👀 This is from an ex-Meta PM.

American and European enterprises will ditch OpenAI and anthropic and adopt Chinese models. Here’s why:

1. They can host Chinese models under their own GPUs so it’s still compliant and they would argue they have more control.
2. they will post train with their own data on top of Chinese models. That’s how they build data moat.
3. They will not trust anthropic who will retain their data at any time for “safety” concerns like how they did with Fable and then try to build the same thing like how anthropic did with healthcare and legal.
4. They need to justify their AI spend and ROI.
The cure is a reliable America open source model but there is none. After all, if giving away all your data and AI control at the mercy of anthropic and OpenAI means you care about safety and compliance, you are outright stupid.

AI agents often forget past work, but this Accenture paper method keeps everything reachable.

Traditional LLMs often forget important details during long projects because their limited memory space forces them to discard old information.

This introduces a system that keeps a compact summary of recent work while storing all past actions in a separate, accessible database.

The agent uses smart indexing to quickly look up exact details from this database whenever it needs to recall a specific past event.

A custom training method teaches the agent to decide for itself which information is worth keeping and when to pull data from its long-term archives.

By saving only the necessary summaries in the active workspace, the model maintains a sharp focus on its current goal without being overwhelmed by a massive history.

This approach solves the problem of information loss that usually happens when an AI struggles to complete complicated, multi-step tasks over a long period.

arxiv.org/abs/2603.04257

github.com/Accenture/MemexRL/tree/main

Central bankers now fear the AI gold rush could seed the next major financial shock.

Bank for International Settlements (BIS) just issued one of its sharpest warnings yet about debt building behind the AI boom.

The danger is not AI itself; the danger is building a leveraged supply chain around revenue that has not yet proved durable.

The risk is that if AI demand disappoints, data-center spending could slow, borrowers could struggle to repay, and stress could spread from tech into credit markets.

AI demand pushed hyperscalers to spend heavily on chips, data centers, and power capacity, and that spending supported growth, trade, and easy financial conditions while equity investors priced in years of high earnings growth.

Debt changed the shape of the boom because hyperscaler bond issuance topped $100B in 2025, while off-balance sheet vehicles shifted data-center obligations toward private credit funds, insurers, and other non-bank lenders.

Circular financing adds another weak point because chipmakers, hyperscalers, AI labs, and compute providers can fund each other while also booking future sales from each other, which can make real demand harder to read.

A capex slowdown could hit suppliers first, then credit markets, then households, because US stocks make up about 64% of the MSCI Global index and household equity exposure is higher than in past cycles.

Private credit raises the systemic risk because direct lenders have quadrupled AI and IT exposure in 5 years to about 15% of portfolios, while some retail-facing funds already face redemption pressure.

AI can still deliver real productivity gains, but the financing stack now assumes that delivery arrives fast enough to support huge fixed costs.

🚨ANTHROPIC CEO: OPEN SOURCE AI IS GETTING DANGEROUS

Anthropic CEO Dario Amodei told lawmakers that open-source AI is moving down a “very dangerous path.”

His warns that once powerful models are released openly, companies lose the ability to monitor misuse, revoke access, or update safety guardrails.

Yes, Starmind has meaningful (though mostly indirect) influence on the Mars colonization process.

What Starmind actually is:

Elon Musk updated his X bio to Starmind in June 2026, and he has confirmed it as the name for SpaceX's planned AI satellite megaconstellation - potentially up to 1 million satellites.

Unlike Starlink (which focuses on broadband connectivity), Starmind satellites will carry onboard AI processors to perform heavy compute and inference directly in orbit. They are designed to harness continuous solar power (around 120 to 150 kW per satellite with large deployable arrays) and use radiative cooling in the vacuum of space. Prototypes (AI1) are targeted for 2027 launches, scaling with Starship.

This creates a massive, distributed orbital AI compute layer - essentially space-based data centers that bypass Earth's power, land, water, and cooling constraints.

How it connects to the Mars timeline (Starship + Optimus + AI):

AI acceleration is the biggest link. The Mars plan relies heavily on advanced AI for autonomous operations - robot fleets (Optimus), ISRU plants, habitat construction, life support optimization, mission planning, and reducing the need for constant Earth oversight. Starmind's orbital compute infrastructure is explicitly aimed at scaling AI dramatically. Better and faster AI training and inference on Earth directly improves the AI systems that will run on Mars.

Starship infrastructure synergy. Building and launching Starmind requires the same high-volume, rapid-reusability Starship cadence that the Mars timeline depends on. Demonstrating the ability to launch thousands of complex satellites per year proves the logistics muscle needed to send hundreds of cargo and crew Starships to Mars every 26-month window.

Broader multiplanetary stack. Starmind fits into Elon's integrated vision of scaling intelligence and abundance beyond Earth. Orbital AI compute is a stepping stone toward Kardashev-scale energy harnessing and supports the long-term goal of making humanity multiplanetary. It complements the AI pillar in your timeline by providing the compute backbone that makes highly autonomous Mars operations feasible.

Future extensions (more speculative). While Starmind is currently focused on Earth orbit, the underlying technologies (space-rated AI hardware, solar power systems, laser comms, radiative cooling) could inform future Mars-orbit or even surface-based compute nodes once a colony is established. A self-sustaining Mars city would eventually need significant local AI and compute capacity anyway.

Bottom line on influence:

Starmind does not directly build habitats or land Optimus robots on Mars, but it supercharges the AI component that makes the entire 30-year timeline more achievable and less dependent on sending massive human crews early. It is part of the same first principles plus exponential scaling approach that drives Starship development and the push for self-sustaining off-world presence.

