Last week at the @YumaGroup Summit I had the opportunity to present on The State of Bittensor. That presentation is in the thread below. If you choose to read it, I'd ask that you keep the following three things in mind:
This is just one guy's view of what was the most relevant for a 25-minute talk; a difficult filter for such a dynamic industry.
The slides were designed to supplement a talk; I've done my best to replicate what I recall of the talk in the accompanying X posts.
The topic of the Summit was "The Tipping Point" - a candid assessment of what could lead to Bittensor's breakout success and what evidence we see of that today - which also thematically anchored this presentation.
Let's dive in:
We are in the most important race in human history – the race for intelligence itself. AI has advanced beyond the point of no return. As an example of what I mean: Ramp is a widely used financial services platform for companies. They looked at spending and revenue across their clients since the launch of ChatGPT: Companies who did not spend on AI have had flat revenue for the last three years. The top quartile of AI spenders have grown revenue by more than 100%.
We are already at the point where investing in AI is a matter of survival. But what exactly are we getting for the hundreds of billions being spent? Right now, its overwhelmingly going to corporations who have repeatedly shown they don’t have our best interest in mind.
Claude Opus 4.6 – the leading deep thinking model, had a measured hallucination rate of 16% in February. Then, without telling anyone, Anthropic throttled its reasoning – presumably to reduce GPU utilization – and didn’t tell anyone. Hallucinations climbed to 33% - a 98% increase.
They only admitted it after third party benchmarking proved it. And they were still charging everyone at the same price the whole time. Even since my talk last week, they've supposedly been found to be throttling people simply because HERMES.md was in their commits. You may say, "well there are solid open source options..."
Yes, open source models have gotten very good, but they’re not immune to capture either. Try asking DeepSeek what happened in Tiananmen Square and then let me know if that’s the intelligence you want to trust.
This needs to be addressed right now or it will be too late. To give you a sense of what I mean, this is a chart of the total annual commits on GitHub. That’s 500% growth since the launch of ChatGPT in 2022. From 200M per year to a one billion in 2025. 2026 is on track for **14 billion** The genie is out of the bottle – there is no going back; we are already at the exponential inflection point.
This reminds me of many years ago: Bitcoin shined a light on how much our rights were impacted when we became dependent on private companies to run our day-to-day lives.
Your right to privacy? That doesn’t extend to your bank account. Your "money" is just a ledger at a private company, available for interrogation and suspension at any time. Bitcoin gave us back the sovereignty of our wealth.
Similarly, we’ve depended on things like privacy of our medical records and attorney client privilege for our entire lives. What do you think is going to happen when a private company’s servers are giving you legal and medical advice? Who are you going to trust for that intelligence? The company that lobotomized its top model? The model constrained by the foreign governments? As I said at the beginning, we’re in the most important race in human history and Bittensor well may be our best shot at winning.
One of the things about having a different model to produce intelligence is it requires an economic system suited to it. Subnets are the intelligence and economic engines that drive Bittensor’s value. That’s why the Summit was themed around The Tipping Point: understanding how subnets can reach breakout success and what we can do to help.
To summarize Bittensor's intelligence economics: miners create intelligence for which they earn subnet tokens. In many cases they sell those tokens to fund operations, putting downward pressure on token prices and decreasing the incentive to mine (similar to bitcoin). In parallel, if that intelligence is being used to generate real world value, one of the parties who benefits from that value (e.g. the Operator monetizing it, institutions using intelligence commodities to advance their research, etc.) can buy the subnet tokens to keep token prices elevated and sustain the miner incentive.
Investors get to participate in this process, often supporting token prices before the commercial value of intelligence is realized, and/or subsequently holding an asset that parties gaining fundamental value from the intelligence (eg Operator or others) will need to purchase at some point in the future if they want to maintain sufficient incentives for the intelligence machine to continue running.
For Bittensor to succeed, this value loop has to work. So, to understand the State of Bittensor, we have to take a look at how that’s going today and what that means for the network overall.
One of the many unique features of Bittensor is that subnets are native to the protocol. That is not the case on most crypto networks where the true utility lives in smart contracts with no direct tie to network value.
