Original Title: Venice ($VVV): The Bubble's Mirror
Original Author: nikshep
Translation: Peggy

Editor's Note: VVV's recent market performance has thrust Venice into the forefront of the AI x Crypto narrative. The CoinMarketCap page shows that the Venice Token's latest price is around $17.28, with a 24-hour price increase of about 19%, and a circulating market cap of around $795 million; CoinGecko shows a price increase of over 60% in the past 7 days, with a market cap of approximately $694 million. This collectively points to one fact: the market is refocusing on this "privacy AI + tokenomics" project.

However, what this article truly discusses is not VVV's short-term price surge, but a more fundamental issue: as model capabilities rapidly commodify, where will the value of AI platforms ultimately settle?

The author's core argument is that cutting-edge AI labs like OpenAI and Anthropic are falling into an "equity structure trap": their valuation is built on the premise of models maintaining long-term scarcity and high premiums, but Chinese open-source models, low-cost training, open weight ecosystems, and cloud deployment are rapidly driving down the price of model capabilities themselves. In other words, the most expensive part of the AI industry may be turning into the most challenging part to sustain profitability.

Within this framework, the author views Venice as a reverse structure: it does not train models but harnesses open-source model capabilities; it does not rely on centralized data retention but emphasizes privacy and TEE proof; it does not turn users into training data but through mechanisms like VVV staking, subscription destruction, DIEM computational power equity, etc., it makes users part of the platform economy. What the author truly wants to convey is that Venice is not a "tokenized AI application" but an experiment in tokenizing consumer-software relationships.

The most noteworthy aspect is not whether Venice can directly challenge OpenAI, but whether the AI market is splitting into two parts: one continuing to serve customers willing to pay for cutting-edge models, accept enterprise-level compliance, and data retention; the other turning towards "good enough" open-source model capabilities and placing more emphasis on privacy, censorship resistance, low cost, native access to intelligent agents, and user ownership. If this split occurs, Venice's opportunity lies not in winning the entire model war but in becoming the inference layer and settlement track in the open intelligent agent economy.

Therefore, this article is a typical structural multi-pronged argument: it is not just a bet on VVV's price rise but a bet that the lines of model-layer commercialization, open-source model catch-up, intelligent agent payments rise, and user ownership economy will all converge simultaneously.

The risk lies precisely here—if the open-source model stalls, token burning cannot sustainably match growth, or Venice fails to solidify user relationships, this narrative will be reevaluated. However, at least at this current stage, VVV's market performance has already indicated that the market is willing to pay a higher premium for this "same demand, opposite economic model" story.

The original text:

These labs are pouring in hundreds of billions of dollars, attempting to defend a moat that is evaporating in real time. GLM-5.1 has outperformed GPT-5.4 in the most challenging programming benchmarks—it is open-source, licensed under the MIT License, and trained on Chinese hardware that the U.S. is trying to block. The cost of training cutting-edge capabilities has dropped by approximately 95% within eighteen months. Every dollar in OpenAI's $852 billion valuation is built on one assumption: these changes do not matter. But they do. And Venice is the only consumer-grade AI platform: when everything finally has to be repriced by the market, its economic structure will directly benefit; even if such repricing never occurs, its investment thesis still holds.

The core argument of the April article is that Venice holds a unique position in the smart agent economy. This assessment still holds—usage has tripled, ledger burns have exceeded 42% of genesis supply, DIEM repriced 75% in six weeks, and the token price has more than doubled compared to when I wrote that in-depth analysis.

But the "Seven Key Advantages" framework I presented in April may have underestimated what is happening. Venice is not an AI company with a privacy label that incidentally issued a token. It is a new economic structure for consumer-facing software: users are owners, the platform is the track, and value is not priced in equity but in computational equity.

This structure is not a stack of functionalities but a configuration that can survive the imminent changes at the model layer. Whatever the bubble is built on, Venice stands on the opposite side of it. The same market, the same demand, a completely opposite economic model. This is the mirror.

This is the argument I did not make clear in April. Clarifying now.

Equity Structure Trap

OpenAI, Anthropic, and Together AI share a commonality unrelated to their products: their investors expect equity returns in dollars, in the range of hundreds of billions of dollars, and require them to be achieved on an accelerated timeline post-valuation adjustment.

