Manman Lu

🇨🇳🇯🇵🇺🇸 Master of AI PM @CarnegieMellon - Exploring AI through the tiny, atypical data of my own life 🦄

[Manman Lu @ManmanLu0515]

Gesture control beats keypad and keyboard, way more intuitive to just move your hands
#VibeCoding #Robotics #AI #GestureControl #codex

[Manman Lu @ManmanLu0515]

The timeline for a "breakthrough" is still uncertain, I find myself wondering how fundamentally will this shift elevate the ceiling of Artificial Intelligence?

#LearningInPublic
#WorldModels #GenAI #AIStrategy #ArtificalIntelligence #SpatialIntelligence

[Manman Lu @ManmanLu0515]

Four technical routes in this space:
1. Video Prediction
2. Spatial Representation
3. Embodied Control
4. System-level Simulation

Training these is a massive hurdle. We lack the vast "action-consequence" datasets that exist for text, and the compute required is on another level.

[Manman Lu @ManmanLu0515]

With over $2B In just the last two months raised by @amilabs and @theworldlabs , the industry is aggressively de-risking the "language-only" bet.

#worldmodels are about giving AI the "common sense" and spatial intuition of a house cat, something text alone simply cannot do.

[Ihtesham Ali @ihteshamali]

I accidentally discovered how to compress a semester of learning into 48 hours.

A grad student at MIT showed me his NotebookLM setup. I thought he was just organized. Then I watched him pass a qualifying exam on a subject he'd never studied before.

Here's exactly what he did:

First: he didn't upload a textbook.

He uploaded 6 textbooks, 15 research papers, and every lecture transcript he could find on the subject.

Then he asked NotebookLM one question:

"What are the 5 core mental models that every expert in this field shares?"

Not "summarize this." Not "explain this topic."
Mental models. The stuff that takes professors years to develop.

But the next part is what broke my brain.

He followed up with:

"Now show me the 3 places where experts in this field fundamentally disagree, and what each side's strongest argument is."

In 20 minutes he had a map of the entire intellectual landscape of the field:
the debates, the consensus, the open questions.

Most students spend a full semester just figuring out what those debates even are.

Then he did something I've never seen before.

He asked:

"Generate 10 questions that would expose whether someone deeply understands this subject versus someone who just memorized facts."

He spent the next 6 hours answering those questions using the source material. Every wrong answer triggered a follow-up:

"Explain why this is wrong and what I'm missing."

By hour 48, he could hold a conversation with his thesis advisor without getting destroyed.

The tool didn't change. The questions did.

Most people treat NotebookLM like a fancy highlighter.
These students are using it like a private tutor who has read everything ever written on the subject.

The difference between a semester and 48 hours isn't the amount of content.

It's knowing which questions to ask.

[Valerio Capraro @ValerioCapraro]

Major preprint just out!

We compare how humans and LLMs form judgments across seven epistemological stages.

We highlight seven fault lines, points at which humans and LLMs fundamentally diverge:

The Grounding fault: Humans anchor judgment in perceptual, embodied, and social experience, whereas LLMs begin from text alone, reconstructing meaning indirectly from symbols.

The Parsing fault: Humans parse situations through integrated perceptual and conceptual processes; LLMs perform mechanical tokenization that yields a structurally convenient but semantically thin representation.

The Experience fault: Humans rely on episodic memory, intuitive physics and psychology, and learned concepts; LLMs rely solely on statistical associations encoded in embeddings.

The Motivation fault: Human judgment is guided by emotions, goals, values, and evolutionarily shaped motivations; LLMs have no intrinsic preferences, aims, or affective significance.

The Causality fault: Humans reason using causal models, counterfactuals, and principled evaluation; LLMs integrate textual context without constructing causal explanations, depending instead on surface correlations.

The Metacognitive fault: Humans monitor uncertainty, detect errors, and can suspend judgment; LLMs lack metacognition and must always produce an output, making hallucinations structurally unavoidable.