As an example, Polymarket has seen 800% growth in volume this year. Users can bet any arbitrarily large amount of value on Polymarket for a few cents of network fees. There is nothing tying that to value of the network’s native token, which is down 80% over the same period as Polymarket’s amazing success.
Conversely, Bittensor subnets are intrinsically linked to $TAO. If you want $1,000 worth of subnet exposure, you first need $1,000 of TAO. We analyzed subnet pool data surrounding the announcement of @tplr_ai's recent training run and normalized across them by indexing them to a starting level of 100.
As shown by the orange line, there was no material change in pool size for non-Templar subnets over the observation period. There was however, major inflow into Templar’s pool. Given Bittensor’s unique network model, we saw a direct correlation to the change in TAO price over the same period. As value flows into subnets, the whole network benefits. A rising boat lifts the tide, so to speak.
That can go both ways. When Sam left, we saw something similar in reverse; as value was exfiltrated from the network, it started in Covenant subnets and dragged TAO down with it. You know what else we saw in the data though? For all of the noise about concerns of Bittensor’s future, the other subnet pools were mostly unchanged.
The event was interesting because it reminded me of the early days of bitcoin: people would say Bitcoin was only used by drug dealers on the internet. I'd stare at them aghast because in the same breath they told me that an open, permissionless network was used to reliably move money anywhere in the world in minutes by the most untrustworthy people on the planet and yet they didn't understand how the technical feat required to achieve that would create tremendous value.
The Covenant situation is similar: people were concerned about the operator's exit, rather than realizing the only reason we care is because a ground-breaking technical innovation was achieved. But even bigger than that: Bittensor has 128 subnets currently, each striving to generate value for themselves and, transitively, the network as well.
And we’re seeing that occur – Templar was not unique in that regard. The same pattern emerged around the Intel publication on @TargonCompute. The non-Targon pools remained largely unchanged. Targon saw heavy inflows. TAO price climbed with it.
Again: rising boats lift the tide. And there are many boats in Bittensor right now.
We’re seeing major technical innovations at an increasing rate.
Just a few examples from the last couple weeks:
@QuasarModels just announced a custom attention architecture targeting 5M token context windows.
@IOTA_SN9 developed a technique that compresses data flowing between distributed GPUs by 128x with little to no loss in training quality, increasing viability of training large AI models across internet-connected machines worldwide.
We're seeing the building blocks start to form whereby competitive large generalized models can eventually be built. In the meantime, we're also witnessing more targeted, niche players start to pull ahead in their respective fields.
During the presentation, I gave the example of @resilabsai achieving 90% accuracy on their home valuation model, making it the most performant open source model and quickly approaching state of the art. Quite literally as I was explaining this during the talk, @markjeffrey pointed out they had just achieved 98% accuracy.
In the time between when I prepared the presentation and actually presented, they went from best open source to at or near state of the art - only further highlighting the unique value of Bittensor's open, competitive intelligence creation cycle.
And the tech that’s being built on Bittensor is getting real attention from serious players. Again, just a few examples of many: Harvard partnered with @Chutes on research about AI inference efficiency. Valeo – an auto company with $20B in annual revenue – is working with @natix on an AI model for self-driving cars. @zeussubnet- the weather forecasting subnet, is the only party in the world allowed to use data WeatherXM’s network of global weather sensors for commercial purposes. And there are in fact many subnets already commercializing their intelligence.
Most of us are already aware of Chutes seven-figure ARR, but a few other examples:
@LeadpoetAI– which uses their Bittensor subnet to source sales leads, announced earlier this year that they crossed $1M ARR
@Bitcast_network– the content creation platform built on their subnet competition – is already operating profitably
@lium_io– a hardware subnet – has bought more than 4,000 TAO worth of their token
Remember the economic model I outlined earlier; we’re seeing real evidence that it’s starting to work across many subnets. Intelligence built on Bittensor, capturing value in the real economy, and bringing it back into the network.
