad covers a lot of angles here today and it’s a privilege to have a philosophical writer enter this debate to enrich the awareness of our readers.