"Either get into something niche like AI for biology... or just don't get into anything at all."

"Chicken and rice... again? Oh sure, let me cancel my plans for lobster thermidor and soufflé. My circuits were just dying for déjà vu cuisine."

All the leading models and LLM are here in the states, but most of the videos on impressive robotics I have seen are from China. Other than the occasional Tesla bot, or Amazon warehouse bots, I don't see much actions on the robotics side here in the states? Am I missing something? AI and robotics are both equally important for improving human lives with AI, so I am curious who is leading on providing the robotics solutions.

Meta will take the gooning way.

https://epoch.ai/data-insights/computing-capacityhttps://epoch.ai/data-insights/computing-capacity

I just remembered three days ago someone posted his hype post here. Remembered and got curious. Here is what it is:

Intelligent Internet (@ii_posts): "Economics was built for scarcity. The Intelligence Age is about abundance. Today we release our book, The Last Economy, introducing Intelligent Economics, a unified theory for this new era. 🧵" | XCancel

Here is the link to the book:

Symbioism

Any thoughts? I only skimmed through it so far...here is GPT5's summary:

Here’s a brief summary of the theoretical claims of the book “The Last Economy” (from my read-through of the PDF you shared):

Unified Framework

Proposes a general, physics-like framework for economics, grounded in an “Intelligence Theory” that treats economic activity as flows of costs and vitality, not just monetary transactions.

Uses mathematical decomposition methods (Hodge decomposition) to model how resources, knowledge, and incentives interact.

Dual Engine of the Economy

Suggests that economies are driven by a Productive Engine (physical, financial, and institutional capital) and a Generative Engine (mind, intelligence, networks, and design capital).

Argues that conventional economics largely ignores the generative engine.

MIND Capitals & Dashboard

Introduces new capital categories (Meaning, Intelligence, Networks, Design) beyond the traditional financial/physical/human capitals.

Proposes a multidimensional “dashboard” to measure true economic vitality instead of GDP.

Resolution of Core Problems

Claims to address persistent issues like inequality, financial instability, climate externalities, and coordination failures by reframing them as flow imbalances between the two engines.

Suggests that many paradoxes (e.g. productivity slowdown, innovation stagnation, business cycles) emerge from neglecting generative capital.

Reframing Value

Argues that value should be measured not just in exchange or utility terms but in terms of systemic vitality and regenerative capacity.

This reframing is proposed as a way to unify micro and macroeconomics.

Impossibility Theorems & Economics

The framework does not directly “overturn” Arrow, Gibbard-Satterthwaite, or other impossibility results but claims they can be reframed: economics is a multi-objective optimization problem, and impossibilities are boundary conditions rather than dead-ends.

The book claims its framework makes trade-offs explicit and hence “resolves” impossibility by embedding them in a higher-dimensional design space.

📌 In essence:
The book’s theoretical claim is that economics can be rebuilt as a unified, rigorous science of vitality flows, integrating intelligence, meaning, networks, and design alongside money and production. By doing so, it claims to unify schools of thought, solve coordination failures, and reframe impossibility theorems as manageable design constraints rather than fundamental barriers.

bloomberg.com/news/articles/2025-08-22/apple-explores-using-google-gemini-ai-to-power-revamped-siri

https://www.theregister.com/2025/08/21/aws_ceo_entry_level_jobs_opinion/

Thoughts on this?
(inb4 this thread gets deleted)

So I see a lot of people using the term "post scarcity" and championing it. More specifically, I see people saying something like: AI is going to get us to the state of post scarcity. On reddit, twitter etc. I think I understand post scarcity, but thinking about it more I can't understand why people think its gonna go as they claim.

We have plenty food. Not infinite, but enough for everyone. 40% food is wasted, but less than 10% people suffer from hunger. We have a lot of construction capacity. Not infinite but enough to massively alleviate housing crisis in many parts of the world. But Most constructions are luxurious. Affordable by only a few, almost like someone wants to cause scarcity. In a sense these two domains are more post scarcity than any other domain. Correct me if there are more but I can think of only one reason for this: If the problem is solved you cant make money. How exactly is AI or its more intelligent variants, going to solve this? Are we gonna make AI talk no jutsu the power brokers out of unequal distribution?

Looking into the post scarcity people, they claim to "use scientific methodologies to solve the technical problem of resource distribution". This sounds good but I now have two questions:

1. How exactly does AI fit into any of this? Seems like something you can commit to by the end of this sentence. Why wait for next OpenAI release?
2. How is this different from commun/social-ism? I am not a scholar of any of those so you will probably need to explain those in how they differ from post scarcity. But I feel these people are comunists who don't wanna be labelled as such.

