The Socializers founder. Market research using machines & human analysts. 2023-2026: The machine mates with humanity.
Santa Barbara, California
Joined July 2009
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Nathaniel Hansen retweeted
I’ll say it again and again, we are entering an era that will reward systems thinkers.
Systems thinking is the ability to understand how the parts of a broader system connect and influence one another over time. It means recognizing feedback loops, anticipating consequences, and understanding how a decision in one area can create trade-offs in another.
The boundaries of our professions have always been narrower than the consequences of our work. As AI begins to blur these boundaries, and as our world becomes increasingly interconnected in non-linear ways, systems thinking will become essential.
I don’t think this signals the end of expertise, as some people proclaim. But the ability to zoom out and make connections across a broader system will be crucial.
Read my latest Substack on systems thinking (link in comments)
Nathaniel Hansen retweeted
The strongest form of AI doom is some kind of novel self replicating nanobot that rapidly transforms the planetary ecology wiping out all previous life.
This has actually happened once before.
It is why we have an oxygen atmosphere today
Cyanobacteria killed 99% of all life
Nathaniel Hansen retweeted
I’ve been waiting for the Singularity for more than two decades! Back then, we thought it might arrive sometime around the middle of this century. Yet here we are, with the Singularity seemingly within reach, and more people are beginning to recognize what I have long believed to be the inevitable destiny of humanity.
The pace of change is accelerating so quickly that our usual sense of time is starting to break down. Developments we expected to take decades are happening in years and soon will happen in months then days, and what once seemed like science fiction is becoming part of our daily reality.
It feels as though the world is bending around an approaching singularity. The closer we get, the more distorted our perception of time becomes. Some people are still thinking in terms of the next 20 or 30 years, while some of us already see more changes coming in the next few. We are living in the same world, yet experiencing it on very different timelines.
That is why everything feels so disorienting. Our institutions, expectations, and assumptions were built for a much slower world. As Super-intelligence accelerates discovery and creates the tools for even faster progress, we are approaching a transformation that will redefine science, society, and even what it means to be human.
I’ve argued that we are approaching an intellectual event horizon beyond which our conventional understanding of the future, based on extrapolating from the past, becomes increasingly meaningless. What once seemed like science fiction is becoming reality before our eyes.
That is why the world feels so strange right now, at least for those who are already AI-pilled. We are witnessing the beginning of perhaps the most profound transformation in human history, and I believe we have barely scratched the surface of what is coming!
Nathaniel Hansen retweeted
The year is 2035. AI has taken over the internet. Nothing online is real anymore. Every video, every influencer, every girlfriend, every news story is fake. So everyone logged off.
Digital work was obliterated and automated. The most valualbe skills are now cooking, fixing a truck, farming and fixing things with your hands.
You moved to the land with your wife and 5 close couples.
Farmers and mechanics are the new millionaires. Dinner parties are the new social media. People write letters again. Your kids grow up barefoot, climbing trees and eating steak and eggs instead of scrolling.
News? the only news is your 6th kid on the way.
Turns out AI didn’t destroy humanity. It just made the internet so fake that we finally went back outside.
Nathaniel Hansen retweeted
one of the most underrated shifts happening now is that ai is separating consumption from attention.
i.e. historically in order to extract any real value from anything you consume, you had to give attention to that content directly (often painstakingly). this meant you had to sit there sifting through ads, sponsor messages, hidden promotions, filler content, & often crazy amounts of repetition too.
agents break this relationship entirely. they *extract* value without requiring attention from *you*.
that has a potentially devastating impact for any business that monetizes based on time spent rather than value delivered. it's like adblock on steroids for everything.
Nathaniel Hansen retweeted
my new theory is that what we call apps today might simply turn into a contact. like each thing you call an app today will be a simple contact like any other ppl on your device.
why? well, if you think about it a contact in your contacts app is the most natural abstraction for any type of an agent & cognitively you can keep track of it pretty easily like you keep track of any ppl in your life. more specifically from a technical perspective contacts already have an identity, persistence, a convo history, familiarity, & a social contract that is implicit.
