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Anthropic’s Claude Code and Computer Use Driving Revenue for the AI Pioneer

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Anthropic’s revenue has seen explosive growth in 2025–2026, heavily driven by its agentic AI offerings, particularly Claude Code, an autonomous coding agent and tool, and related features like Computer Use which allows Claude to interact with a user’s desktop, browser, files, and applications to complete tasks.

Revenue Trajectory (Annualized Run Rate/ARR)

January 2025: ~$1 billion ARR. Mid-2025: ~$4–5 billion. End of 2025: ~$9 billion. February 2026: ~$14 billion with Claude Code contributing significantly. March 2026: Surged to ~$19 billion. April 2026 recent reports: Exceeded $30 billion ARR, with some months adding billions in run-rate growth. This represents roughly 10x+ annual growth sustained over multiple years, and in some periods, multi-billion-dollar monthly increases.

Enterprise deals including via AWS Bedrock and Google Cloud make up the vast majority—often cited around 80%—with over 500 customers now spending $1M+ annually and eight of the Fortune 10 using Claude. Claude Code launched to the public around May 2025 and quickly became a major growth engine: Reached $1 billion ARR in about six months.

Hit $2.5 billion+ ARR by February 2026, more than doubling since the start of 2026. Business and enterprise subscriptions quadrupled in the early months of 2026. Enterprise usage now accounts for over half of Claude Code revenue. It has driven a notable share of overall growth; some estimates put it at ~20% or more of total revenue at certain points, with reports of 4%+ of all public GitHub commits authored by it in early 2026.

Anthropic has also expanded agentic capabilities with: Computer use and browser agents; Claude controlling screens, apps, and workflows. Agent Teams and multi-agent coordination. Products like Claude Cowork for white-collar automation. These features emphasize tool use, long context, and autonomous execution, which boost usage-based pricing and enterprise adoption over pure chat interfaces.

Perplexity’s recent 50% monthly revenue jump to ~$450M ARR; post-agent pivot with Perplexity Computer and usage-based pricing is impressive for a smaller player but remains dwarfed by Anthropic’s scale. Anthropic’s agentic push especially in coding and computer control has fueled far larger absolute and relative gains, with Claude Code alone surpassing Perplexity’s total ARR in velocity.

This aligns with industry patterns: agentic systems command premium monetization because they deliver measurable productivity e.g., 20x faster development in some case studies, massive time savings in compliance or security workflows. However, challenges remain—high compute costs can lead to negative margins on heavy usage, and scaling safely while maintaining reliability is key for Anthropic’s constitutional AI focus.

Anthropic’s trends strongly validate the agents = higher revenue thesis seen at Perplexity. The company has moved from a safety-focused research outfit to a revenue powerhouse by productizing agentic tools that enterprises will pay heavily for, particularly in software engineering and workflow automation. Growth has been so steep that some analysts project even higher run rates potentially $100B+ annualized if momentum continues.

Anthropic’s own research on real-world Claude conversations estimates that AI assistance reduces task completion time by around 80% in many cases, with software developers seeing the largest contributions to overall labor productivity about 19% of AI-attributable gains.

Internal Anthropic data shows engineers using Claude in ~60% of their work, reporting a 50% productivity boost up from 20% the prior year, including more output volume and the ability to tackle tasks that wouldn’t have been done otherwise. Pull request merge rates have increased significantly in some cases.

Developer reports and case studies often cite: 40% faster task completion in controlled evaluations of Claude-based copilots; strongest for mid-complexity scaffolding, glue code, debugging, and business logic rather than novel architecture. Up to 2x developer velocity, doubled pull request rates, or 164% increases in story completion for individual users.

In enterprises: One fintech team achieved 2x velocity and +10% test coverage; another platform saw 95–99% R&D time reduction for non-technical users building tools. Broader estimates suggest current-generation models with adoption could boost U.S. labor productivity growth by 1.0–1.8% annually over a decade, with coding as a top area.

How Blockchain Makes Online Poker Truly Provably Fair

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Poker players have always faced the same question when sitting at an online table: how do you know the cards are dealt fairly? Traditional platforms ask players to trust their software, their audits, their reputation. Blockchain removes the need for that trust by replacing it with math.