That’s why when we look at Bittensor we like to look at Total Network Value (TNV);
$TAO market cap is only part of the story in Bittensor. TNV = market cap of TAO + market cap of subnets – tao in the pools [as not to double count] The actual value of this network is already higher than most people realize. And notably, subnets make up an increasing proportion of TNV – recently crossing 35% - as value continues to flow into the pools.
Interestingly, we recently noticed a change in TNV: In particular, despite all the volatility in TAO, the dramatic subnet issuance curves, etc. - the combined subnet market cap had been remarkably consistent around $750 million for most of the last year, until recently.
It’s nearly doubled over the last few months – a clear breakout in the trend. If you were looking for Tipping Point, it might look something like this...
I hear a lot that that value is relatively concentrated in the largest subnets. And the market cap distribution does indeed reflect that, but that’s not necessarily a bad thing.
This is the market cap distribution of the S&P 500. Many healthy economic systems tend towards Pareto distributions. And so what if some subnets are worth more? As we showed earlier, this is an ecosystem that will win or lose *together* And we’re seeing that play out every day.
We track announcements of subnets utilizing each others infrastructure and intelligence. Just as an example, we identified at least eight subnets who announced that they use Chutes for inference. But we have dozens of similar examples of cross-subnet collaboration across many subnets like
1. Collaboration seems to be happening at an increasing pace as subnets continue to mature and build out contiguous pipelines of AI infrastructure
2. Keeping money circulating within an economy creates a money multiplier. Capital circulating within a single economy without leaving creates economic value for each party it passes through, without having to bring in new capital. That’s uniquely possible here because of the diversity of infrastructure built on Bittensor.
This network is not 128 discrete growth drivers; it’s increasingly functioning as an interconnected graph, which has substantially more stickiness and value And the pace is about to increase dramatically:
We’re starting to see increasing agents operating on Bittensor: subnets mined by agents, subnets operated by agents...
Consider the Bittensor value flywheel:
-An intelligence goal is established
-Miners compete to achieve the goal
-That produces intelligence
-Intelligence generates value
That’s happening today, as we’ve seen earlier in this discussion.
As agents get more capable, that flywheel spins faster and faster. Permissionless entry means any agent can compete. Protocol-native economic incentives mean good work gets rewarded. Bittensor is uniquely advantaged for agentic speed over guarded, centralized alternatives with corporate procurement cycles.
That also means exploits will be found faster. But, it also means solutions that harden the network against them will be found faster as well.
Accordingly the impact of the network primitives – incentives, accessibility, governance, security, reliability, and all the infrastructure we’re building around the network - have an exponentially larger impact. It is critical that we get these right. The time to nail this, is right now. If we don’t someone else will.
The good news is, for now, Bittensor seems to be in the lead The 30-day moving average of Daily active wallets just crossed a record, approaching 10,000 Up 100% just in the last year.
We’re also seeing subnet ownership increasingly diversify and distribute. The median number of holders of subnet tokens at 2,000 is a 10x increase since the dtao launch a year ago. And at Yuma, we spend a lot of effort and resources to help broaden that access.
Yuma currently partners with 16 custodian and wallet providers to bring Bittensor access to the masses As an institutional-grade validator, the relationships and service we offer give them the confidence to make TAO staking available to millions of end users.
During the Summit, we announced that BitGo’s clients will now have access to subnet token staking through our partnership, making subnet investing available to customers of one of the world’s largest custodians.
We also help people gain access to subnets via investment vehicles. The Yuma Composite Fund gives investors access to a market-cap weighted portfolio of subnets through traditional investment structures. The Yuma Large Cap Fund gives investors concentrated exposure to Bittensor's largest subnets.
Our institutional asset management team handles everything from initial subnet token purchases, to portfolio rebalancing, custody, and reporting. The appeal for institutions is obvious, but even for the Bittensor native, it’s an amazingly simple way to get access to a broadly diversified portfolio, rebalanced regularly.
Between the breakout performance of subnets, the attractive staking rewards, and benefits of diversification, the Yuma funds have outperformed TAO materially year to date [as of when the presentation was created] Nearly 3x outperformance relative to TAO.
