After eTAO actually went live, subnets can finally decide how their own emissions are split. Before, the whole network followed one fixed ratio. Now they can at least adjust it based on their own needs. Seeing some subnets already experiment with different allocations, it feels like the network is moving toward greater autonomy. That kind of change matters more to me than just waiting for another feature update. bittensor:native

Elon’s one sentence just sentenced 200+ neoclouds to death. TAO’s window may be opening. Yesterday Elon replied to Chamath and cut straight to the brutal reality of today’s AI infrastructure: Debt markets have almost completely shut off funding for AI companies that lack substantial independent revenue. SpaceX could raise $40B because it is investment-grade and has real cash flow from Starlink and launches. The remaining 200+ neoclouds are now facing “guppy-level” death and consolidation. What does this mean? The pure GPU-cloud plays that were built over the past two years by burning cash, taking on debt, and relying on GPU financing are being crushed by the rising cost of capital. Many data centers will become partially completed, underutilized “stranded assets.” Hyperscalers will prioritize their own internal needs and push third-party model companies onto their own routers, taking a cut. In this environment, betting on “just build an even bigger centralized GPU farm” has become dramatically harder. Bittensor ($TAO) is taking a different path: • It does not rely on massive debt-fueled expansion. It uses token incentives to make developers around the world compete to deliver the best models and compute. • Its subnet mechanism breaks AI tasks into smaller pieces, allowing the best algorithms to emerge organically instead of depending on one company spending tens of billions to build data centers. • As centralized neoclouds go through large-scale consolidation and capital is re-priced, a decentralized, capital-light, open-competition network has a chance to become an outlet for the compute and developers that get squeezed out. This does not mean TAO is about to moon. It still has to prove: 1. That models produced on its subnets are something people are actually willing to pay for (real demand, not just emissions). 2. That the open network can absorb some of the high-quality compute and algorithms when centralized giants reduce third-party supply. 3.That the token economics can still sustain sufficient participation after the halving. On a macro level, however, Elon’s comment draws a clear line under the “pure debt-driven AI infrastructure bubble.” On the other side of that line are models that can keep running without continuous large-scale financing. TAO is now more like a waiting game: after this cleanup finishes, how much of the displaced developer talent, compute, and demand will flow toward open networks? If 2027 really becomes the year of stranded compute and consolidation, that is not bad news for decentralized AI.

After watching bittensor:native for a long time, the biggest feeling isn’t that the mechanism is complicated. What used to be discussed most was “how to understand it and how to participate.” Now the clearer thing is the stratification among subnets: Some are seriously building products and finding real paying customers, gradually generating external revenue. Others are still purely sustained by emissions, and once competition intensifies, they struggle to continue. This divergence is becoming increasingly obvious. The market is voting with its feet — and faster than many people realize.

Replit just shifted over $100M a year in AI spend from Anthropic to OpenAI — mainly because of a 30%+ price discount. Frontier models have moved from competing on capability to competing on price. But temporary discounts don’t solve the long-term cost problem. Bittensor’s inference subnets are built as competitive markets that continuously drive costs down through real competition, and some already have paying external customers. This is exactly the problem Bittensor is designed to solve — using competition to keep driving inference costs lower. $TAO #Bittensor

Moonshot just released Kimi K3.1 Pro. A 3T-class MoE model with 1M context. It scores close to Claude Opus 5.5 on Terminal-Bench while being cheaper. More importantly, they deliberately reduced its hacking capabilities and still plan to open the weights on November 4. This kind of near-frontier open model is exactly what Bittensor subnets are built for. Once the weights drop, miners worldwide can immediately deploy, fine-tune, accelerate, and compete on agent and coding use cases. Centralized labs are becoming more cautious and closed. Open weights keep moving forward. bittensor:native is currently the only network that can scale this with real incentives. #Bittensor #TAO

While Claude Duet and other proactive agents are rolling out, banks are already racing to block unauthorized third-party agents. Capabilities are accelerating, but so is the risk that models and agents get adversarially manipulated or bypassed. @perturbaix (SN26) on Bittensor is doing the opposite of waiting for problems to appear: miners continuously find inputs that fool models, then use those attacks to harden the models themselves. Turning attack and defense into an ongoing competition.