It all sounds mundane until you extrapolate this logic further.

With OpenAI's $852 billion valuation, by 2030, it would need to achieve approximately $200 billion to $280 billion in annual revenue to justify this valuation multiple. The company currently brings in $2 billion in monthly revenue, incurred a $13.5 billion loss in the first half of 2025; meanwhile, as the cost of inference has skyrocketed fourfold to $8.4 billion, its adjusted gross margin has dropped from 40% to 33%. Compute and talent costs consume 75% of total revenue. Microsoft is also set to extract another 20% by 2032. OpenAI anticipates that by 2028, its compute expenses will reach $121 billion, with a loss of $85 billion just in that year, and profitability may only be possible after 2030.

Anthropic falls into a similar trap, albeit on a different scale. With a $380 billion valuation, a $300 billion ARR run rate, and projected training costs of $42 billion by 2029. Google pledged $40 billion last month, and Amazon injected another $25 billion—both essentially recycling cloud service quotas rather than genuine equity capital. The top five hyperscale cloud providers have committed between $660 billion and $690 billion for AI infrastructure just in 2026. Goldman Sachs projects cumulative spending from 2025 to 2027 to reach $1.4 trillion, about three times the expenditure from 2022 to 2024. Sam Altman personally secured a $1 trillion AI deal, while OpenAI only brings in $13 billion in revenue.

These are not ordinary enterprises. They are sovereign-level infrastructure bets disguised as software companies. Their valuation demands the model layer must remain prohibitively expensive. Yet the reality is that the model layer is becoming increasingly affordable.

Decoupling

Over the past 60 days, the relationship between AI capital expenditure and AI capabilities has been decoupled. The release of three open weights models illustrates this.

Released on April 7th by Z.ai, GLM-5.1 scored 58.4 on SWE-Bench Pro, surpassing GPT-5.4 at 57.7 and Claude Opus 4.6 at 57.3. It is open-sourced under the MIT license, trained entirely on Huawei Ascend chips, without utilizing any NVIDIA hardware; and Z.ai itself is on the U.S. Entity List, prohibited from accessing the H100. Its API is priced at $1 per million token input, $3.2 per output, making it 5 to 8 times cheaper than Claude Opus' $5 / $25.

The Kimi K2.6 released by Moonshot on April 20 became the top-ranked open-weight model on the Artificial Analysis Intelligence Index with a score of 54, surpassing the score of 57 from the Frontiers Closed Labs. It outperformed GPT-5.4: HLE-with-tools, which scored 54.0, higher than GPT-5.4's 52.1. It scored 80.2 on the SWE-Bench Verified, almost matching Claude Opus's 80.8. Cloudflare priced it at $0.95 for input and $4 for output, making it around 15 times cheaper than Claude Opus under heavy load. The initial training cost of the Kimi K2 was only $4.6 million.

The DeepSeek V4-Pro, released on April 24, ranked second on the Intelligence Index, just behind Kimi K2.6, surpassing all models except the top three from the Frontiers Closed Labs. It is released under the MIT license. The training cost of DeepSeek V3 was $5.6 million.

Three Chinese labs, in 60 days, all open-source, all achieving or surpassing state-of-the-art on at least one major benchmark, priced 5 to 15 times cheaper, with one running on sanctioned hardware. The kind of capability that is projected to support OpenAI's valuation in 2024 is now freely downloadable on Hugging Face, deployable on rented hardware, and continuously improving every quarter.

This is not the so-called "Chinese AI Moment." This is a real-time arbitrage at the model layer. An academic paper in March 2026 directly stated: "Pre-training scale is now decoupled from frontier AI capabilities." The share of global usage of Chinese open-source models has grown from 1.2% in 2025 to 30%. Apple is evaluating the use of DeepSeek, Qwen, and Doubao for iOS 27. AWS, Azure, and Google Cloud all offer DeepSeek deployment. Nowadays, 80% of startups seeking VC funding are building on open-source models. Meta's Llama series is intentionally released to drive model layer commoditization—when a $16 trillion company is the staunchest price-cutter in your market, it signals where the profit margins are heading.

