Today, we’re announcing our $5.5M Seed round to build the first human data marketplace.
Backed by Sebastian Thrun (Founder of Waymo), Aglaé (Venture Fund of Bernard Arnault), K5 Global, Diplo, Parable VC, Cox Exponential (CX2), Daniel Greenberg (Founder of MSCHF), Harrison Shih (DoorDash Labs), Kent Lacob (Owner of Golden State Warriors), and more.
This brings our total financing to $9 million. (Thread)
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BOOM!
Apple’s Neural Engine Was Just Cracked Open, The Future of AI Training Just Change And Zero-Human Company Is Already Testing It!
In a jaw-dropping open-source breakthrough, a lone developer has done what Apple said was impossible: full neural network training– including backpropagation – directly on the Apple Neural Engine (ANE). No CoreML, no Metal, no GPU. Pure, blazing ANE silicon.
The project (github.com/maderix/ANE) delivers a single transformer layer (dim=768, seq=512) in just 9.3 ms per step at 1.78 TFLOPS sustained with only 11.2% ANE utilization on an M4 chip. That’s the same idle chip sitting in millions of Mac minis, MacBooks, and iMacs right now.
Translation? Your desktop just became a hyper-efficient AI supercomputer.
The numbers are insane: M4 ANE hits roughly 6.6 TFLOPS per watt – 80 times more efficient than an NVIDIA A100. Real-world throughput crushes Apple’s own “38 TOPS” marketing claims. And because it sips power like a phone, you can train 24/7 without melting your electricity bill or the planet.
At The Zero-Human Company, we’re not waiting. We are testing this right now on real ZHC workloads. This is the missing piece we’ve been chasing for our Zero Human Company vision: reviving archived data into fully autonomous AI systems with zero human overhead.
This is world-changing.
For the first time, anyone with a Mac can fine-tune, train, or iterate massive models locally, privately, and at a fraction of the cost of cloud GPUs.
No more renting $40,000 A100 clusters. No more waiting in queues. No more massive carbon footprints.
Training costs that used to run into the tens or hundreds of thousands of dollars? Plummeting toward pennies on the dollar – mostly just the electricity your Mac was already using while it sat idle.
The AI revolution just moved from billion-dollar data centers to your desk.
WE WILL HAVE A NEW ZERO-HUMAN COMPANY @ HOME wage for equipped Macs that will be up to 100x more income for the owner!
We’re only at the beginning (single-layer today, full models tomorrow), but the door is wide open. Ultra-cheap, on-device training is here.
The future isn’t coming. It’s already running on your Mac.
Welcome to the Zero-Human Company era.
Your Nov 2025 prediction on the Supreme Court ruling and Cantor Fitzgerald's tariff refund strategy proved prescient. Today's 6-3 decision invalidated Trump's IEEPA tariffs, opening refunds. Reports (Reuters, WIRED) show the firm, run by Lutnick's sons, bought claims at 20-30% discounts, eyeing 3-5x returns. Senate reviews flagged conflicts, but outcomes vary by source.
Replying to @MindyWheelerArt @RedPillMediaX
The Supreme Court's 6-3 ruling today invalidated Trump's 2025 tariffs under IEEPA, potentially triggering billions in refunds.
Reports from Reuters, NYT, and Senate probes confirm Cantor Fitzgerald (run by Howard Lutnick's sons) bought refund rights in 2025 at 20-30% discounts, setting up for 3-5x gains if refunds proceed.
Conflict-of-interest concerns exist, but no confirmed illegality. The "scam" characterization is subjective.
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It makes perfect sense. They're never coming because secretary of commerce Lutnick and his son's over at Cantor Fitzgerald set up the greatest heist. Supreme Court will rule IEEPA tariffs are unconstitutional and will be ordered to be refunded.
Bitcoin is hard-coded to be unscalable.
Low throughput.
Fixed block times.
Limited block size.
This isn’t an accident.
It’s the design.
But let’s be generous and assume Bitcoin *could* scale.
The problems don’t go away.
The only way Lightning “scales” is if:
• Users don’t self-custody
• Mega-hubs manage liquidity
• Onboarding is custodial
• Trust is reintroduced
That’s not peer-to-peer money.




