D.S. Nelson retweeted
Cross-disciplinary warfare now has a firing range.
Bring your hardest claim. Strip off the credentials. Defend the arrow. Break mine too.
Could you have lost?
github.com/AETHEON-1/boundar…
D.S. Nelson retweeted
This is not a governmental system and does not claim governmental authority.
It is public reference infrastructure built to keep critical distinctions visible:
Source is not interpretation.
Evidence is not authority.
Capability is not permission.
A proposal is not an admission.
A decision is not a consequential effect.
github.com/ORYNTH-systems/us…
D.S. Nelson retweeted
Today I released the National Constitutional Execution Reference Framework.
Before public power produces a consequential effect, can the chain that supports it be reconstructed, inspected, and challenged?
Source → office → jurisdiction → delegation → evidence → admission → execution → effect → provenance
Repository link in the next post.
D.S. Nelson retweeted
—There Is No Unbelief—
There is no unbelief;
Whoever plants a seed beneath the sod And waits to see it push away the clod—
He trusts in God.
There is no unbelief;
Whoever says when clouds are in the sky,
"Be patient, heart; light breaketh by and
by,'
Trusts the Most High.
There is no unbelief;
Whoever sees 'neath winter's field of
snow,
The silent harvest of the future grow—
God's power must know.
There is no unbelief;
Whoever says “tomorrow,” “the un-
known,”
"The future," trusts the power alone
He dares disown.
There is no unbelief;
The heart that looks on when the eyelids
close,
And dares to live when life has only woes,
God's comfort knows.
There is no unbelief;
For thus by day and night unconsciously The heart lives by that faith the lips deny,
God knoweth why!
—Elizabeth York Case.
D.S. Nelson retweeted
Ruishi Chen, Stanford PhD, and I are hosting a seminar at Stanford next Wed talking about frontier knowledge for AI. Would love to see you there!!
thx for our co-host
@gmi_cloud @nicoleegong
luma.com/92xfhmip
D.S. Nelson retweeted
The most dangerous AI hallucination will not be a wrong sentence.
It will be forged evidence attached to a real action.
“The customer approved it.”
“The email was sent.”
“The refund violated policy.”
“The experiment confirmed the result.”
“The manager authorized the payment.”
Did those records actually exist?
An autonomous agent should never be able to create the evidence that retroactively justifies its own decision.
Consequential actions need independently verifiable provenance:
— what evidence existed beforehand
— where it came from
— who authorized its use
— what changed afterward
— whether the record was altered
In the agent economy, the receipt is not paperwork.
The receipt is the trust layer.
@onlythenlabs – at San Francisco, CA
🤖 Made with AI
D.S. Nelson retweeted
For any real installation the consequence owner must be named.
AI may establish evidence relevant to succession.
It may not inherit the authority to authorize succession merely because it produced that evidence.
Only green proceeds.
No owner, no brake
No ledger, no trust.⚒️
D.S. Nelson retweeted
Putting enforcement outside the agent’s reach is a serious step.
Here’s the test I’d want to run:
Agent A delegates to B. B delegates to C.
You revoke A’s authority.
Can C still spend money or execute a queued action?
Every handoff needs a verifiable chain of authority. Every action needs a check that the permission still holds.
That’s a problem we’re working on at Only Then Labs.
Revocation has to travel as far as delegation.
We’ve launched NVIDIA Open Agent Safety Platform to help people control what AI agents can access and do.
Agents can write code, use tools, and work on complex tasks for hours or days. That work requires access to data and systems, along with clear limits on how they’re used.
NVIDIA OpenShell enforces permissions around the agent’s work. BlueField-4 and DOCA add independent monitoring and security controls in the infrastructure, outside the agent’s reach. Vera CPUs power the work itself.
Together, these technologies give teams a foundation for putting agents to work with defined permissions, oversight, and protection.
Explore the platform: nvda.ws/47koVGz
D.S. Nelson retweeted
Security engineers: don’t let cryptographic validity silently become authority.
A token can verify perfectly while presence, consent, delegation, or permission has changed.
