Google is reportedly withholding an advanced internal AI model because massive inference costs make it commercially unviable
Story Overview
A social media claim suggests Google developed a frontier-level model internally yet chose not to release it, citing inference costs that would erase any profit. No primary documents, benchmarks, or company statements back the assertion, leaving the model's existence and the exact cost math unconfirmed as of June 13, 2026.
Inference costs keep pushing labs toward selective releases
Industry reports already show negative gross margins at several frontier labs when serving the largest models, with per-token improvements failing to offset volume at the highest capability tiers.
Whether this changes release timelines stays unresolved
The unverified claim is being used to argue AI takeoff will plateau, but absent any leaked specs or Google confirmation the economic limit remains a hypothesis rather than a documented decision.
Users expressed frustration and mockery toward Google's decision to withhold an advanced internal AI model over negative margins, criticizing it for blocking needed hardware and model releases.
Most Activity
This is actually how the intelligence explosion hits the top of the s curve btw
Thermodynamics is the great regularizer
There is no foom
Google has an internal Fable- or GPT-5.6-level model and there's no reason to release it because it's negative margin
Exponential is becoming an S curve overnight and we did it to ourselves.
This is actually how the intelligence explosion hits the top of the s curve btw
Thermodynamics is the great regularizer
There is no foom
Sounds like you just need to expand energy.
Supply and demand right?
Right now there is a rapidly growing demand but little supply.
If you can increase an abundance of supply then the price drops.
You just need to tip the scale in the other direction.
There will always be more and more demand, you just need to figure out how to make sure supply outpaces that demand.
In reality the cost to develop that infrastructure isn’t anymore than say any other infrastructure we have built.
Think of it like start up costs to begin a new company.
Sure it costs a lot now, and you won’t see profits right away but over time you start to become more profitable once you expand operations and increase revenue flow.
If you just heavily invest into the start up of this new infrastructure now, it will then allow everything to become more profitable later.
It allows you to expand exponentially, otherwise you’ll just keep being limited in growth potential and you’ll always have to struggle with high costs and low supply and that restricts revenue and profits.
Rip the bandaid off already, just bite the bullet and go all in here.
It’s pays off massively if you do and everything becomes easier but harder for your growth and development.
@elonmusk @sundarpichai @sama @PalmerLuckey @DarioAmodei @JeffBezos @NVIDIAAI @PalantirTech @AnthropicAI @ChatGPTapp @anduriltech @SecretaryWright @ENERGY
@zephyr_z9 Why?? it’s quite possible actually.
@beffjezos energy constraints always win
@beffjezos we need new hardware and models, fk this shit
https://github.com/Kuonirad/thermo-truth-proto
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How It Works
Proposal. Each node proposes a ConsensusState — a state vector plus a Proof-of-Work whose difficulty adapts to network entropy and estimated Byzantine activity.
Ensemble metrics. Proposals are collected into a ThermodynamicEnsemble that computes its temperature (∝ proposal variance), Shannon entropy, and Helmholtz free energy F = U − T·S.
Byzantine filtering. Outliers are removed with a Median Absolute Deviation (MAD) modified z-score — robust to contamination that would inflate a naïve mean/standard-deviation filter.
Annealing. Simulated annealing with parallel tempering (replica exchange) drives the ensemble toward minimal free energy and sub-threshold variance.
Extraction. The agreed value is the Boltzmann (energy-weighted) mean of the surviving states — proposals backed by more work weigh more.
The full engine lives in src/thermodynamic_truth/core/ (http://state.py, http://pow.py, http://annealing.py, http://protocol.py), with a gRPC transport in network/ and CLIs in cli/.
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Reproduced Byzantine run (15 nodes, 40% malicious, 5 rounds): the MAD filter removed all 6 malicious proposals every round and post-filter variance held at ~0.006 — well below the 0.05 consensus threshold.
Performance numbers depend on hardware and configuration; treat the table as indicative of the included benchmarks rather than a service-level guarantee.
@zephyr_z9 cope on the tl is off the charts rn
@beffjezos Thermo caps the curve but software bends it
@beffjezos there is only fomo