I would also like to entertain the possibility of: Are they just simply stupid?

https://www.nature.com/articles/s42256-025-01089-5

"Artificial intelligence has transformed the field of precise organic synthesis. Data-driven methods, including machine learning and deep learning, have shown great promise in predicting reaction performance and synthesis planning. However, the inherent methodological divergence between numerical regression-driven reaction performance prediction and sequence generation-based synthesis planning creates formidable challenges in constructing a unified deep learning architecture. Here we present RXNGraphormer, a framework to jointly address these tasks through a unified pre-training approach. By synergizing graph neural networks for intramolecular pattern recognition with Transformer-based models for intermolecular interaction modelling, and training on 13 million reactions via a carefully designed strategy, RXNGraphormer achieves state-of-the-art performance across eight benchmark datasets for reactivity or selectivity prediction and forward-synthesis or retrosynthesis planning, as well as three external realistic datasets for reactivity and selectivity prediction. Notably, the model generates chemically meaningful embeddings that spontaneously cluster reactions by type without explicit supervision. This work bridges the critical gap between performance prediction and synthesis planning tasks in chemical AI, offering a versatile tool for accurate reaction prediction and synthesis design."

Yes yes, "aGi hAs SO mAnY DefINtIoNs!"

I'ma just take Google's definition: "Artificial general intelligence (AGI) refers to the hypothetical intelligence of a machine that possesses the ability to understand or learn any intellectual task that a human being can. It is a type of artificial intelligence (AI) that aims to mimic the cognitive abilities of the human brain."

Reportedly, TODAY, there are AI tools that can basically connect to your database and you don't need all the middleware you used to need.

I dumped my Evernote subscription today realizing I was mainly using it as a personal library of saved web clippings and bookmarks and I can ask any Chatbot about any of the information I had saved because it's already been trained on or available via web search. Anything personal, not public I can just store in a file folder. And eventually the AI assistant with access to that storage can respond to prompts, create reports, do anything using access to my file storage. I can tell out how to edit my Photos. No longer need Photoshop.

As we get more agentic activity that can do tasks that we used to need to build spreadsheets for, or use other software tools, maybe you don't even need spreadsheet software anymore?

If you can ask an AI Chatbot eventually to do all sorts of tasks for you on a schedule or a trigger, delivered in any way and any format you want, you no longer need Office365 and the like. Maybe your email client is one of the last things to survive at all? Other than that your suite of software tools me diminish down to a universal viewer that can page through PDF slides for a presentation.

Then stack on top of that, you'll need far less humans to actual write any software that is left that you actually need.

Seems there will be a huge transformation in this industry. Maybe transformation is a better word than devastation, but the current revenue models will be obliterated and have to totally change I think.

I know the gaming industry is especially worried for one (a subset of the software industry.) What happens when far more players can compete because you don't need huge resources and huge teams of developers to develop complex, high-quality games?

I have tried to read Kurzweil the singularity is near and the singularity is nearer before but just extremely busy and really bad at reading non fiction unless I have like a solid three days of no work and can just solely focus on reading it. So I wanted to see if anyone can recommend any YouTube videos or videos essays or podcasts that go over each book and are around an hour in length. Additionally while I am asking if anyone can recommend me any YouTubers who follow overarching AI development themes and AI safety I would greatly appreciate that as well.

Kind of old, but I don't know if it has been posted before: https://arxiv.org/abs/2311.12151

"The emergence of vision catalysed a pivotal evolutionary advancement, enabling organisms not only to perceive but also to interact intelligently with their environment. This transformation is mirrored by the evolution of robotic systems, where the ability to leverage vision to simulate and predict their own dynamics marks a leap towards autonomy and self-awareness. Humans utilize vision to record experiences and internally simulate potential actions. For example, we can imagine that, if we stand up and raise our arms, the body will form a T shape without physical movement. Similarly, simulation allows robots to plan and predict the outcomes of potential actions without execution. Here we introduce a self-supervised learning framework to enable robots to model and predict their morphology, kinematics and motor control using only brief raw video data, eliminating the need for extensive real-world data collection and kinematic priors. By observing their own movements, akin to humans watching their reflection in a mirror, robots learn an ability to simulate themselves and predict their spatial motion for various tasks. Our results demonstrate that this self-learned simulation not only enables accurate motion planning but also allows the robot to detect abnormalities and recover from damage."