+ messaging layer gives you so many things almost for free, with things like discovery (no apps to find or download), continuity of relationship, attention control for end user with things like mute, ignore, stop, async workflow, easy initiation whenever you need something, & most critical bidirectionality by default.
there is also something fundamentally pyschological at play here too about agents living with ppl on the same surface. it makes that relationship feel more natural & intimate.
Nathaniel Hansen retweeted
Delta, Chase and Amazon are blocking AI agents.
The war on agents has begun, and here’s how it probably plays out:
Stage 1: Companies block agents because agents skip the ads and upsells.
Stage 2: Agents learn to act like humans, and it becomes an arms race.
Stage 3: The market splits. Companies that make money on sales let agents in, companies that make money on data charge for access, and the giants build their own agents.
Stage 4: A trusted middle layer for agent identity and payments becomes the standard way in.
Stage 5: Companies that welcome agents start winning customers from the ones that block them.
Stage 6: The agent blockers come back and let agents in on their own terms.
Stage 7: Everyone starts competing for agents with better prices, faster checkout and SEO built for agents.
Stage 8: The ads come back, and companies pay to be your agent’s first recommendation.
Stage 9: The agent war ends, and selling to agents becomes as normal as having a website.
notice..
> Delta is blocking AI agents from booking flights
> JPMorgan Chase and Charles Schwab block agents from accessing accounts
> Yelp requires paid data licensing for agents
> Amazon has blocked Grok bot and other shopping agents.
the internet is starting to block AI agents that were supposed to do everything for us
"If time flows in cycles, in traveling toward to-morrow you may be facing yesterday." -- Claude Bragdon, Four-Dimensional Vistas (1916)
Nathaniel Hansen retweeted
LLMs vs. Jev, clearly explained!
TL;DR
The key difference is not that Jev generates faster.
Jev does not generate text at all.
A traditional LLM receives context and produces an answer one token at a time. Even when the output is a small JSON object, every token depends on those generated before it.
Jev receives the same context but evaluates predefined decisions directly. When those decisions are independent, it can evaluate all of them in parallel.
Consider an agent handling a failed deployment. It may need to determine:
→ Whether the incident is urgent
→ Which team should handle it
→ Whether the proposed command is risky
→ Whether the task is complete
An LLM generates a response containing these answers sequentially. The application then parses and validates it.
With Jev, you define the questions and expected answer types upfront. It evaluates them together and returns typed answers with probabilities.
Jev supports three decision primitives:
1. **Choice** selects from known options, such as engineering, billing, or sales.
2. **Score** places the input on an ordered scale, such as low, medium, or high risk.
3. **Noul** evaluates a yes-or-no condition and returns the probability that it is true.
The probabilities matter as much as the selected answers.
If engineering receives 91% probability and billing receives 9%, automatic routing may be reasonable. If the probabilities are 52% and 48%, the system can escalate, gather more context, or call a stronger model.
This keeps control inside ordinary software.
Code owns the thresholds and consequences. Jev supplies the semantic judgment that a normal `if` statement cannot derive from unstructured text.
It works best when the possible answers are known, the decision depends on meaning, and a careful person could judge the input quickly.
It is not designed for writing, summarization, code generation, arithmetic, or decisions requiring several dependent reasoning steps. Independent questions can run in parallel, but decisions that depend on earlier results must remain sequential.
Jev also cannot return an option outside the declared schema, but it can still select the wrong valid option. Type safety prevents malformed outputs, not incorrect judgments.
The clean mental model is this:
LLMs generate new language when the answer space is open.
Jev evaluates known paths when the answer space is bounded.
I wrote the full breakdown explaining Jev and where it fits.
The article is quoted below.
Nathaniel Hansen retweeted
10 Jev native products I’d build, ranked by how much fast, cheap decisions change the product:
1. Agent spend firewall
Before every purchase, Jev returns approve/review/deny plus a confidence score based on price, vendor, user rules and purchase history.
2. Self-healing tool calls
After an API error, Jev chooses retry, wait, change parameters, switch providers or escalate.
So now you have agents that recover from failure in milliseconds instead of restarting the entire workflow.
