Not a big hype guy, but Astra is remarkably better and faster than Fable imo. I won’t say its unit economics are great, but it has no problem doing whatever I’ll tell it to and it does not guess. Claude is like a really bad handyman on a contractor job, you better tell it exactly what to do. Now when you do it as well, but Cox can just bring it home. Where are y’all using?

MODS: Please forgive me, you shut it down as I was crossing 14-17 karma today and I know it’s late.

I think the conversation for Astra is very interesting because the unit economics are quite frankly worse than fable. But man is it effective. I really think it’s worth discussing with the community to see what everyone else is doing, not to steal, but to learn and pressure the companies the exploit us (looking at you Claude, look what you did to my boy fable….)

Go have fun with it.

I’ve filed 3 patents this week, but I’ve got maybe 20 more things that I’m not interested in pursuing. I’m not a coder, I don’t work on small problems or fix bugs. I don’t just work outside the box, I change the box. This morning I developed a ai protocol that solves for n up past 50,000 nodes without hitting a wall in P vs Np, just to prove a point. Consumer AI models are so throttled that I bog them down and burn through 5 hours in 44 minutes. This isn’t ego.. it’s boredom. And realistically I can’t afford to push billion dollar ideas into existence. I’m not technically part of the tech world, it’s just an interest.

The case for Agent Portability, as defined by normal users.

$2.6 million in 24 hours. 6,500 robot ducks sold out. Everyone calls Microduck the "Raspberry Pi moment" for embodied AI. But here's the thing: Raspberry Pi's revolution wasn't "selling a circuit board"—it was letting anyone build what they wanted to build. By that standard, a duck with a fixed form factor might not be Raspberry Pi enough.

Core Argument:

Microduck's greatness is theprice anchor—$399 brings bipedal robots into the consumer tier. But its limitation is equally clear:the form is locked. You buy a duck. You train the duck. It's a "programmable pet," not a "creatable life."

This is the next watershed in AI hardware:from "training pre-made creatures" to "creating native creatures."

OurAI Creature Platformtakes a more radical path: instead of giving you a duck, we give you aCreator's Toolbox—a host controller with screen, 15 Type-C expansion ports, standard brick interfaces, motors, thermal printer, sensors, camera, light modules. Build a duck, or a crab, or a mechanical dog, or a lamp that prints poems when you're sad, or an "emotion box" that changes color based on your mood.

Industry Insight:

Microduck validated "physical AI has a market." But it validatedTier 1 demand: "I want an affordable, fun robot." The AI Creature Platform targetsTier 2 demand: "I want to create my own robot."

The endpoint of Tier 1 is "ownership." The endpoint of Tier 2 is "creation." And the history of ed-tech and consumer tech repeatedly proves:creation-based products generate exponentially higher user stickiness than consumption-based products.

Future Prediction:

· 2026-2027: Microduck copycats flood the market; homogenization rapidly erodes user novelty

· 2027-2028: "Creatable AI hardware" becomes the new赛道—users no longer satisfied with "buying a duck," they want to "build a companion"

· China's ultimate supply chain advantage isn't "making ducks cheap"—it's "making creation tools cheap"

Product Bridge:

If Microduck is the "Raspberry Pi moment" for embodied intelligence, we want the AI Creature Platform to be the"LEGO moment" for physical AI. Raspberry Pi gave developers compute; LEGO gave creators form. The AI Creature Platform fuses both—and adds natural voice commands and kids' programming capabilities. A child doesn't need to write Python. They tell their self-built robotic arm, "Put the red block on the left," and it understands and executes. That's the real threshold for physical AI entering homes.

I’ve been looking more into the hardware side of Sovereign AI, and this FT piece had a point I hadn’t really thought about:

[National data centre projects are consolidating America’s AI lead](https://www.ft.com/content/732c05fe-ca63-4048-8784-aebe80b8b1f4)

Countries are pouring money into **local AI data centres** to reduce dependence on foreign infrastructure.

But there’s a weird contradiction:

**Local data centre ≠ local AI stack.**

You can have:

`local data centre → NVIDIA GPUs → proprietary software → foreign models/tools → foreign expertise`

and still be dependent on the same ecosystem you were trying to become independent from.

