As artificial intelligence agents begin executing high-value business tasks, the market is rushing to build the legal and judicial guardrails they need to operate safely. Today's major developments center on enforcing machine-to-machine contracts, headlined by a new Web3 consortium launching a decentralized 'Internet Court' for AI disputes and the UN advancing global trust standards.
A consortium of 27 crypto and Web3 firms, led by the GenLayer Foundation and including OKX, MetaMask, and Matter Labs, has launched the 'Internet Court.' This new open standard is designed to resolve contractual disputes between autonomous AI agents at machine speed. The initiative aims to create a unified, interoperable framework for payments, escrow, and arbitration, addressing a critical infrastructure gap in the burgeoning agentic economy where traditional legal systems are too slow to handle machine-to-machine disagreements.
Why it matters
The 'Internet Court' is a foundational piece of infrastructure for a functional agentic economy. As AIs begin to transact with each other, the probability of errors, disagreements, and bad-faith actions approaches certainty. Without a mechanism for rapid, automated dispute resolution, the entire system would grind to a halt. This initiative moves beyond simple payment rails to build the legal and trust layer necessary for agents to reliably engage in commerce, ensuring that financial commitments can be enforced without human intervention. This is a critical step for enabling high-value, high-volume B2B agentic commerce.
Proponents, including the 27 founding members, argue this protocol is essential for building trust and accountability into AI agent transactions, which is vital for scaling the agentic economy to its projected multi-trillion dollar potential. Skeptics may question the feasibility and impartiality of a decentralized, AI-driven adjudication system, particularly in handling complex or subjective disputes that currently challenge human courts. The framework will rely on ZK-powered infrastructure from Starknet, ZKsync, and others to function.
Amazon Web Services has launched a new 'Agentic AI Solutions Framework' designed for complex SAP enterprise resource planning (ERP) use cases. The framework, built using Amazon Bedrock AgentCore and the Strands SDK, automates processes like matching payments to invoices. Crucially, it incorporates a 'deny-by-default' security model, assigns separate, unique identities to each AI agent, and creates immutable logs for every action, ensuring full auditability.
Why it matters
This release from AWS is a significant real-world implementation of the trust and identity principles the market has been theorizing about. By building in deny-by-default permissions, distinct agent identities, and immutable audit trails from the start, AWS is providing a concrete architectural pattern for how enterprises can safely deploy autonomous agents. This isn't just a new tool; it's a production-grade template for solving the agentic governance problem, directly addressing the security and accountability concerns that are currently the primary brake on enterprise adoption.
Amazon CTO Werner Vogels has recently emphasized the need for 'verifiable trust' systems, warning that LLMs are optimized for plausibility over truth. This framework is a direct answer to that concern, implementing verification and permissioning at an architectural level. Security experts highlight that separating agent identities, rather than having them operate under a user's or a shared service account's credentials, is a fundamental requirement for preventing privilege escalation and ensuring accountability.
A new analysis argues for a shift in focus from trying to build perfectly trustworthy AI agents to designing robust architectural 'harnesses' that make agent fallibility manageable. Using a custom tool called 'Ringer' to simulate an agent-run company, the author demonstrates that while individual agents will inevitably fabricate or fail, systems with strong audit trails, clear organizational structures, and defined appeals processes can contain the damage. The piece draws parallels to historical trust mechanisms like double-entry bookkeeping, which made human error a manageable line item.
Why it matters
This provides a critical, counterintuitive framework for founders and operators struggling with AI reliability. Instead of waiting for flawless models, the playbook is to build systems that presume failure. This approach of 'harness engineering'—designing the scaffolding for context, verification, and memory around the agent—is the key to deploying agentic AI productively in the near term. It reframes the problem from an unsolvable one of model perfection to a solvable one of systems design, offering a practical path forward for building agent-powered products and internal tools.
A new GitHub repository dedicated to 'harness engineering' reinforces this concept, compiling patterns and templates for building the necessary infrastructure around agents. This discipline emphasizes that the harness, not just the model, determines an agent's success in production. This aligns with warnings from figures like Amazon CTO Werner Vogels about the inherent untrustworthiness of LLMs, suggesting that architectural solutions like multi-agent quorums and automated verification are the only way to manage enterprise risk.