The technology allows players to verify every shuffle, every deal, every outcome after the fact. No faith required. The proof sits on a ledger that neither the platform nor the player can alter once recorded. This shifts the entire relationship between operator and player from one built on reputation to one built on cryptographic certainty.

The Core Problem with Traditional Online Poker

Standard online poker sites use random number generators to shuffle cards. Players cannot see how these generators work. They cannot confirm the output was fair. Third-party auditors may review the software, but those audits happen behind closed doors. The player receives a certificate and a promise.

This setup requires faith in multiple parties. The platform must be honest. The auditor must be competent. Neither party has an incentive to expose problems that would damage their business relationship. Players accept these conditions because they have no alternative.

How Cryptographic Hashing Changes the Game

Blockchain poker platforms use SHA-256 cryptographic hashing to create verifiable randomness. Before any cards are dealt, the platform generates a server seed and publishes a hash of that seed. Think of it as a sealed envelope containing the outcome, posted publicly before the hand begins.

Players then contribute their own seed to the process. The platform combines both seeds using SHA-256 algorithms to produce the final shuffle. After the hand ends, the platform reveals the original server seed. Players can then hash it themselves and confirm it matches what was published before the hand started.

If the hashes match, the platform could not have changed the outcome mid-game. If they altered anything, the hash would be different. The math exposes any manipulation automatically.

Hiding Cards on a Public Ledger

Blockchain ledgers are transparent by default, which creates an obvious problem for poker. Every player would see every card if the data sat openly on-chain. Mental Poker protocols address this through commutative encryption and zero-knowledge proofs, allowing hands to remain private while still being auditable after the fact.

The approach works for online poker, baccarat, blackjack, and other games where hidden information matters. Players can verify that no manipulation occurred without exposing active hands to opponents or observers during play.

Chainlink VRF and External Randomness

Some platforms use Chainlink VRF to generate random values for smart contracts. The system returns random numbers along with cryptographic proof showing exactly how those values were generated. Neither the oracle providing the randomness, nor miners processing the transaction, nor the application itself can predict or manipulate the output.

This matters because blockchain games need randomness that cannot be gamed by any party involved in the process. Chainlink VRF provides this through a seed-based system where the proof itself demonstrates the values were generated correctly.

Commit-Reveal Schemes in Multiplayer Games

For games involving multiple players, commit-reveal protocols add another layer of verification. Each participant commits to a secret value by publishing a hash of that value. During a later phase, all participants reveal their original values. The final random output comes from combining every revealed value together.

No single player can control the outcome because they cannot know what others committed. No single operator can manipulate results because player inputs are part of the equation. The randomness emerges from collective participation rather than centralized control.

What Players Can Actually Verify

After each hand, players receive the data needed to run their own verification. They can hash the server seed. They can confirm it matches the pre-game commitment. They can trace how their seed combined with the server seed to produce the shuffle.

The math works identically on any computer. Players do not need to trust the platform’s verification tool. They can use their own software or third-party verification services. The cryptographic functions are public and standardized.

Market Growth and Adoption

The global blockchain gaming market was estimated at $13.0 billion in 2024. Projections from industry analysts place it at $301.53 billion by 2030, representing a compound annual growth rate of 69.4%. These figures suggest growing confidence in blockchain-based gaming models.

Poker represents a natural fit for this technology. The game already involves hidden information, probabilistic outcomes, and player distrust of operators. Provably fair systems address each concern with verifiable solutions rather than assurances.

Practical Limitations to Consider

Verification requires some technical literacy. Players who want to confirm fairness need to understand hashing or use tools that handle it for them. Most will never run their own verification, relying instead on the fact that they could if they wanted to.

Transaction costs on some blockchains add friction. Recording every shuffle on-chain can become expensive during periods of network congestion. Some platforms address this by recording batch proofs or using cheaper layer-2 solutions.

Speed presents another challenge. Cryptographic operations and blockchain confirmations take time. Platforms must balance verification rigor against the pace players expect from an online game.