For every dollar of OpenAI's $852 billion valuation, it assumes these changes are irrelevant. It assumes enterprise customers will indefinitely pay a high-priced token for high-end capabilities, even though GLM-5.1 could offer similar capabilities at one-eighth of the price; it assumes the open-weight of Kimi K2.6 is not crucial; it assumes DeepSeek selling for less than 3% of the price of a top model doesn't matter. It assumes these labs can achieve a tenfold revenue growth and expand margins in a market where competitors offer products for free.

Sapphire Ventures' Jai Das referred to OpenAI as the "Netscape of the AI era." Mark Zuckerberg also publicly acknowledged the existence of the AI bubble dynamics. In March, the Pentagon officially flagged Anthropic as a supply chain risk because Anthropic refused to allow Claude to be used for large-scale surveillance and autonomous weapons; whereas OpenAI and Google signed an "all lawful use" agreement to avoid a similar fate. Centralized AI companies are susceptible to government coercion, and their architecture cannot resist such coercion. Venice's architecture can.

These labs are not oblivious to the problem. They just can't pivot. Those investors who wrote the check with a valuation of $852 billion did not buy into a future where a model would be commoditized. They bought into a future where a model would always carry a high premium. These are two entirely different companies, and the latter must write down the valuation of the former if it is to truly materialize.

That's the trap. The issue is not the refusal stack or the logging architecture. The real problem is that the only investors who can stomach an economic structure like Venice happen to be those who already hold VVV.

Not One Market, But Two Markets

From here on out, this argument no longer needs a bubble burst to hold.

Assume these labs barely make it through. Assume GPT-6 still holds the lead in its class, Claude Opus 5 continues to lead in reasoning, Gemini remains at the forefront of multimodal capabilities. Assume enterprise contracts can last long enough for these companies to complete their refinancing and weather their valuation pressures.

It still won't matter. The market will split.

Frontier intelligence only represents a small portion of total reasoning demand. The vast majority of real workloads—programming assistance, writing, analysis, image generation, video, agent execution, customer service, research, summarization—reached a "good enough" level months ago. In production environments, the encoding capability of GLM-5.1 is already on par with GPT-5.4. Kimi K2.6's ability to run agents is already comparable to Claude Opus 4.6. DeepSeek's general reasoning ability is also essentially on par with any model outside the absolute top tier. For 80% of real needs, the open-weight ecosystem is already sufficient and getting better every quarter.

These demands require not stronger intelligence but intelligence attributes that these labs cannot provide structurally: privacy, censorship-resistant output, accountless operation, loglessness, native agent access, predictable costs, user ownership. The labs serve a small fraction willing to pay enterprise prices and accept monitoring for high-end demands. Venice serves everyone else, which happens to be the larger, faster-growing half of the market.

The bull market scenario is: these labs collapse, and Venice takes over the entire market. The baseline scenario is: the market splits, with Venice having the larger share. Even in a bear market scenario—where these labs continue to dominate long-term capabilities without any repricing event—Venice remains one of the few consumer AI platforms capable of serving the 80% inferencing needs: needs that don't require cutting-edge capabilities and can't afford the lab's business model.

This argument doesn't require a meltdown. It only requires the open-source curve to continue in the direction it's already taken.

Why is Venice capturing the larger half of the market? Not because it's destined to win it all. It might, but the structural answer is simpler.

Venice is the only consumer AI platform that allows users to own the track they run on. Stake VVV, earn rewards and lifetime Pro access. Lock sVVV, mint DIEM, own a perpetual compute stake that appreciates as inference costs commoditize. Every paying user drives a burn flywheel, compounding the position of all other users. This isn't a feature; it's an entirely different consumer-product relationship—one that Big AI can't provide because their equity structure can't accommodate "user as owner."

Look again at what users truly need that labs can't provide. Privacy isn't a policy; it's verifiable TEE proofs, no footprint, and an architecture where nothing can be seized. For the 99% of intelligent use cases that don't need to pass through an enterprise brand safety council, unreviewable outputs are critical. Open-source cutting-edge models go live within days of release because Venice doesn't need to defend a moat that keeps the model layer persistently expensive. Agent-native access—self-serve API keys, x402 wallet payments, zero human touch—because the agents being deployed today can't use anything else.

