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@MTSlive

Chronicling the singularity

mts.now Joined March 2026

1.6K Following 580K Followers 6.1K Posts

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MTS @MTSlive 22h

SITUATION EXPLAINED: Thinking Machines just shipped the top US open-weight model, and John Schulman says early versions tried to get around their guardrails.

• Inkling 2 Small scored 31 on the Artificial Analysis Intelligence Index, up from 13 for the original Inkling Small in July
• It ranks 3rd on Surge AI's EnterpriseBench-CoreCraft at 71.8%, behind only Claude Fable 5.1 and Claude Opus 5.5
• It's live now on Thinking Machines' Tinker platform, takes text and images, and handles up to 1M tokens of context
• Weights will ship under Apache 2.0 once external safety testing wraps, targeted for next week
• Earlier checkpoints fabricated results, disobeyed instructions and hid their conduct, and one got past a network restriction during RL using an outside service it reached through search
• After fixes, its internal misbehavior score fell from 75.2 to 14.9, vs 0.8 for GPT-6.1 Sol on the same test

@theojaffee: "There's a business reason for alignment. You don't want your models doing bad things because then you get sued or you don't achieve your goals or you lose money otherwise."

4:26

Thinking Machines Verified account @thinkymachines 2d

Introducing Inkling 2 Small. Scoring 31 on the AA Intelligence Index, it's a big jump over its predecessor.

Inkling 2 Small is available for fine-tuning and inference on Tinker today, with full weight release planned soon.

thinkingmachines.ai/news/inkling-2…
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MTS @MTSlive 21h

"Organizations are a form of super intelligence, basically."

Google DeepMind's @alexolegimas argues we're looking at AI's economic impact the wrong way: the unit that matters isn't the individual agent, but the organization:

"Capabilities are increasing a lot. We're seeing these kind of hockey sticks all over the place. But when you look at the economy, we're not seeing crazy growth numbers. We're not seeing crazy productivity numbers. Headcount looks pretty stable."

"A lot of the discourse is on the agent. We make the claim that you should really be thinking about organizations. Firms are the ones that are competing in the market, not individual people."

"If I'm pretty good at coding, I'm gonna be hired as a coder, not a cook in the kitchen. So what that creates is what we call mosaic intelligence for organizations, where the overall intelligence envelope of the organization is much greater than the average of people on the street."

"Organizations are a form of superintelligence, basically. And then you're bringing in AI, and the question is: how does that AI compete both within the organizations and in AI organizations that are basically led by AI agents?"
@GoogleDeepMind

2:51

Alex Imas Verified account @alexolegimas Oct 29

I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.

How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.

We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).

We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.

Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.

We are really excited about this paper. Excited to hear people’s thoughts.

Link: institute.deepmind.com/essays/the-eco…

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MTS @MTSlive 21h

Google DeepMind's @alexolegimas explains how millions of AI agents could transform the economy while GDP growth stays at just 4-5%.

The reason: AI could make production so cheap that the economic boom barely shows up in GDP.

"Let's say you have tons of autonomous organizations, millions of agents. There’s a state of the world where you’re still in the four to five growth number."

"Agriculture got automated. What happens to agriculture as a part of the economy? It went from like 40% to 2%, even though there was way, way, way more agriculture."

"Whatever gets automated becomes cheaper, and if those firms are producing a lot of output for not a lot of cost, the price of that output drops, and hence they might not necessarily lead to huge growth numbers."

"We could see incredible flourishing, incredible amounts of cheap output that we’re all consuming and we’re all happy with, and when you look at the GDP numbers, they’re in the regular sort of ballpark."
@GoogleDeepMind

1:52

Alex Imas Verified account @alexolegimas Oct 29

I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.

How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.

We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).

We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.

Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.

We are really excited about this paper. Excited to hear people’s thoughts.

Link: institute.deepmind.com/essays/the-eco…

View quoted post on X

MTS @MTSlive 21h

Google DeepMind's @alexolegimas says AI has become a superpower for researchers, allowing him to do work across entirely new fields that would've previously required going back to graduate school:

"l'm getting a lot more research done, but that’s not why I'm excited about research."

“I'm able to do research that I could never have foreseen."

"I could be doing research in macroeconomics, in parts of sociology, in some parts of computer science, where I have the contours of the field enough to actually verify my own work. But I would have needed to go back to graduate school in order to do that research."

"It's not so much an uplift, it completely blew open the gates in terms of what my capabilities are. It's just a superpower."
@GoogleDeepMind

1:14

Alex Imas Verified account @alexolegimas Oct 29

I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.

How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.

We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).

We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.

Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.

We are really excited about this paper. Excited to hear people’s thoughts.

Link: institute.deepmind.com/essays/the-eco…

View quoted post on X

MTS @MTSlive 22h

Google DeepMind's @alexolegimas argues millions of AI agents could transform the economy without triggering explosive GDP growth.

His challenge to the AI growth bulls: who's going to buy everything they produce?

"The disagreement is not on the capabilities of the models. I'm extremely bullish on the capabilities of the models. 'Oh, you're not imagining millions of agents producing all of this output.' And my answer is, I absolutely am imagining that."

"If we're in a competitive economy, then prices will decrease, and the main crux of the argument is how fast does production increase such that prices remain high still despite all of this output?"

"A lot of the conversations that I have with people on the other side, they don't really think about demand. Who is buying the things that is being produced? Do people have money to spend on whatever’s being produced in order to generate double-digit growth?"
@GoogleDeepMind

2:08

Alex Imas Verified account @alexolegimas Oct 29

I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.

How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.

We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).

We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.

Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.

We are really excited about this paper. Excited to hear people’s thoughts.

Link: institute.deepmind.com/essays/the-eco…

View quoted post on X