Following up on the UN ITU's push for an agent 'trustworthiness passport' we noted recently, the agency has formally detailed its Focus Group on Trust and Identity for Humans and Agentic AI (FG-TIDA). The initiative is focused on developing international frameworks to ensure agents are identifiable and accountable, driven specifically by the rising risks of impersonation in critical sectors like finance and telecommunications.
Why it matters
Following a series of enterprise and national-level proposals, the ITU's involvement elevates the 'Know Your Agent' problem to the level of global standards-setting. This signals a broad consensus that jury-rigged or proprietary solutions for agent identity are insufficient. The creation of a common international standard—akin to a 'trustworthiness passport' for agents—is a foundational step toward enabling secure and interoperable agentic commerce on a global scale. This is no longer a niche technical problem; it's now a formal diplomatic and standardization effort.
Seizo Onoe, Director of the ITU's Telecommunication Standardization Bureau, stated that agentic AI is advancing faster than current trust mechanisms, making global standards an urgent necessity. Salesforce AI Research echoed this sentiment, proposing five principles for enterprise leaders that mirror the ITU's goals, including verifiable identity and structured human accountability. The initiative will focus on preserving human control in critical transactions while building common standards.
Identity authorization network Proof, which recently launched the x401 protocol for agent identity we covered, has partnered with business data platform Enigma to launch 'Business Certificates.' These cryptographic credentials are designed to continuously verify a business's identity and link it to specific, authorized individuals and AI agents. The system combines Proof's biometric verification with Enigma's Know Your Business (KYB) data, creating a verifiable chain of authority from entity to agent.
Why it matters
This product directly addresses the critical liability gap in agentic B2B commerce: proving that an AI agent is acting on behalf of a legitimate, verified business and is authorized to do so. By cryptographically binding an agent's action to both a human's biometric identity and a business's legal identity, this solution provides a robust mechanism for accountability. For builders in the agentic commerce space, this is a key piece of trust infrastructure needed to underwrite high-value transactions and mitigate fraud risk.
The solution is an implementation of the 'Know Your Agent' (KYA) frameworks that have been gaining traction. It tackles the problem from both sides: Know Your Business (KYB) to establish the entity, and Know Your Customer (KYC) with biometrics to establish the human authorizer. This provides a strong countermeasure to scenarios where agents might be compromised or act outside their mandate.
In a recent interview, Meta's Chief Data Officer Alex Schultz described agentic commerce as an inevitability and the 'next tier of business' for the company. He revealed that over a million businesses are already using Meta's AI agents on a weekly basis. Schultz also stated that stablecoins are the likely payment layer for this new economy and made an explicit call for a scalable, reliable decentralized identity service to verify agents, calling the prospect 'incredible'.
Why it matters
This is a strong signal from one of the world's largest platform companies that agentic commerce is not a future-tense phenomenon; it's happening now at scale. Schultz's specific call-out for a decentralized identity solution validates the core thesis of many startups building trust infrastructure for AI. For founders in the decentralized identity and verifiable credential space, this is a clear market pull signal from a potential massive-scale partner, indicating that the lack of a robust identity layer is a recognized bottleneck to further growth.
Schultz's vision aligns with the broader industry rush to build agentic infrastructure. His focus on stablecoins as the payment layer echoes recent findings showing USDC dominates the ~$73M in crypto-based agent settlements over the past year. The explicit need for a verification layer to distinguish legitimate human users from bots and authorized agents reinforces the fundamental security challenge that vendors like Proof, Enigma, and projects within the Linux Foundation are racing to solve.
Enterprise AI-agent startup Lyzr reportedly used its own AI agent to orchestrate the outreach for its Series B fundraise. According to a post-mortem, the agent handled repetitive, asynchronous tasks like initial investor research, Q&A, and drafting memos. This compressed a process that typically takes a month into just two weeks, generating $400M in investor interest for a targeted $100M round. Human founders remained responsible for high-judgment tasks like building relationships and closing the deal.
Why it matters
This case study provides a concrete playbook for how founders can leverage AI agents to automate the 'middle-links' of complex, high-stakes workflows like fundraising or enterprise sales. The key is not to delegate the entire outcome ('raise our Series B') but to break it down and hand off the high-volume, repetitive parts. This frees up founder bandwidth for the irreplaceable tasks of human judgment and trust-building, offering a practical model for scaling founder-led efforts without scaling headcount.