Each of these forces is independently reinforcing. As data breaches rise, regulations tighten, the need for privacy grows. As users grow disillusioned with "brand-safe AI products" that routinely balk at everyday tasks, the demand for anti-censorship grows. Open-source steadily narrows the gap of "good enough" each quarter. Agents are doubling their share of total inference demands. None of these forces point to the labs. They all point to Venice.

Mirror

A platform built on the inverse of every bubble assumption, many of its attributes look accidental until you see the whole form.

Zero Training Cost. Venice has never spent a dollar to train a model. Every release from Llama, Qwen, Mistral, GLM, DeepSeek, Kimi has been a free upgrade. While labs have spent hundreds of billions of dollars trying to maintain a lead measured in "months," Venice's cost is zero, riding directly up the paid-up-curve of development. When GLM-5.1 was released at one-eighth the price of Claude, it was a margin expansion event for Venice; but for companies trying to charge a premium for equivalent capability, it was an existential threat.

Zero Retained Liability. In the lab, privacy is a policy promise; in Venice, privacy is a mathematical structure. The OpenAI Enterprise Edition, by default, does not use customer data to train models, and customers can also set retention windows, but during inference, prompts still flow through OpenAI's servers and may be accessed by authorized personnel for abuse investigations, support, and legal matters. Policies can change. Vendors can also be breached - in November 2025, Mixpanel leaked API customer names, emails, and organization IDs through SMS phishing. Runtime data can also be leaked through novel vulnerabilities - Check Point disclosed a ChatGPT vulnerability in March that silently leaked conversation content through a DNS side channel. Even if contracts specify zero retention, their architecture is still trust-based. Venice's TEE proof turns privacy guarantees into cryptographic guarantees. Secure Enclave processes prompts, returns results, proves execution, and then discards inputs. Venice cannot see your data because its architecture does not allow it to. This is not a privacy moat but a legal balance sheet that grows stronger as data regulation tightens.

Token Appreciation Mechanically Bound to Usage. Every payment request buys VVV on the open market and burns it. Layered subscription burns will expand with revenue growth: Pro around $2, Pro+ around $5, Max around $10. Emissions have been reduced five times in the past 18 months and are scheduled to halve again by midsummer. 42% of the Genesis supply has already been burned. There is no allocation towards investor returns because there are no investors at all. Every dollar earned is compounded back into the assets held by the stakers.

Users are a Class of Asset, Not a Product. This is a point that no one really makes clear. On centralized platforms, users generate data, data becomes training input, and training input becomes the platform's moat. Users are the product. Whereas on Venice, users consume tokens through staking, subscriptions, and payment of inference fees, which are burned, thereby enhancing the value of every holder's position. Users are the asset. The economic vector is entirely opposite to almost all other consumer software businesses on the planet.

DIEM is an inference-backed fixed income instrument. 1 staked DIEM = a $1 daily auto-renewing limit, permanently valid. It can be traded on Aerodrome and unlocked from original sVVV staking via burning. During the lockup period, it also earns approximately 80% of the yield of regular VVV staking. It's not a regular token but a fixed income instrument backed by AI infrastructure. As the underlying compute power becomes commoditized, each DIEM can purchase more inference power per year while maintaining the nominal claim. The Lab issues equity based on a depreciating asset; Venice issues perpetual equity on an asset appreciating against itself.

Put all this together, and you don't get a "crypto-flavored AI company". You get an entirely different consumer software form: every economic relationship between the user and the platform is intermediated by assets that users themselves own, price, trade, and earn returns from. And these attributes hold true regardless of whether those Labs survive. They are not trades bet on collapse but rather structurally advantageous systems that compound in any macro environment.

Why Now

The era of the smart agent economy is arriving, coinciding perfectly with these Labs running out of funding runway.

The transaction volume on Coinbase Agentic Wallets on x402 has exceeded 165 million. Google AP2 has launched with over 60 partners. Visa has released the Trusted Agent Protocol. Mastercard acquired stablecoin infrastructure for $1.8 billion— the largest stablecoin transaction ever. Coinbase launched Agent.market in April with 69,000 active smart agents trading on it. McKinsey forecasts that by 2030, consumer commerce mediated by smart agents will reach $3-5 trillion.