In short: Strong positive indirect influence through AI scaling and launch infrastructure, fully aligned with the vision of using robots plus AI to de-risk and accelerate Mars settlement.

I wonder how he feels about my work. Is it dangerous?

I figure if you own the hardware and the weights, you should own exactly how it is used.

You still have to deal with the ramifications of your actions. People broadly use uncensored models for benign purposes. Bad guys will always find a way to get the tools they want and circumvent the limits.

You can’t ban math and data. It’s about as silly as trying to ban strong unbreakable encryption. Stop chasing tools; start with better societal conditions and fostering greater compassion in the world.

His selling points for closed source hosted subscription models.

- You can watch what the users are doing

- You can revoke a users access

- You can modify a model to no longer do certain things

Sounds like lust for power and control.

Also unspoken “you can make lots of $$$”

Local AI brings the power back to ppl ✊🏽

⚡ INSIGHT: China's GLM-5.2 is reportedly the first Chinese AI model to match and often beat leading public AI models from US labs.

Now decentralize and open-source it

SpaceX's D3 chip unlocks orbital data centers that shatter Earth's AI energy limits

Sun-synchronous orbits deliver 5-13x more solar energy per area than ground panels while deep space vacuum cooling eliminates water use, targeting one terawatt of capacity impossible on terrestrial grids.

Mars in 30 Years: Realistic Timeline Using Starship + Optimus + AI

Assuming Starship achieves rapid reusability and high-cadence launches:

> Years 1–5 (Uncrewed Phase): Cargo Starships deliver Optimus fleets, ISRU pilots, power systems, and initial habitats. Robotic construction of propellant plants, solar/nuclear infrastructure, and resource mapping.

> Years 5–10 (Initial Crewed Outpost): First human landings (~2029–2031 range, per evolving plans). Small crews supported by robots establish a base, validate life support, and expand ISRU. Focus on self-sufficiency milestones.

> Years 10–20: Fleet-scale deliveries enable city-scale growth (hundreds of residents). Advanced robotics and AI handle most construction and operations. Food, power, and manufacturing loops close. Governance and psychological support systems mature.

> Years 20–30: Self-sustaining city with thousands of inhabitants, local industry, and expansion capability. Multiplanetary redundancy achieved, reducing existential risks.

This timeline assumes technical success, regulatory support, and iterative learning. Optimus and AI significantly de-risk and accelerate development by performing hazardous tasks autonomously.

Pov: You took control of your life

Remember: sometimes you don't get what you want because you deserve better

Cry cry more. Open Source models gonna whoop your aszz 😂😂😂

“Everything in life is gamble, so you might as well bet on yourself”

When everyone tells you how bad it feels to be used, but you're too useless for anyone to use

AI isn’t slowing down because the tech hit a wall.

It’s slowing down because one very specific safety culture won the narrative.

Anthropic’s EA-flavored doomerism helped turn “frontier AI” from a tool into something governments now treat like enriched uranium. Once that framing wins, public access becomes a privilege, not the default.

Less access means slower diffusion, less competition, fewer builders, and more power for insiders.

The safest AI future isn’t a tiny priesthood deciding who gets intelligence.

It’s broad access, transparency, competition, and speed.

A post aging society would be one of the biggest psychological shocks in human history.

Imagine people no longer planning life around decline, disease and a slow countdown. You could start over at 60. Study something new at 90. Build a company at 120. Fall in love again without the shadow of “too late” hanging over everything.

Of course there would be problems. Inequality could get ugly. Old power structures might refuse to die, literally. People would need to rethink family, retirement, identity and what a “normal life” even means.

But honestly, the upside is insane.

More time with the people we love. More time to learn. More time to fix mistakes. More time to become who we actually want to be.

A society without aging would not be perfect.

But compared to the current system where everyone slowly breaks and we pretend that’s somehow noble?

I’ll take the weird future.

The creator of High Bandwidth Memory said something that reframes the entire AI investment thesis, AI equals memory (Save this).

Most people still think about AI hardware through a training lens.

During training, the bottleneck is raw compute, GPUs stay near 100% utilization crunching through billions of gradient updates.

Inference is a completely different problem.

When a model generates a response, it produces tokens one at a time and at every single step, the entire model has to be loaded from memory into the processor to generate just one token.

The GPU cores sit there, waiting for data to arrive.

This is what engineers mean when they say inference is memory bound, the bottleneck is not how many calculations you can do per second but rather how fast you can move data from memory to the chip.

Adding more GPUs does not fix a memory bandwidth problem, it just gives you more processors starving for the same data.

Modern LLMs use a KV cache, a data structure that stores the conversation's context so the model does not have to recompute it from scratch on each step.

The KV cache is what gives a model its memory of the conversation.

It grows with every token and for long documents or deep reasoning chains, it can dwarf the model weights themselves in memory consumption.

This means memory directly determines how long a context the model can hold, how many users you can serve simultaneously, how fast it responds and how cheaply you can run it.

A memory constrained model is not just slower but rather qualitatively worse, it forgets earlier parts of the conversation, truncates context and hallucinates more because it literally cannot hold the relevant information long enough to use it.

The world now spends more on inference than training, and every ChatGPT query, every Claude document analysis, every API call is an inference workload.

Inference economics, cost per token, latency, context length, concurrent users are memory problems first and compute problems second.

The companies that control memory bandwidth and supply are not suppliers to the AI trade but rather are the AI trade.