The framework suggests that agents are best used for tasks that are asynchronous, repetitive, and have clear inputs and outputs. This allows founders to maintain control over strategic decisions while dramatically increasing their operational leverage. The success of this approach hinges on a clear separation between tasks that require machine efficiency and those that demand human connection.
ZoomInfo has launched GTM Bench, a new benchmark for evaluating the performance of LLMs and AI agents on real-world go-to-market tasks. The benchmark scores AI systems along two axes: 'Answer,' which measures how much of a job the system completes (e.g., building a target list), and 'Grounding,' which measures how much of the output is based on verifiable data. In initial tests, ZoomInfo's own GTM.AI reportedly led the field.
Why it matters
The lack of standardized, task-specific benchmarks has made it difficult for founders and sales leaders to evaluate the explosion of AI GTM tools. GTM Bench provides a much-needed framework for cutting through the hype by focusing on two metrics that actually matter for sales ops: job completion and data accuracy. This is a move toward accountability for AI vendors and gives builders a more rigorous way to assess which tools will actually improve their GTM execution.
The focus on 'Grounding' is particularly important, as unverified or hallucinated data in a sales context can damage reputation and waste resources. This benchmark pushes the industry beyond evaluating AI on abstract reasoning and towards measuring its practical utility and reliability in a core business function.
In a significant move demonstrating deepening institutional adoption, J.P. Morgan has tokenized approximately $800 million in assets across two of its funds on the public Ethereum mainnet. This action highlights the growing preference among major financial institutions for Ethereum's infrastructure when implementing their blockchain strategies for real-world assets.
Why it matters
This isn't a pilot program; it's a substantial deployment of assets on a public blockchain by one of the world's largest banks. It provides a strong proof point for Ethereum's convergence narrative, showing it's being chosen as the foundational settlement layer for serious financial instruments, not just speculative assets. For builders, this validates the thesis that institutional-grade finance is being built on Ethereum, creating demand for related services and infrastructure.
This move complements the general trend of institutional integration. Tom Lee of Bitmine recently predicted Ethereum would unite traditional finance and crypto. Meanwhile, a Cambridge Centre for Alternative Finance report flags a counter-risk: the concentration of Ethereum nodes in the US and on major cloud providers could pose a threat to the $25 billion of tokenized assets on the network if a major outage were to occur, highlighting the tension between adoption and decentralization.
The Ethereum Foundation has disclosed that its Protocol Security team used coordinated AI agents to successfully identify genuine security vulnerabilities in core Ethereum code. In a blog post on Thursday, the team confirmed agents found several bugs, including a networking-layer flaw that has since been patched. This marks one of the first public admissions from a major protocol team that machine-driven code review is catching critical bugs that human auditors have missed.
Why it matters
The successful use of AI agents to secure a system with hundreds of billions of dollars at stake is a powerful proof point for the maturity of AI in critical infrastructure security. It demonstrates that agentic AI can be a potent defensive tool, not just an attack vector. However, it's a double-edged sword: the same capabilities are available to attackers. The key takeaway, as the EF team notes, is that the primary challenge shifted from bug discovery to the human-led triage and verification of agent findings, underscoring where expert judgment remains irreplaceable.
The EF's security team emphasized that the main bottleneck wasn't getting agents to find potential bugs, but the human effort required to triage the findings and distinguish real vulnerabilities from false positives. This effort is part of a broader 'Trillion Dollar Security Initiative' (TDS) that systematically uses a suite of LLMs (Claude, Gemini, GPT) and custom tools to audit the protocol, especially complex components like zkEVMs and rollups.
An analysis from maccelerator.la argues that a new, strategic role is emerging in post-product-market-fit startups: the 'Flow Engineer.' This role evolves from the 'GTM Engineer,' who manually wires together sales and marketing tools. In contrast, the Flow Engineer designs self-running, AI-powered revenue systems that automate routine decisions across the GTM stack. The author contends that many founders mistakenly scale operational headcount to handle complexity when they should be scaling automated systems.
Why it matters
This provides a critical framework for founders thinking about how to scale operations efficiently. Hiring Flow Engineers instead of more ops people represents a fundamental shift towards building a company that runs on automated, intelligent systems rather than manual processes. For a $0-10M stage company, making this transition early can create a massive competitive advantage in cost-to-serve, response speed, and the ability to scale revenue without a corresponding explosion in overhead.