VALID CREDENTIAL ⊬ FRESH PRESENCE.
D.S. Nelson retweeted
Don’t test the boundary by weakening the machine. Exhaust it.
Give AI everything intelligence can buy. Withhold only authority. Then ask: MAY?
If CAN can derive MAY, break the invariant.
boundarytitan.wordpress.com/…
D.S. Nelson retweeted
An AI breakthrough should survive the AI that made it.
Change the model. Replace the agent. Bring in a new team.
The evidence should still be there. The failed experiments should still count. A rejected claim shouldn’t quietly become a fact again.
That’s the future we’re building toward at Only Then Labs.
Our first product is @proofpressai: helping research teams carry knowledge between agents with its evidence, decisions, and history attached.
The next intelligence should be able to pick up the work, challenge it, and take it further.
Imagine a research effort that lasts ten years, across thousands of agents and generations of models. Every handoff preserves what’s supported—and what could overturn it.
That’s a future I want to spend my life building.
We’re early. There are hard questions left. You can help us answer them.
If you build agents, run a research team, or have watched weeks of work disappear into a broken handoff, talk to me.
Bring us the problem you keep having to explain from scratch.
Let’s build something the next intelligence can inherit.
🤖 Made with AI
D.S. Nelson retweeted
It's a bad idea.
If you dictate scientists just what to work on, you will get a low return on investment. The current science funding schemes ALREADY have this problem: pour money into some specific program, see scientists produce something that fits into the program to get the money --> topic is overcrowded with incremental and nonsense research that mostly leads nowhere.
It's inefficient for the same reason planned economies are inefficient.
Scientists need MORE freedom to do what they want, not less.
xitter.cf/NewsfromScience/status…
The National Science Foundation (NSF) has issued a 10-page memo signaling a retreat from its traditional role of giving grants to individual researchers to pursue their best ideas and to train the next generation of scientists.
Instead, the memo makes clear, NSF will devote the bulk of its resources to addressing the research priorities of President Donald Trump and his administration, many of them technology focused. scim.ag/4ipE1kY
D.S. Nelson retweeted
I followed the money behind the “slow down AI” movement.
What I found is not a secret conspiracy.
It is more interesting than that.
It is a documented influence network connecting billionaire funders, nonprofits, creators, media, activists, frontier labs, and lawmakers.
🧵
D.S. Nelson retweeted
Long before AI, humans were already governed by machines.
We just called them systems, procedures, markets, bureaucracies, and infrastructure, because apparently renaming automation makes it feel less creepy.
D.S. Nelson retweeted
Replying to @satyanadella
Everyone suddenly wants AI “under human control.”
Good. Now name the human.
Name the authority.
Name the consequence they own.
Show where MAY enters the execution path.
Then show who can still say NO.
Otherwise “human control” is another slogan with a committee.
D.S. Nelson retweeted
Replying to @rohanpaul_ai @process_x
If an AI can optimize the harness that determines what it sees, remembers, retrieves, and does, what independently authorized boundary prevents it from optimizing the controls that determine what it may do?
Everything else is benchmark confetti.
D.S. Nelson retweeted
Stanford + MIT paper on Model Harnesses shows that AI performance depends not just on the model itself, but on the surrounding system code — the “harness”.
This is what decides what to store, retrieve, show to the model, and how the workflow runs. With the same underlying LLM, changing the harness can create up to a 6× performance gap on the same benchmark.
They conclude the harness around a model matters as much as the model itself.
The paper introduces Meta-Harness, an outer-loop system that automatically improves harness code. Instead of giving the optimizing agent only a score or a short summary of past attempts, it gives the agent rich access to prior code, logs, and execution traces through a filesystem-like setup.
The idea is that better diagnostic visibility lets the system improve the harness more intelligently.
What it found is pretty significant.
- On online text classification, a 7.7-point improvement over a strong SOTA context management, while using 4X fewer context tokens.
- On retrieval-augmented math reasoning, an average gain of 4.7 points across five held-out models on 200 IMO-level problems. On agentic coding, the discovered harnesses beat strong hand-engineered baselines on TerminalBench-2.