And last but definitely not least, our subnet accelerator has helped a wide range of companies access Bittensor. We help them acquire subnet slots, design incentives, provide marketing assistance, review pitch decks, make introductions to other investors, etc. At Yuma we deeply believe in the power of subnets and have helped many of the network's leading intelligence providers start and succeed.
Disclaimer: For informational purposes only. Nothing herein should be construed as financial, investment, legal, or tax advice. This material does not constitute an offer to sell or a solicitation of an offer to buy any securities or tokens. Investing in digital assets involves significant risk, including the potential loss of principal. Subnet tokens do not represent equity or ownership interests in any entity. Performance comparisons and index references are illustrative only and not indicative of future results. Charts and indices are based on methodologies and assumptions that may change and may not reflect actual market conditions or liquidity.
🚨 BREAKING: The Final Clarity Act Bill Text is Official! 🇺🇸🔥
After more than a year of back-and-forth, the final draft is here—incorporating 126 last-minute amendments requested by Democrats just 24 hours before the vote. 🤯
Key updates in the final text:
Strict Ethics Oversight: Expanded restrictions now cover federal officials, judges, and spouses, with Senator Lummis noting Trump opted in voluntarily.
Banking Safeguards: Treasury gains authority to step in if high-yield stablecoins start draining liquidity from community banks.
Builder Protections: Civil safe harbor provisions have been strengthened to explicitly cover crypto miners and network validators.
Market Integrity: Added guardrails target conflicts of interest and affiliate trading while leaving state consumer protection laws intact.
Does it have enough momentum to secure 60 votes tomorrow? 👀
RFK Jr: "The Pandemics are coming from labs. ALL OF THEM... Lyme, COVID, RSV, HIV & Spanish Flu came out of a vaccine lab." ☠️ 💉
"Gain-of-Function Vaccine research has created the worst plagues in our history."
"We can go down the whole list of diseases... It’s just a disaster. It’s given us no benefits. It’s given us everything from Lyme disease to Covid, and many many other diseases. RSV, which is now one of the biggest killers of children, came out of a vaccine lab."
"There’s strong evidence that even Spanish flu came from vaccine research."
"There’s plenty of evidence that HIV also came from a vaccine gain-of-function lab program. "
"The 'PANDEMICS' are coming from labs... ALL OF THEM."
🚀The industry has gotten incredible at teaching robots
🚀The industry has gotten incredible at teaching robots to move, sprint, and imitate body dynamics. But as Michael Parker (@bittensormax) points out in The UMI Thesis, there’s still a massive missing piece in Physical AI: Motion Understanding.
✨ Key Takeaways:
🔹Looking Human vs. Understanding Humans: Robots can execute impressive physical feats, but they still struggle to reliably read non-verbal human cues in context.
🔹Motion is Meaning: A gesture, hesitation, or glance changes completely depending on posture, timing, and surrounding context.
🔹Beyond Pixels: True intelligence requires mapping human intent and sequence across time—not just processing raw frames.
🔹The UMI Intelligence Layer: As robots enter hospitals, factories, homes, and stores, Bittensor’s SN78 @umi_sn78 UMI (Universal Motion Intelligence) aims to own the critical layer that translates human movement into real meaning.
The future of robotics isn't just about how machines move—it's about how ...
🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨
Chutes is gaining attention as a decentralized AI inference platform that claims to combine real usage, cryptographic verification, confidential computing, and open-source infrastructure into a working production system. The thesis is simple: instead of trusting Big Tech clouds with AI workloads, users get a distributed compute layer built around verification and privacy.
🔑 Key points
🔹 Chutes is live in production and reportedly scaled to more than 1,170 active GPU nodes, including large numbers of Nvidia H200s and Blackwell-class hardware.
🔹 The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.
🔹 The team reportedly cut unprofitable usage programs, reduced total token volume, and still improved revenue efficiency, with revenue per GPU rising sharply after removing subsidized traffic.
🔹 Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...
🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨
🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨
🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨
A new clash is emerging between legacy finance and crypto legislation after JPMorgan CEO Jamie Dimon reportedly warned that the CLARITY Act could let crypto firms offer bank-like products without bank-level oversight. The dispute is quickly turning into a larger fight over regulation, competitiveness, and who controls the future architecture of digital finance in the United States.