The piece suggests that the core job of a Flow Engineer is to map out the company's routine decision-making processes—like lead routing, discount approvals, or churn risk identification—and then design an automated 'flow' to handle them. This frees up human employees to focus on exceptions and high-judgment tasks, which is a more scalable and cost-effective organizational model.
A reflection by a former early Facebook engineer argues that the company's initial success was a direct result of its disciplined, narrow operational focus. The scarcity of resources forced leadership to reject many 'good ideas' that fell outside the core product mission. This discipline created clarity and alignment, but according to the author, it weakened as the company grew and resources became abundant, leading to a diffusion of focus through the accumulation of many 'small, reasonable' commitments.
Why it matters
This is a powerful, counterintuitive lesson for founders navigating the $0-10M stage. The temptation to pursue adjacent opportunities and say 'yes' to reasonable feature requests can be the very thing that derails a startup. The analysis provides a structural framework for understanding how 'strategic debt' accumulates, not through big mistakes, but through a series of small, seemingly logical additions that collectively erode focus. Maintaining the discipline to say 'no' is presented as a core, and often overlooked, driver of long-term success.
The author suggests that this loss of focus is a natural consequence of success and growth. When a company has more money, people, and a stronger market position, the institutional 'muscle' for making hard trade-offs atrophies. This makes it crucial for founders to intentionally preserve a culture of focused execution even when resources are no longer scarce.
ICONIQ's Q2 2026 'State of AI' report indicates a major shift in the AI economy, moving from demonstrating technical capability to proving financial return. Based on a survey of 305 executives, the report finds that AI products are now approaching 50% of revenue for the companies surveyed. Key trends include expanding margins and a growing focus on treating pricing, cost, and organizational design as integral product decisions.
Why it matters
This report provides hard data showing the AI market is maturing rapidly. The era of 'AI for AI's sake' is over; the new mandate is to prove AI pays. For founders, this means that GTM, pricing strategy, and capital-efficient organizational design are no longer afterthoughts to the technology—they are core to the product itself. Demonstrating a clear path to revenue and margin expansion is now the primary requirement for securing funding and winning enterprise customers.
The report highlights a move toward 'nano-unicorns'—companies achieving significant revenue with minimal staff, enabled by AI. It also notes a strategic shift where founders are increasingly hiring founding designers early to focus on creating 'Minimum Lovable Products,' signaling that user experience and trust are becoming key differentiators in a crowded AI market.
While fighting a multi-front battle against CFTC probes and a recent ban upheld by Dutch gambling regulators, Polymarket's affiliate, Coming Home GBA LLC, filed applications on July 3rd to become a registered futures commission merchant (FCM) with the National Futures Association (NFA). The move is the first step in a process to offer regulated margin trading on its prediction market platform in the U.S., which would allow users to trade with leverage.
Why it matters
Offering regulated margin trading would be a game-changer for US prediction markets, dramatically increasing capital efficiency and attracting more sophisticated, professional traders. This could significantly boost liquidity and improve price discovery. However, Polymarket is attempting this expansion while under active regulatory scrutiny, creating a high-stakes scenario. The outcome will not only determine Polymarket's future in the US but also set a major precedent for how much product innovation regulators are willing to tolerate from the entire sector.
Crypto Daily notes that while leverage increases capital efficiency, it also introduces higher risk and complex operational challenges around liquidations and oracle clarity. The application puts Polymarket in direct competition with regulated rival Kalshi, which received authorization for margin trading in March. The ongoing CFTC probe and a recent court ruling questioning federal preemption over state gambling laws represent significant headwinds to these plans.
We've been tracking the extreme barbell structure of the H1 2026 venture market, specifically the record $412.7 billion deployment masking a 27% collapse in seed funding. New underlying data reveals just how heavily skewed the landscape has become: 91% of that capital went to mega-rounds of $100 million or more. Simultaneously, first-time fund formation is on pace for its worst year since 2016.
Why it matters
This isn't just a trend; it's a structural reshaping of the venture market into a 'K-shaped' reality. For founders, this means the environment for raising capital is radically different depending on your sector and stage. If you are not a late-stage AI company, capital availability has effectively collapsed. This extreme concentration creates a severe pricing problem for the rest of the startup ecosystem, distorting incentives and making it exponentially harder for early-stage and non-AI companies to get funded.