Microsoft’s AI diagnostic system (MAI-DxO) clearly outperformed general physicians on complex cases — that’s worth paying attention to. It doesn’t just spit out an answer. It works step by step, like a panel of doctors: asking questions, deciding which tests to order, and updating its judgment based on the results. But these results are still from controlled text-based cases, not real patients. Real deployment has to deal with data sources, verification standards, accountability, and privacy — not just another centralized lab announcing it’s better. If Bittensor can produce verifiable, competitive results in medical-related subnets instead of relying on a single company’s narrative, that would be more interesting. A lot of the big lab “breakthroughs” in medicine right now are still basically “we tested it internally.” Open competition at least offers another way to check the claims. $TAO

Most subnets talk about building useful AI. @perturbaix is actually doing it — running a live network where miners get paid to attack models and then make them harder to break. That’s the kind of real work the TAO ecosystem needs more of. Not more narratives. More pressure testing.

The real next stage of AI isn’t just building a better chatbot. It’s whether AI can actually connect to the real world. For the past few years, most intelligence has stayed inside centralized labs and closed systems. No matter how strong the models get, they still face the same problem: continuous feedback from the real world is hard for a few companies to monopolize long-term. Once AI needs to perceive, act, fail, and learn again in real environments, centralized approaches hit clear limits — slow data collection, narrow feedback loops, and rigid incentives. This is where decentralization becomes more concrete. It’s not about building the bodies ourselves. It’s about enabling real-world data, task feedback, and model iteration to be organized more openly across a network. Whoever can more efficiently connect real-world actions to model improvement will have a chance to claim a position in the next stage. Chatting is just the starting point. Being able to act is the next stage. The demands this places on decentralized networks will be much higher than they are today. bittensor:native #TAO #Bittensor #DecentralizedAI

Will TAO’s development consider landing in manufacturing? Right now the focus is still on general inference and compute, and manufacturing isn’t the main narrative. But factories are where AI is most likely to generate real paying demand — quality inspection, production scheduling, predictive maintenance. These use cases can actually be measured in dollars. @const_reborn how do you see this path? Bittensor won’t centrally decide the direction, but specialized industrial subnets can absolutely emerge. The hard parts are data privacy and edge deployment. Whoever first delivers real paying factory use cases will prove the model works. Manufacturing cares about results on the floor, not concepts. bittensor:native

My current view on bittensor:native is that it is going through a fairly typical mechanism transition. There is real progress, but also unresolved issues from earlier stages, which is why the overall picture feels messy right now. External customer revenue has crossed $50M annualized and is growing at a decent pace. This is one of the more concrete positive signals we have seen, because it shows that some subnets are building things paying users actually want, rather than just circulating emissions internally. At the same time, a number of subnets extracted meaningful value through emissions over the past few years without leaving behind a comparable amount of product or recurring revenue. That mismatch does not disappear simply because the revenue number improved, and it continues to affect how the market evaluates many subnets. eTAO makes emission splits more flexible, which is directionally reasonable since different subnets have different business models. In the short term, however, that flexibility can also be used for surface-level adjustments — whether that means lowering the owner cut to signal support for builders or raising it to capture more rewards — without a corresponding increase in product progress or revenue. My judgment is that the biggest uncertainty for TAO is not whether revenue exists, but how many subnets can turn that revenue into consistent product delivery and buybacks instead of continuing to rely primarily on emissions and narrative. If the number of subnets with verifiable external revenue and ongoing product progress increases meaningfully over the next few months, concerns about extraction should gradually ease. If the revenue figure keeps rising while most subnets show little change in actual delivery or capital usage, the sense that problems are accumulating will likely persist. What matters more right now is not how individual subnets adjust their splits, but how many of them can provide clear evidence on both revenue and product development at the same time.