The Value fault: Human judgments reflect identity, morality, and real-world stakes; LLM "judgments" are probabilistic next-token predictions without intrinsic valuation or accountability.

Despite these fault lines, humans systematically over-believe LLM outputs, because fluent and confident language produce a credibility bias.

We argue that this creates a structural condition, Epistemia:
linguistic plausibility substitutes for epistemic evaluation, producing the feeling of knowing without actually knowing.

To address Epistemia, we propose three complementary strategies: epistemic evaluation, epistemic governance, and epistemic literacy.

Full paper in the first reply.

Joint with @Walter4C & @matjazperc

[Manman Lu @ManmanLu0515]

@michelleefang Would love to have one. Thanks so much!

[Andrej Karpathy @karpathy]

Happy weekend to those who celebrate

[Angry Tom @AngryTomtweets]

2.5 years of AI progress

https://t.co/SVVDo0b0GG

[Manman Lu @ManmanLu0515]

Insight of the day: Gen AI is basically an energy converter.

It turns raw electricity into mass-produced intelligence at scale. The opportunity is simple arbitrage.

Find the gap where:
Cost of Compute < Value of Output.

[Manman Lu @ManmanLu0515]

@60fpsdesign Love it!! The next can be 2 x 2 matrix!

[Manman Lu @ManmanLu0515]

@haider1 AI’s value to humanity ≠ AI company’s commercial value
Don't confuse the two. The impact on civilization is way broader than enterprise value.

[Manman Lu @ManmanLu0515]

@ylecun @neilzegh @GradiumAI Tried the demo. Excited to see it evolve! 🚀
1. Vocabulary-accent mismatch: eg. British words with American pronunciation
2. Add non-native accents: eg. Chinese/Singaporean English to expand diversity
3. Consistency: eg. gender changes unexpectedly when adjusting tone/accent

[Noam Brown @polynoamial]

Social media tends to frame AI debate into two caricatures:
(A) Skeptics who think LLMs are doomed and AI is a bunch of hype.
(B) Fanatics who think we have all the ingredients and superintelligence is imminent.

But if you read what leading researchers actually say (beyond the headlines), there’s a surprising amount of convergence:

1) The current paradigm is likely sufficient for massive economic and societal impact, even without further research breakthroughs.

2) More research breakthroughs are probably needed to achieve AGI/ASI. (Continual learning and sample efficiency are two examples that researchers commonly point to.)

3) We probably figure them out and get there within 20 years. @demishassabis said maybe in 5-10 years. @fchollet recently said about 5 years. @sama said ASI is possible in a few thousand days. @ylecun said about 10 years. @ilyasut said 5-20 years. @DarioAmodei is the most bullish, saying it's possible in 2 years though he also said it might take longer.

None of them are saying ASI is a fantasy, or that it's probably 100+ years away.

A lot of the disagreement is in what those breakthroughs will be and how quickly they will come. But all things considered, people in the field agree on a lot more than they disagree on.

[Google AI Studio @GoogleAIStudio]

https://t.co/oN9JI5C1Xh

[Andrew Ng @AndrewYNg]

Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenAI’s $1.4 trillion plan and Nvidia briefly reaching a $5 trillion market cap, many have asked if speculation and hype have driven the values of AI investments above sustainable values. However, AI isn’t monolithic, and different areas look bubbly to different degrees.
- AI application layer: There is underinvestment. The potential is still much greater than most realize.
- AI infrastructure for inference: This still needs significant investment.
- AI infrastructure for model training: I’m still cautiously optimistic about this sector, but there could also be a bubble.

Caveat: I am absolutely not giving investment advice!

AI application layer. There are many applications yet to be built over the coming decade using new AI technology. Almost by definition, applications that are built on top of AI infrastructure/technology (such as LLM APIs) have to be more valuable than the infrastructure, since we need them to be able to pay the infrastructure and technology providers.