The Verification Standard Going Forward

Provably fair poker does not eliminate all risk from online play. Collusion between players remains possible. Account security still matters. But the specific question of whether the cards were dealt fairly now has a mathematical answer.

Players can point to a hash. They can run the same algorithm. They can confirm the outcome was locked before they acted. The proof exists independently of any party’s word or reputation. That represents a material improvement over systems that ask players to trust and hope.

He Can’t Code: New Yorker Exposé Reignites Questions Over Altman’s Technical Authority and OpenAI’s Power Structure

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A sweeping New Yorker investigation into OpenAI chief executive Sam Altman has reopened one of the most consequential questions in the artificial intelligence industry: whether Sam Altman, the man who has become synonymous with the AI boom, can be trusted with the extraordinary power now concentrated around OpenAI.

The report, based on more than 100 interviews and previously undisclosed internal documents, offers the most detailed account yet of the doubts that led to Altman’s dramatic ouster and rapid reinstatement in 2023.

Far from the polished public image of Altman as a visionary steward of the AI era, the investigation paints a more troubling portrait, one that goes beyond questions of personality, his inability to write code, and cuts into the governance architecture of one of the world’s most strategically important companies.

The report is largely focused on the secret memos compiled by OpenAI cofounder and former chief scientist Ilya Sutskever, which were circulated among board members in the lead-up to Altman’s removal.

One memo reportedly begins with the line: “Sam exhibits a consistent pattern of …” with the first item listed as “Lying.” That allegation is notable not simply because it concerns Altman personally, but because OpenAI’s original structure was explicitly built around trust.

Unlike conventional Silicon Valley startups, OpenAI was founded as a nonprofit with a board whose legal duty was to prioritize the safety of humanity over commercial success. The CEO, in that framework, was never meant to be just a growth executive. He was meant to be a custodian of potentially civilization-shaping technology.

That is why the report’s most important insight is not whether Altman can code.

The more consequential issue is whether institutional safeguards around him have weakened as OpenAI’s commercial and political influence has expanded. The article suggests that several insiders questioned whether Altman’s technical authority has been overstated.

Former colleagues reportedly said he lacks deep experience in programming and machine learning and has at times misused basic technical terminology in discussions.

In isolation, that may not be disqualifying because many of the most powerful technology CEOs are not the lead engineers behind their products. But in Altman’s case, the technical mythology has played an important strategic role.

He has increasingly been positioned in public discourse as an AI statesman, someone whose pronouncements on superintelligence, economic transformation, and national security carry unusual weight in Washington and global capitals.

Just days before the exposé, OpenAI released a broad industrial policy paper calling for a new economic framework for the intelligence age, including proposals on taxation, public wealth funds, and the future of work.

This juxtaposition has become the focal point because Altman is helping shape the public policy response to AI disruption, while the New Yorker investigation raises questions about whether the internal governance of his own company has kept pace with the scale of that influence.

In many ways, this story has been interpreted to be about power concentration since OpenAI is no longer merely a research lab or even a fast-growing technology company. The ChatGPTmaker is now a central actor in global infrastructure build-out, defense contracting, cloud partnerships, education tools, enterprise software, and increasingly state-level policy discussions.

The company is reportedly preparing for a public offering that could approach a $1 trillion valuation. That scale means concerns around executive accountability take on systemic importance.

The article also revisits the 2023 boardroom crisis with new details. According to the reporting, when Altman was fired, his allies among investors and senior executives quickly mobilized to reverse the decision.

One line from the report stands out: investor Josh Kushner reportedly said, “We just immediately went to war.”

That episode exposed a deeper tension within OpenAI’s structure. The nonprofit board may have had formal authority, but the economic ecosystem surrounding the company, investors, employees with large equity stakes, and strategic partners such as Microsoft, had enormous practical leverage.

In effect, the crisis suggested that governance mechanisms designed to restrain leadership could be overwhelmed by capital-market pressure. That observation makes one former researcher’s comment particularly revealing.