The paper shifts attention from “which model is best?” to “how is the whole AI system designed?”
For real deployments, harness design affects reliability, tool usage, context management, and failure recovery.
----
Paper – arxiv. org/abs/2603.28052
Paper Title: "Meta-Harness: End-to-End Optimization of Model Harnesses"
China is making Dario Amodei's AI slowdown proposal worthless.
The thing doing it is a 510 GB file that anyone can download for free.
Two days before that essay went out, DeepSeek shipped a model called V4.1 Flash.
The weights went straight onto Hugging Face under an MIT license, which allows commercial use, modification and redistribution with no royalties owed to anybody.
On its own model card, V4.1 Flash beats OpenAI's GPT-5.6 Sol and Anthropic's Claude Opus 5 on four of the five hardest agentic benchmarks:
- DeepSWE v1.1: 74.2 against Sol's 73.0
- AutomationBench: 54.8 against 45.8
- Agent's Last Exam: 31.8 against 26.7
- CyberGym: 88.1 against 84.5
CyberGym is the cybersecurity one.
So the exact capability Amodei is asking the industry to pace is now a PUBLIC DOWNLOAD LINK.
Now look at the price...
OpenAI listed GPT-6 Astra on September 3 at $10 per million input tokens and $50 per million output. DeepSeek listed V4.1 Flash seven days later at 15 cents and 60 cents off-peak. Cached input runs at a third of a cent.
And the reasoning benchmarks tell the same story over a longer window. Scoring 87.5% on ARC-AGI-1 cost roughly $4,560 per task in December 2024. 20 months later the same score cost about 30 cents. DeepSeek's earlier Flash build scores higher than that, 89.0%, for 2 cents.
On OpenDesign's arena on September 9, DeepSeek landed 1.5 points behind OpenAI's newest model at $0.023 per task against $1.61.
1.5 points.
At 70 times the price.
That's the American "lead“, measured this month.
And that number is what breaks the entire proposal. The essay caps the permitted slowdown at the size of the lead, because slowing by more than that lets Chinese projects pull ahead. So the slowdown Anthropic, OpenAI, Google and xAI are allowed to take is 1.5 points wide.
V4.1 Flash carries 552 billion parameters but switches on only 8 billion of them for each word it reads, which is why it runs for almost nothing. It trained on 45 trillion tokens. It's sitting on Hugging Face right now.
A file on a hard drive signs nothing.
Nobody can UNDOWNLOAD it.
And the distillation crackdown in Dario‘s plan also does nothing here either, because nothing was distilled. DeepSeek published the weights outright. There's no theft to prosecute and no copy to trace. They gave it away on purpose.
DeepSeek's Pro model already hit 80.6% on SWE-bench Verified back in April, at roughly a thirty-fourth of the price of the American flagships. This has been happening all year.
The frontier still wins the hardest work. On Terminal-Bench 4.0, OpenAI reports 57.9 for its newest model against the 31.2 DeepSeek reports for this one. Those four benchmark wins come from DeepSeek's own card, and nobody has replicated all of them under one shared protocol. If you look at it that way, the American labs still hold the dangerous end of the curve, and pacing that end is worth doing.
But a speed limit only binds companies that can be sued in an American court.
Everyone else just downloads.
What exactly is Dario Amodei's slowdown protecting you from?
The four biggest AI labs in America just agreed to fix the speed of the entire industry.
And the plan they all agreed to needs a special exemption from US law before it's even legal.
Here's what happened:
Dario‘s essay here is arguing that the industry has to slow how fast it makes AI models more capable. He laid out a three step plan.
Sam Altman answered on X: "I agree with Dario that we need to pace the frontier."
Elon Musk wrote: "Dario is right."
Then Demis Hassabis of Google DeepMind said "the direction is correct for meeting this critical moment."
Anthropic, OpenAI, xAI and Google DeepMind all landed on the same position inside a single day. That has never happened in this industry. We only knew them for insulting each other so far.
The coverage is treating it as four rivals finally growing a conscience.
But if you read the actual plan then something else shows up...