I am seeing many green shoots across many businesses that are applying agentic workflows, and am confident this will grow. I have also spoken with many Venture Capital investors who hesitate to invest in AI applications because they feel they don’t know how to pick winners, whereas the recipe for deploying $1B to build AI infrastructure is better understood. Some have also bought into the hype that almost all AI applications will be wiped out merely by frontier LLM companies improving their foundation models. Overall, I believe there is significant underinvestment in AI applications. This area remains a huge focus for my venture studio, AI Fund.

AI infrastructure for inference. Despite AI’s low penetration today, infrastructure providers are already struggling to fulfill demand for processing power to generate tokens. Several of my teams are worried about whether we can get enough inference capacity, and both cost and inference throughput are limiting our ability to use even more. It is a good problem to have that businesses are supply-constrained rather than demand-constrained. The latter is a much more common problem, when not enough people want your product. But insufficient supply is nonetheless a problem, which is why I am glad our industry is investing significantly in scaling up inference capacity.

As one concrete example of high demand for token generation, highly agentic coders are progressing rapidly. I’ve long been a fan of Claude Code; OpenAI Codex also improved dramatically with the release of GPT-5; and Gemini 3 has made Google CLI very competitive. As these tools improve, their adoption will grow. At the same time, overall market penetration is still low, and many developers are still using older generations of coding tools (and some aren’t even using any agentic coding tools). As market penetration grows — I’m confident it will, given how useful these tools are — aggregate demand for token generation will grow.

I predicted early last year that we’d need more inference capacity, partly because of agentic workflows. Since then, the need has become more acute. As a society, we need more capacity for AI inference.

Having said that, I’m not saying it’s impossible to lose money investing in this sector. If we end up overbuilding — and I don’t currently know if we will — then providers may end up having to sell capacity at a loss or at low returns. I hope investors in this space do well financially. The good news, however, is that even if we overbuild, this capacity will get used, and it will be good for application builders!

AI infrastructure for model training. I am happy to see the investments going into training bigger models. But, of the three buckets of investments, this seems the riskiest. If open-source/open-weight models continue to grow in market share, then some companies that are pouring billions into training models might not see an attractive financial return on their investment.

Additionally, algorithmic and hardware improvements are making it cheaper each year to train models of a given level of capability, so the “technology moat” for training frontier models is weak. (That said, ChatGPT has become a strong consumer brand, and so it enjoys a strong brand moat, while Gemini, assisted by Google's massive distribution advantage, is also making a strong showing.)

I remain bullish about AI investments broadly. But what is the downside scenario — that is, is there a bubble that will pop? One scenario that worries me: If part of the AI stack (perhaps in training infra) suffers from overinvestment and collapses, it could lead to negative market sentiment around AI more broadly and an irrational outflow of interest away from investing in AI, despite the field overall having strong fundamentals. I don’t think this will happen, but if it does, it would be unfortunate since there’s still a lot of work in AI that I consider highly deserving of much more investment.

Warren Buffett popularized Benjamin Graham’s quote, “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.” He meant that in the short term, stock prices are driven by investor sentiment and speculation; but in the long term, they are driven by fundamental, intrinsic value. I find it hard to forecast sentiment and speculation, but am very confident about the long-term health of AI’s fundamentals. So my plan is just to keep building!

[Original text: https://t.co/psPlIFRJsi ]

[Manman Lu @ManmanLu0515]

@georgesttock NANO

[Manman Lu @ManmanLu0515]

@ai_for_success Gemini. It offers Gemini Pro free for a year for students. I dont believe ChatGPT do the same.

[Manman Lu @ManmanLu0515]

@zarazhangrui @NotionHQ meeting can bring step 1 - 3 together in one place

[Manman Lu @ManmanLu0515]

@AcquiredFM Really fun episode. I enjoyed it a lot!! Created a Infograph by @NanoBanana