Carroll Wainwright told the magazine, “he sets up structures that, on paper, constrain him in the future. But then, when the future comes, and it comes time to be constrained, he does away with whatever the structure was.”

That quote captures the central criticism now hanging over Altman’s leadership style: that formal safeguards exist, but may not be durable when they conflict with strategic ambition.

As OpenAI’s models are increasingly embedded in public institutions, national security workflows, immigration systems, and enterprise operations, policymakers are expected to see the expose’ beyond corporate drama.

Community Rage Over Insider Trading Culminating to Massive Profits for Ceasefire Contracts on Polymarket 

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President Donald Trump announced a conditional ceasefire with Iran via Truth Social earlier in the week, following strain of direct US-Israeli military strikes on Iranian targets that began earlier in 2026. Iran’s Supreme National Security Council accepted a two-week pause in direct hostilities, with conditions including a halt to attacks and steps toward reopening the Strait of Hormuz for shipping.

The deal remains fragile as of April 9: Israel has continued operations in Lebanon which the US says are not covered by the truce, Iran has restricted Hormuz traffic in response, and negotiations are set to begin in Islamabad. Some markets on Polymarket now trade outcomes like ceasefire extensions or breakdowns.

The Profits on Polymarket

Polymarket saw over $170 million in volume on US-Iran ceasefire contracts, one of its largest geopolitical markets to date. Key examples of outsized gains include:One trader reportedly turned ~$13,200 into ~$477,000 roughly 3,500% return by betting on a ceasefire. Blockchain analytics firm Lookonchain flagged wallets profiting $194k, $200k+, and others, with three newly created accounts alone netting a combined ~$485k on a ceasefire by April 7 market.

These wallets had no prior activity and placed large “Yes” bets hours before Trump’s post e.g., as late as ~1:59 pm UTC on April 7, with the announcement around 10:32 pm UTC. Another cluster of accounts, some with a track record on prior Iran-related strikes identified by Bubblemaps made over $600k combined on the ceasefire, on top of $1.2M+ from correctly timing earlier military actions.

Bets were placed when implied probabilities were low often single digits to low teens, then prices spiked as news broke. Prediction markets like Polymarket are decentralized, pseudonymous, and unregulated in the traditional sense for US users in many cases. This setup amplifies suspicions when:Newly created wallets with no history suddenly deploy significant capital on hyper-specific, low-probability outcomes right before major announcements.

The timing is extremely tight—sometimes hours or less before public news. Similar patterns occurred earlier in the Iran conflict. Analysts and lawmakers have noted it’s highly unlikely these are all good-faith random trades. Polymarket has faced repeated scrutiny on events like Venezuelan political developments and prior Iran strikes, leading the platform to tighten some rules against suspicious activity. However, enforcement is limited without KYC or centralized oversight.

Critics argue this could allow government and military insiders, journalists, or connected parties to monetize non-public info. Well-timed trades alone aren’t proof of illegality. Sophisticated traders monitor news, sentiment, and on-chain signals; prediction markets often price in rumors faster than traditional media. Some profitable accounts had prior successful (publicly visible) bets on related events, suggesting skill or research rather than leaks.

Polymarket resolutions can also spark disputes over exact definitions e.g., what counts as a ceasefireadding another layer of uncertainty. This isn’t isolated—prediction markets have boomed on geopolitics, elections, and crypto events, offering crowd-sourced probabilities that sometimes outperform polls or analysts.

But high-stakes, low-liquidity geopolitical contracts invite both genuine edge and potential abuse. Regulators and Congress have eyed rules for these platforms, especially as volumes grow. The story highlights a tension: decentralized markets can aggregate information efficiently, but pseudonymity makes policing insider trading difficult.

Whether these specific wins reflect leaks, exceptional analysis, or luck remains unproven publicly—on-chain data shows the profits, not the source of the edge. Markets continue to trade related outcomes, with significant ongoing volume. Geopolitical events like this often produce volatility in oil, equities, and crypto alongside the betting action.