Every one of these smart agents requires an inference service provider. But they can't use OpenAI or Anthropic in a serious setting. Lab's compliance architecture demands KYC; their revenue model requires logging; their content policies demand rejection. Smart agents can't fill out registration forms, enter CVVs, or agree to terms of service that may change next quarter. Coinbase's CEO puts it bluntly: AI smart agents can't meet KYC requirements and can't use traditional banking systems.

So, as the core business of these Labs is being arbitraged by unsupervised models rising from China, the most critical new demand category in AI infrastructure—autonomous smart agents—is structurally incompatible with their architectures. Smart agents exacerbate market divides: high-end demand remains top-tier, and everything else will move towards smart agent nativity.

Venice serves both ends of this transaction simultaneously. The self-sovereign API key flow is now live—a smart staking VVV, token signing, key minting, DIEM payment, all hands-off. x402 wallet payments are live on all paywalls. A single credential gives access to 11-chain JSON-RPCs. Each Eliza, Fleek, OpenClaw, Hermes, and NanoClaw smart entity is plug-and-play. The reason the smart entities being deployed today run on Venice rails is that there is no other option that is permissionless, private, censorship-resistant, and natively supports smart entities.

As the smart intermediary's business scale hits the tens of trillions of dollars McKinsey predicts, while those labs bump into the equity structures built-in wall—whether they actually do or not—Venice has become the reasoning layer of this economy.

Compounding Something

The April argument is no longer speculative. On April 7th, daily volume hit 500 billion tokens and 1 million images. GLM-5.1, Kimi K2.6, and DeepSeek V4 all landed in Venice within days of release, with privacy contracts intact. DIEM's execution discount has repriced from 57% at the beginning of March to now around 32%—the repricing of the market is about reliability, not new utility. As long as the discount drops below 20%, DIEM will mechanically cross $1500. Staking inflows have surpassed $15 million. Over 32 million VVV tokens are staked, locking up about 70% of the circulating supply. The tiered subscription burn mechanism launched in April, and is producing significant monthly burns; projected forward at the current rate, even without the next emission cut, VVV will flip to net deflation by Q3.

Every judgment made in that April piece has either compounded or become clearer. None have weakened.

The April piece argued that Venice was the only platform combining seven specific advantages. That judgment still holds. But what I didn't make clear then is why: these seven advantages are not a stack of features but a consumer software company shaping itself. What the VCs bought into is equity based on an asset about to be commercialized.

There are two evolutionary paths for this market. The labs will either buckle under their equity structures, and Venice will take over the entire stack. Or the market will fork—labs holding onto that tiny slice of high-end demand willing to pay enterprise prices and accept surveillance, while Venice owns everything else: the bigger, faster growing half of the market where "good enough" smart blends with privacy, censorship-resistant outputs, smart entity native access, and user ownership.

The endpoint of both paths is for Venice to become the reasoning layer of the Open Intelligence Economy. This thesis does not require a bubble burst. It only requires the open-source curve to continue evolving in the direction it has been going—something it has been doing every quarter, at a pace faster than the market can update its models.

Venice is built on this bet. Three months ago, I made this assertion at $2, and no one listened. A month ago, as the price reached $8, some started to take notice. Now, with the price at $18, the market still hasn't fully grasped this structural thesis—the yet-to-be-priced part is what happens when the two scenarios ultimately converge to the same answer.

A bubble is based on the assumption of the model layer maintaining a high premium. Venice's compounding is based on the trend of the model layer moving towards free. Whether the bubble suddenly bursts or slowly deflates, the endpoint of this transaction is the same.

Same market. Opposite economic model.

The labs can't keep up. The compute providers can't capture the users. The protocols are being handed over to the foundation. Value will, as always, concentrate in a few places: the brands people choose, the tracks on which intelligences run, and the currencies they use to price things.

Venice is building the brand, operating the tracks, and issuing the currency.

The next chapter is not a celebration. The real question is: Will the structural thesis presented in the April article be repriced as venture-backed comparables gradually run out of road, or will it be repriced as the market naturally cleaves around them?

From the current evidence, both are happening as scheduled.

Not investment advice. Please do your own research.

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