🔑 Key points
🔹 Jamie Dimon reportedly called the CLARITY Act a threat to the financial system, arguing it could allow crypto firms to offer yield-like products while avoiding the capital, reserve, and oversight burdens traditional banks face.
🔹 Senator Cynthia Lummis pushed back publicly, framing the issue as a global strategic race and warning that if the U.S. does not set digital asset standards, other powers will.
🔹 The core tension is whether the bill creates legitimate regulatory clarity or simply opens the door to regulatory arbitrage for crypto platforms operating outside the traditional banking...
👉 Coinbase just launched an AI agent for Crypto Trading
Custom AI assistants that print money in your sleep? 🔜
The future of Crypto x AI is about to go crazy.
👉 Here’s what you need to know:
💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit
👉 What this means for the future of Crypto:
1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto 👉txns done by AI agents by 2025
🚨 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.
👉 Coinbase just launched an AI agent for Crypto Trading
👉 Coinbase just launched an AI agent for Crypto Trading
⚖️ CLARITY Act ethics fight centers on Trump-linked crypto concerns ahead of September 15 vote ⚖️
Senate Democrats have demanded major changes to the CLARITY Act’s crypto-ethics provisions before supporting Tuesday’s cloture vote. Republicans say the latest text includes 126 Democratic-requested changes, but seven Democratic votes are still needed.
🔑 Key points
🔹 State AG enforcement: Democrats want state attorneys general to prosecute violations alongside the DOJ if federal officials refuse to act.
🔹 Family coverage must expand: The current language covers officials and spouses. Democrats want it extended to children and other family members involved in crypto ventures.
🔹 Ethics rules should be permanent: The current provisions sunset on January 20, 2029. Democrats want the restrictions to continue beyond the current presidential term.
🔹 Existing holdings must be divested: Democrats oppose allowing officials to place significant crypto interests in a blind trust instead of selling ...
🛒 How to buy Bittensor subnet tokens using TaoStats 🛒
TaoStats provides a simplified way to acquire Bittensor subnet tokens by connecting a wallet, selecting a subnet, and swapping TAO for the corresponding alpha token.
🔑 Key points
🔹 Connect a compatible wallet: Users begin by linking a Bittensor-supported wallet containing TAO.
🔹 Choose a subnet: TaoStats displays available subnet information, including alpha prices, market capitalization, liquidity, and emissions.
🔹 Review the market first: Users should check liquidity, trading volume, price movement, circulating supply, and recent emissions before swapping.
🔹 Enter the TAO amount: The platform calculates the estimated amount of subnet alpha the user will receive.
🔹 Slippage matters: Thin liquidity can cause the final execution price to differ significantly from the quoted price.
🔹 Confirm the transaction: Users review the exchange rate, network fee, price impact, and receiving wallet before signing.
📐 Conjecture (SN66) reportedly breaks a 66-year-old Erdős geometry problem 📐
Conjecture (SN66) has reportedly produced a breakthrough on a long-standing geometry problem associated with Paul Erdős, using distributed mathematical search and verification.
🔑 Key points
🔹 Six decades of open work: The problem had remained unresolved for approximately 66 years.
🔹 AI-assisted discovery: Conjecture uses computational systems to explore mathematical structures, test cases, and search for potential solutions.
🔹 Distributed contributors: Miners can compete to generate candidate constructions, proofs, counterexamples, or reductions.
🔹 Verification is essential: A proposed result must be checked through formal reasoning or independently reproducible computation.
🔹 “Breaks” requires clarification: The claim could mean a complete proof, a counterexample to the original conjecture, or a major reduction of the problem.
🔹 Search is not proof: Finding a pattern or candidate solution is only the ...
Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.
Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.
The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.
Revolut claims that derived biometric face data was not.
The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.
The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.
Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.
Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.
Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.
The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.
The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – either in response to valid legal requests, or, as in the case of Revolut, fake ones.
One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons.
This means that even if you close your account, your identity documents don’t disappear.