US SEC Acknowledges 2025 Enforcement under Gensler Delivered No Meaningful Investor Benefit

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WASHINGTON, DC - OCTOBER 03: Securities and Exchange Commission (SEC) Chair Gary Gensler listens during a meeting with the Treasury Department's Financial Stability Oversight Council at the U.S. Treasury Department on October 03, 2022 in Washington, DC. The council held the meeting to discuss a range of topics including climate-related financial risk and the recent Treasury report on the adoption of cloud services in the financial sector. (Photo by Anna Moneymaker/Getty Images)

The SEC has officially acknowledged in its fiscal 2025 enforcement results that certain Gary Gensler-era crypto cases delivered no meaningful investor benefit, reflected a misinterpretation of federal securities laws, and represented a misallocation of agency resources.

In a public statement accompanying its FY 2025 enforcement report, the agency highlighted: Seven crypto registration cases and six dealer definition cases from FY 2022–2024 that applied novel legal theories without establishing clear investor harm. These actions identified no direct investor harm and produced no investor benefit or protection. The current Commission views them as demonstrating a misinterpretation of the federal securities laws, a misallocation of Commission resources, and a bias for volume of cases brought versus matters of investor protection.

The report also critiqued a broader set of 95 recordkeeping actions mostly off-channel communications cases that generated $2.3 billion in penalties but similarly yielded no direct investor protection benefits. This marks a clear pivot from the regulation by enforcement approach under former Chair Gary Gensler who left in January 2025. Under Gensler, the SEC pursued dozens of crypto-related actions—peaking at 46 in 2023—often targeting unregistered securities offerings, staking, exchanges, and related activities using the Howey Test.

Current Chair Paul Atkins has emphasized redirecting resources toward high-harm misconduct like fraud, market manipulation, and abuses of trust, rather than pursuing volume or novel theories with limited investor impact. He stated: “We have redirected resources toward the types of misconduct that inflict the greatest harm… and away from approaches that prioritized volume and record-setting penalties over true investor protection.”

This aligns with broader changes since early 2025: Dismissals or closures of high-profile cases against Coinbase, Binance, Kraken, Consensys, and others. Overall drop in enforcement actions; down ~22–30% in some metrics and a sharper decline in crypto-specific cases. Establishment of a Crypto Task Force and moves toward clearer rulemaking rather than litigation-driven regulation.

The admission is notable because it comes directly from the SEC itself—not just critics or industry groups. It validates long-standing complaints from the crypto sector that many actions were overly aggressive, created regulatory uncertainty, and diverted resources from genuine fraud cases. Critics of the Gensler era argued this regulation by enforcement chilled innovation, cost the industry hundreds of millions in legal defense, and pushed activity offshore.

Supporters countered that widespread noncompliance in crypto necessitated strong action to protect investors. The current SEC appears to be signaling to courts, industry, and Congress that it wants to reset expectations: focus on clear harm and fraud, while exploring rulemaking and innovation-friendly frameworks. This doesn’t mean the SEC is abandoning oversight—fraud and manipulation remain priorities—but it does represent an explicit rejection of parts of the prior strategy as inefficient and legally strained.

The development reflects the broader policy shift following the 2024 election and leadership change at the agency. The SEC’s April 2026 acknowledgment that certain Gensler-era crypto enforcement actions produced no direct investor harm or benefit, involved novel legal theories, and represented a misallocation of resources has triggered several tangible and ongoing impacts across the industry, markets, regulation, and legal landscape.

The SEC dismissed often with prejudice at least seven major crypto cases starting in February 2025, including actions against Coinbase, Binance, Consensys, Kraken (Payward), Cumberland DRW, Dragonchain, and Balina. This removed significant legal overhang for these firms, halting protracted litigation over unregistered securities offerings, staking, and platform operations.

Investigations or actions involving entities like Gemini, Uniswap Labs, OpenSea, Crypto.com, Robinhood, and Ondo Finance were dropped or wound down for policy reasons, shifting away from regulation by enforcement. By publicly labeling these approaches as flawed or misguided, the SEC has undermined the legal foundation for future cases relying on the same novel interpretations of the Howey Test or dealer definitions. This strengthens defendants’ positions in any remaining or new litigation.