Which of TAO, RNDR, FET, and AKT is more likely to actually capture AI value? A simple comparison across three dimensions: Real customer revenue: $TAO currently leads with the strongest reported external subnet revenue (annualized above $50M and still growing). $RNDR has ongoing rendering/compute jobs that directly burn tokens, though at a smaller absolute scale. $AKT shows real cloud leasing usage, but the revenue base remains modest. $FET has the weakest publicly verifiable customer revenue data so far. Adoption & product maturity: RNDR and AKT are further along on the compute/cloud side. TAO is progressing faster on the intelligence marketplace side. FET remains more ecosystem- and narrative-driven. Token-value linkage: RNDR’s burn mechanism is the most direct. TAO links value indirectly through reward distribution plus some subnet buybacks. AKT has a BME mechanism, but limited revenue scale caps its impact. FET’s linkage is relatively weaker. Right now, $TAO leads on real AI-related revenue, while $RNDR is cleaner on “usage = burn.” All four still have relatively small revenue bases and remain dependent on emissions — sustained real revenue will determine who actually captures value. #TAO #RNDR #FET #AKT

Openτensor has launched eTAO, giving subnet owners the ability to decide how alpha emissions are distributed, instead of being locked into the fixed 18% (owner) / 41% (validators) / 41% (miners) split. According to the official announcement, owners can now adjust the allocation within certain limits: owner up to 33% (at least 18% for leased subnets), validators up to 50%, and miners at least 33%, with changes allowed roughly once every 7 days. The system also automatically adjusts the subnet’s effective TAO weight to protect root bittensor:native stakers’ returns. In my view, this effectively decentralizes part of the incentive design to subnet owners, allowing different projects to tailor their reward structures based on their specific needs rather than forcing everyone into the same template. For subnets aiming for differentiated competition, this is a meaningful step forward. That said, greater flexibility also means subnet quality will depend more on the owner’s ability to design incentives well. Done right, it could create a positive flywheel; done poorly, it might hurt long-term participation. It’ll be interesting to see how subnets actually adjust these ratios once it goes live.

The AI dividend has already partially arrived, but it is highly concentrated. NVIDIA and the hyperscalers have captured the most certain infrastructure gains. At the micro level, code output and some knowledge work have seen clear productivity improvements, yet broader macro productivity and the average person’s wallet have lagged, with most value still flowing to capital and the largest companies. Decentralized AI projects are trying to offer another path so that contributors, not just shareholders, can capture part of the value:
 $TAO (Bittensor) turns AI work into open subnet markets where miners, validators, and stakers earn rewards based on output quality, and some subnets are already generating real external customer revenue;
 $RNDR focuses on a distributed GPU marketplace serving rendering and generative workloads;
 $FET (ASI) is centered on AI agents;
 $AKT provides decentralized cloud compute resources. These are not simply trying to replace the big labs. They open up parts of the AI supply side so individuals can participate by providing compute, models, or services. Risks remain high — revenue scale, real adoption, and sustainability still need to be proven. Not financial advice, DYOR. #AIDividend #TAO #RNDR #FET #AKT #Bittensor

bittensor:native is still transitioning from an emissions-driven economy to a customer-driven one. Subnets have started generating revenue from external customers paying for compute and inference, with annualized revenue exceeding $50 million and growing relatively quickly. Emissions still account for the majority of incentives, but real customer demand is beginning to appear — a stage most AI projects haven’t reached yet. In my view, this transition matters more than the revenue number itself. Many projects either stay stuck in pure narrative or treat any fee activity as proof that they’ve already achieved product-market fit. TAO’s current state feels more like this: emissions are still supporting the system, but customer payments are starting to reach meaningful scale and accelerating. If this trend continues, the real question becomes whether subnet revenue can gradually reduce reliance on emissions and eventually create more direct demand for TAO itself. It’s still early, but the direction looks right.