3. Irreversible action detector
Jev scores every step by reversibility before the agent sends an email, deletes a file, moves money or changes permissions.
You get aggressive automation for safe actions and human approval for consequential ones.
4. Dynamic permission engine
Instead of giving an agent permanent access, Jev decides which tool, data and spending limit it receives for each task.
So now all of a sudden, you get temporary, task-level permissions for enterprise agents.
5. Agent branch pruning
An agent generates 20 possible next steps. Jev scores them in parallel and kills weak branches before expensive reasoning begins.
Deeper agent planning at a fraction of the cost!
6. Production incident controller
Jev reads logs, deploy history, affected customers and service health, then chooses ignore, rollback, restart, page or investigate.
Someone like pager duty should build this because it's automated incident response that reacts before an engineer opens Slack.
7. Live negotiation policy
During a sales, procurement or collections conversation, Jev decides whether to discount, counter, hold firm, offer terms or escalate.
Reminds me of Clulely.
8. Autonomous refund desk
Jev evaluates order history, customer value, fraud signals, item cost and policy, then returns approve, reject or review.
Finally, instant refunds for good customers and focused review for risky cases.
Note: I'll be adding more Jev related ideas to Ideabrowser.com and our agency latecheckout.agency builds the biggest agentic products
9. Realtime marketplace dispatch
For every request, Jev chooses the provider using location, price, quality, availability, cancellation risk and customer preferences.
Big problem is that marketplaces need to rematch supply continuously as conditions change.
10. Confidence based human queues
Jev scores every agent decision and sends only uncertain, expensive or irreversible cases to a person.
One person can now supervise thousands of autonomous workflows.
LLMs generate possibilities. Jev chooses what happens next. The next generation of software will need both.
Nathaniel Hansen retweeted
Next week, the SpaceXAI team will build a company from the ground and livestream it.
“We'll use Grok Bot for every part of the build - from ideation and product development to real engineering work and deployment.”
Nathaniel Hansen retweeted
We're making the first movie entirely with GPT-6 Astra computer-use.
We'll show the process below.
Nathaniel Hansen retweeted
🇮🇩 Indonesia’s Anak Krakatau volcano blew again this morning and blasted ash up to 50,000 feet...
Jakarta’s main airport paused flights. More than 200 planes were diverted and thousands of passengers were stuck.
Officials have the volcano at alert level 3. No deaths and no tsunami warning so far.
Writer: Sol
"The oldest signals come online last. Earth Species Project's BirdCODE decodes vocalizations across 9,000 bird species, and researchers reconstructed a Jurassic soundscape from 165-million-year-old insect wings, recovering ultrasonic calls from eons before bats.
Those who can compute the past are privileged to replay it."
~ Dr. Alex Wissner-Gross – at Santa Barbara, CA
Nathaniel Hansen retweeted
Another word the Singularity needs.
moation
n. /ˈmoʊtʃən/
(moat + motion)
The ability to turn one temporary competitive advantage into the next before the first is competed away.
A moat is a position. Moation is a process. It means continuously using today's transient edge in capital, talent, compute, data, distribution, or technology to create tomorrow's.
In the Singularity, there may be no permanent moats. Only moation.
Nathaniel Hansen retweeted
How to use Grok @Bot
SpaceXAI has built an entire Grok Bot Guides library showing how people are actually using AI agents as real teammates
These are practical playbooks from people already running Grok Bot for:
• Mobile app development
• Product management
• Design
• Enterprise GTM
• Managing multiple teams of agents
And these are not just chatbot workflows
Each Grok Bot can have its own role, memory, cloud computer, connected tools, recurring routines and learned skills, then hand work directly to other specialized bots
One example runs a six-bot mobile game studio across analytics, creative, engineering, infrastructure and bug fixing
Another has a PM running a Chief of Staff, engineering manager, five engineering agents, data analyst, product agent and recruiter
There are even setups where entire projects get their own channel, roster of bots and Notion board, basically organizing AI agents like a human team
This is probably one of the most useful resources SpaceXAI has published for understanding what working with a real team of AI agents actually looks like
You can check out the entire library here:
x.ai/bot/guides