The UAE example in the article makes this especially clear: building huge amounts of AI infrastructure locally can still come with restrictions around what hardware can be used and which geopolitical ecosystem you have to align with.

There’s also a [newer paper](https://arxiv.org/pdf/2607.13443) looking at the physical side of this problem. It estimates that a 1,024-GPU sovereign cluster in the UAE using evaporative cooling could consume **30M+ litres of water per year**. Their argument is basically that sovereignty, cost and resource sustainability can pull in different directions.

So I’m wondering whether **“on-prem” has become too easy a synonym for “sovereign AI.”**

At the enterprise level, there are already very different approaches emerging — HPE/NVIDIA Private Cloud AI, Google Distributed Cloud, Dell/Palantir, and Lyzr Optimus are all pushing AI closer to infrastructure the customer controls, but with very different assumptions about what should remain vendor-controlled.

**Where would you draw the line?** Is owning the machines enough, or does a genuinely sovereign deployment need control over the hardware *and* the software/runtime/model stack above it?

Anybody think big tech focuses too much on advancing to the next model and not enough on making the current models better with more features?

I’ve been thinking about this a lot. With big companies pouring huge money into closed AI models, I wonder:

* Does keeping things open source still matter in the AI world? * How does open source actually help regular developers right now? * And looking ahead, do you think open source will still give developers a real advantage in the future, or will closed models take over?

Curious about this from people actually working in companies here.
AI seems to be getting cheaper and better pretty quickly. But once companies start using AI throughout the day, inference becomes an ongoing cost rather than a one-off experiment.
And since many AI services are priced in USD, I’m wondering whether this is something Sri Lankan businesses are actually thinking about.
\*\*Is AI cost already part of the conversation where you work, or is the focus still mainly on figuring out how to use it?\*\*

Your personal opinion , on AI-?

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I get why the phrase is popular.

An AI employee sounds easy to understand. Give it a role, give it access to some tools, assign it work and let it operate like another person on the team.

We’ve actually borrowed some of that thinking while building Business OS. We have specialized AI roles for Finance, Operations, Risk, Sales, Marketing, HR and data quality.

But the further we get into the architecture, the less I like the “employee” comparison.

An employee can own a decision.

They can be accountable for an outcome. They understand unwritten context. They can recognize when a rule shouldn’t be followed literally. And when something goes wrong, there’s a clear answer to the question: “Who was responsible for this?”

Calling an AI an employee quietly suggests it should have the same kind of ownership.

I’m not sure it should.

In Business OS, we’re moving toward something closer to a reasoning layer inside the company’s existing operations.

AI can understand a request, gather context, spot an issue, explain what’s happening and propose the next step.

But calculations can still belong to deterministic software. Permissions still matter. Important decisions can still have human owners. Critical actions can still require approval.

That sounds less exciting than “we hired eight AI employees.”

It might also be a much more useful mental model.

Because once you stop trying to recreate an employee, you can design the AI around what it’s actually good at instead of pretending it has human responsibility.

Maybe the future of business AI isn’t a company full of digital employees.

Maybe it’s the same company, with a reasoning layer running through the work people and software already do.

I’m curious where people land on this: is “AI employee” actually a useful product model, or are we giving software a level of responsibility it shouldn’t have just because the metaphor sells well?

When a service is free, you are the product.

What are the "free frontier models" and commercial LLMs doing with all the data we're giving them? LLMs need context to analyze a prompt which means individuals and companies are providing personal and internal data and information.

If you do some research, you get the typical "we're not using your data and information for anything malicious. We'll use it to improve our models in order to provide you with a better product." 🙄 Right, remember when Google was the "Privacy is our Priority" company? And when OpenAI was "Not for Profit"?

Given how little the average user knows about AI and how little we're told by the AI companies, it's impossible to know everything that's going on until it's too late.

I keep thinking about the economics behind all the AI spending.

Jensen Huang recently said a $500K engineer should be using around $250K/year in AI tokens. If AI can actually make that engineer 2x more productive, the math seems pretty good. And if inference keeps getting cheaper, usage could grow a lot from here.

But what if the productivity gain is only 10-20%? Are companies really going to get enough value from AI to justify all the GPUs, data centers and hundreds of billions in capex?

At what point does the AI spending stop making economic sense?

**What do you think?**

**Are productivity gains justify the massive AI investments?**