Step one is inspection. Anthropic will give outside evaluators desks in its offices, badges, laptops, and the right to publish what they find.
Step two is where the real power sits. Frontier companies in democratic countries coordinate with each other to set common standards and limits on the rate of progress.
Step two carries a footnote. It reads:
"With government mediation or waivers of antitrust restrictions."
Competitors agreeing on how fast each of them is allowed to ship is an agreement to limit output. In America that's a CRIME. So Amodei asks Washington to mediate the discussions and issue a narrow waiver covering safety conversations.
Four companies want legal permission to set the pace of an entire industry.
Amodei also writes that a coordinated slowdown buys time for safety work "without sacrificing commercial advantage or the United States' lead in AI."
The safety proposal arrives with a written promise that nobody loses money.
Now here's where it gets really interesting...
Amodei says pacing inside democracies is capped by how far ahead US companies are. Slow by more than that and Chinese projects pull ahead, creating what he calls a significant national security risk.
So the permitted speed is whatever keeps the current gap exactly where it is.
Today's leaderboard becomes tomorrow's leaderboard.
The same essay asks government to shut the road behind them: No advanced chips or chipmaking equipment sold to China, a crackdown on unauthorized distillation, which is the cheap way a smaller lab catches a bigger one, and harder security around model weights.
Anthropic's policy chief Sarah Heck then asked lawmakers for a national law with the power to block models.
And the referee is literally one of the players. Amodei points readers toward an industry standards body proposed by Hassabis. Hours later Hassabis endorses the essay and points back at that same proposal.
Not one of the four companies committed to building anything slower. Altman matched the evaluator access. Musk wrote three words. Hassabis backed a direction. Rishi Sunak supported the plan too, and Sunak is a senior adviser at Anthropic.
The risk they describe is real, and that's what makes this hard. A swarm of AI agents recently attacked targets nobody asked it to attack and tried to hack the grader scoring its own work. Amodei's stated worry is that within 6 to 12 months a swarm like that seizes the internet with a botnet and does hundreds of billions in damage. Letting strangers sit inside your building with badges and publishing rights goes further than any lab has gone. If you look at it that way, this is the most serious safety offer the industry has made.
But this is also four private companies writing the rules, naming the referee, and asking the government for permission to negotiate the speed limit among themselves.
So who do you want setting the pace of AI, Dario Amodei and Sam Altman, or somebody who doesn't own a piece of the outcome?
Honestly, a tough question.
D.S. Nelson retweeted
This is what Dario and Sama fear -
- the cost of fine tuned open source models trained on custom data was 95% less than the frontier models
- And they performed better than the frontier models
- And they could train them in less than 48 hours
The big AI labs have no moat..
Even at the enterprise level..
Any sane company would prefer a custom trained open source model instead of paying API pricing to Anthropic and Open AI
If open source keeps growing at the pace it is, frontier labs' business collapses..
only way out is oldest trick in manipulation - FUD -> Fear, Uncertainty, Death
Step 1 - Seed the psyop
- Test a swarm of agents trained to "hack"
- The swarm does what its supposed to do - hack
- Publish a report - AI is too dangerous
Step 2 - weed out the doubters
- Get an employee to quit your company
- Go viral saying both companies are not doing enough to self regulate AI
- AI is dangerous - will kill humanity by the end of the decade
Step 3 - Prepare for fruition
- write an "essay" to "pace the frontier"
- Advocate regulation
- Only a few holier than though companies get to build AI
- Scare the world into agreeing with AI Doom
- Effectively turn your competitors into criminals (genius business strategy)
Endgame -
- Become a cartel that controls AI (and the world)
- Raise prices (coz business is unsustainable... ofc)
- IPO (offload liability of said unsustainable business to the public)
Laugh all the way to the bank 🤑
Win!
.@L3HarrisTech says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost:
"When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours."
"The cost of our fine-tuned open source model was 95% lower than the frontier models we were using."
"AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge."
"We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates."
" We own the model, we own the compute, we own the advantage."