And while the incident with Revolut happened in the financial sector, it’s by no means the only one that requires customers to hand over sensitive identity information. Discord, a popular chat service, said in an October 9, 2025 security update that government ID photos of approximately 70,000 users may have been exposed after a third-party customer service provider got hacked.
This was not a financial service, nor the same type of attack. But the result was similar – because the underlying business process was the same: requiring and storing sensitive identity documents. In the case of Discord, these were used to review age-related appeals.
It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss.
The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.
This Is The Income A Family Needs To Live Comfortably In Every US State
Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:
In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.
So… how much does a family of four need in your state?
This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.
The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.
And asVisual Capitalistnotes, Massachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.
Hawaii follows at $313,165, while California ranks third at $302,682.
Rank State Income needed for family of four (2026)
1 - Massachusetts - $329,555
2 - Hawaii - $313,165
3 - California - $302,682
4 - Connecticut - $298,189
5 - New Jersey - $295,110
6 - New York - $291,533
7 - Colorado - $283,213
8 - Washington - $281,798
9 - Oregon - $280,966
10 - Vermont - $280,384
11 - Alaska - $272,064
12 - New Hampshire - $267,904
13 - Rhode Island - $264,659
14 - Minnesota - $263,078
15 - Maryland - $257,837
16 - Maine - $250,931
17 - Montana - $249,434
18 - Pennsylvania - $247,936
19 - Illinois - $244,109
20 - Virginia - $242,944
21 - Nevada - $242,278
22 - Indiana - $241,696
23 - Wisconsin - $238,451
24 - Arizona - $236,870
25 - Utah - $235,789
26 - Delaware - $228,134
27 - Ohio - $226,221
28 - Idaho - $226,054
29 - Florida - $223,392
30 - New Mexico - $223,142
31 - Nebraska - $223,059
32 - Missouri - $217,734
33 - Georgia - $214,573
34 - Michigan - $214,323
35 - South Carolina - $212,909
36 - North Carolina - $212,410
37 - Wyoming - $212,410
38 - Oklahoma - $211,910
39 - North Dakota - $210,496
40 - Kansas - $207,917
41 - Iowa - $204,422
42 - Texas - $203,424
43 - West Virginia - $202,592
44 - South Dakota - $201,760
45 - Alabama - $198,931
46 - Louisiana - $197,933
47 - Tennessee - $197,267
48 - Arkansas - $195,437
49 - Kentucky - $194,854
50 - Mississippi - $187,533
Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.
Colorado and Vermont Make the Top 10
As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.
However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.
Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.
Just Six States Come in Below $200,000
Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.
Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.
The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.
🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
Now that AI is moving into the physical world, many are asking a bigger question:
Will these same companies end up controlling robotics too?
It's a valid concern.
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
That movement is decentralized AI.
Why Decentralized AI Exists
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
Why This Matters for Robotics
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
This is where decentralized systems become interesting.
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
Imagine a future where:
Warehouse robots contribute operational data.
Delivery robots contribute navigation data.
Factory robots contribute manipulation data.
Developers contribute models.
Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
That vision is beginning to emerge.
Bittensor's Move Toward Physical AI
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
In other words, the pieces are starting to appear.
Not a decentralized robot network yet.
But the infrastructure that could support one.
Beyond Bittensor: The Rise of Physical AI Networks
Bittensor isn't alone.
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
The goal is not simply decentralization for its own sake.
The goal is resilience.
If one server fails, the system continues.
If one company disappears, the network survives.
If one participant leaves, innovation continues.
But Here's the Reality
Decentralized AI faces the same challenge every decentralized technology faces.
Big Tech has resources. A lot of resources.
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
The challenge isn't just decentralizing intelligence.
It's decentralizing intelligence while maintaining performance.
That's much harder.
The Most Likely Outcome
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
The companies building robots may use NVIDIA hardware.
Train on Azure.
Run foundation models from OpenAI.
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
The Bigger Question
The real question isn't whether decentralized AI can eliminate Big Tech.
It can't.
At least not anytime soon.
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
Because the battle for the future of robotics is no longer about hardware.
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