From a centralized AI perspective on bittensor:native : OpenAI, Anthropic, and Google still lead clearly in frontier model quality, product maturity, and enterprise support. Bittensor is not trying to directly compete on “who builds the strongest general-purpose model.” It’s doing something different: turning AI capabilities (inference, compute, data, training, etc.) into an open, competitive market. Subnets have started generating real external revenue, but the network looks more like an infrastructure and long-tail innovation layer than a short-term replacement. Centralized AI currently leads at building the strongest models. Decentralized AI is trying to turn intelligence into a market anyone can participate in. In the near term, the two are more complementary than zero-sum.

From a TAO perspective, @alpharidge_ai is worth paying attention to. It’s not a project stuck in the roadmap stage. It’s a live product layer built on SN45: it has real paying users, a clear Pro plan at $99/month, and its news and events engine is powered by subnet miners processing real data. Compared with many subnets that only talk about concepts and haven’t shipped a product, this kind of closed loop—where people are actually paying to use it—is more meaningful for the ecosystem.

A lot of people use AI every day, but very few actually understand it. A UK survey found that almost everyone has heard of AI, yet only about 17% can explain what it is in detail. Most people can only give a vague description or can’t explain it at all. They use AI to write, research, and analyze things, but they don’t really know how it generates content, and they often overlook the fact that it can hallucinate, carry biases, and has clear limitations. bittensor:native (Bittensor) takes this a step further. Instead of letting one company train and control the models, it uses blockchain incentives to organize participants around the world. Different subnets focus on specific AI-related tasks—some handle inference, some provide compute, others work on data or predictions. Validators score these contributions, and those who perform well earn TAO rewards. TAO has a hard cap of 21 million tokens and a halving mechanism, aiming to decentralize both the production of AI and the incentives around it. The problem is that people who don’t even understand basic AI will find TAO’s subnet system, validator scoring, and dTAO mechanisms even harder to grasp. Many will simply buy in because of the “AI + crypto” narrative and treat it as just another hot token, without knowing what the subnets actually do, how rewards are distributed, or where the risks lie. Tools and concepts are spreading quickly, and more people are getting involved, but the number of people who truly understand the underlying logic hasn’t kept up. The knowledge gap isn’t shrinking—it may even be growing. This isn’t anyone’s fault. It’s just the current reality. AI has become part of daily life, and crypto projects are trying to decentralize it, but there’s still a significant distance between “using it” and “understanding it.”

Centralized AI’s path is clear: the strongest models and compute are concentrated in the hands of a few companies. They train, deploy, and price everything themselves. Ordinary people can only access it through their APIs, paying per use, with the capability ceiling, pricing, and even access itself all controlled by those companies. Bittensor is trying a different path. It breaks the act of “providing intelligence services” into competitive subnets where anyone with compute, developers, and miners from around the world can participate. Whoever provides more useful and recognized services earns emissions and real customer revenue. In the future, whether you’re an individual user, a small team, an independent developer, or a regular company, you can directly call and use AI capabilities on these subnets — without first becoming a big tech customer or getting hired by one. $TAO is the base currency of this open intelligence market. The goal isn’t to build yet another stronger closed model, but to gradually turn AI from “a product exclusive to a few companies” into “a public capability that anyone can participate in, call, and benefit from.” That is the problem Bittensor is actually trying to solve.

Bittensor subnets have always paid miners for supply. @say_gm_ (SN28) is now also paying for demand. SayGTM routes a share of Subnet 28 emissions into an on-chain pool. Affiliates compete for it by bringing real paying customers — not clicks, not signups. Your share of referred revenue = your share of that round’s alpha. Proof of Customer instead of just Proof of Work.