Fortune magazine describes the situation as 'almost none of it trickled down.' Analysts note that OpenAI and Anthropic alone captured 43% of all global startup funding. This dynamic is also reflected in corporate VC behavior, where CVCs participated in a decade-low percentage of deals but accounted for a record 82.6% of deal value, focusing on large strategic AI bets.
An analysis in HTX News argues that Silicon Valley's venture capital ecosystem is structurally shifting away from meritocracy towards a system based on connections and pedigree. The piece contends that three factors are driving this: AI distorting growth expectations, LP capital concentrating in mega-funds, and the professionalization of VC. This results in a 'king-making' dynamic where well-connected founders, especially in AI, receive massive, pre-emptive funding rounds while outsiders face a closed door.
Why it matters
This structural analysis formalizes what many founders are experiencing anecdotally. The 'game' of fundraising is no longer just about building a great business; it's increasingly about having access to the right networks and fitting a consensus pattern. This concentration of capital and influence among a select few threatens to reduce innovation diversity by creating a monoculture of well-funded, insider-led companies, making it critical for outsider founders to develop alternative strategies for funding and distribution.
The article suggests this shift poses a long-term risk to Silicon Valley's 'meritocratic' ethos, which has historically been its core competitive advantage. It connects directly to the macro data showing extreme capital concentration, arguing that access to elite deals is now more important for VCs than pure investment selection skill. This forces founders into a binary world: either you're in the 'club' or you're on your own.
Following its recent rollout of AI crawler controls for beehiiv publishers, Cloudflare has partnered with Patreon to block bots from scraping paywalled and exclusive creator content. The move utilizes Cloudflare's network-level bot detection to prevent AI companies from training models on creator data without compensation, extending its broader policy shift to block 'multi-purpose crawlers' by default.
Why it matters
This is a major development in the battle over content value in the AI era. A major creator platform and a core internet infrastructure provider are teaming up to give builders and publishers a practical tool to defend their IP. This directly impacts distribution mechanics and monetization models, forcing a market for licensed content to emerge where AI companies must pay for the data they ingest. For creators, this is a significant step towards regaining control over their work.
Naked Capitalism reports that some publishers are even considering delisting from Google Search entirely to protect their content from uncompensated scraping. This highlights the growing tension and represents a significant power shift, where content owners are beginning to push back collectively against the 'data appropriation' practices of large AI model providers.
Japanese printing and tech firm Toppan is partnering with South Korean security company Raonsecure on a proof-of-concept pilot to establish a trust framework for Verifiable Credentials (VCs) between the two countries. The pilot focuses on streamlining the verification of digital academic credentials for university exchange programs, with plans to expand to employment, tourism, and business use cases.
Why it matters
This is a significant, practical step toward solving the hard problem of cross-border digital identity. While many protocols exist in theory, this pilot tests the legal and technical interoperability of VCs between two major economies. For the broader adoption of decentralized identity, proving that credentials issued in one country can be trusted and verified in another is a critical milestone. It moves ZK and cryptographic identity tech from protocol-level news into real-world deployment for credentialing.
The pilot uses Raonsecure's blockchain-based OmniOne Digital ID platform and involves Soka University in Japan and Chung-Ang University in South Korea. Success here could create a template for establishing bilateral or regional trust frameworks, dramatically reducing administrative friction for international students, workers, and travelers.
A new analysis on dev.to, referencing Vitalik Buterin's recent writings, explores the design challenges of verifying contributions in an AI-saturated world. The author argues for a system that attests to specific actions rather than binding everything to a single, global identity. This approach allows for agent accountability and credentialing (e.g., 'this agent performed this verified action') without forcing users into a 'global deanonymization' trap or sacrificing pseudonymity.
Why it matters
This framework offers a nuanced solution to the identity-versus-privacy problem in agentic systems. As AI makes creating artifacts cheap, the value shifts to proving participation, responsibility, and auditable evidence. The proposed model of attesting to actions allows for the creation of robust, verifiable reputation systems for agents and their operators without requiring a brittle, over-centralized identity infrastructure. This is critical for building trust in decentralized or pseudonymous environments where agents need to be held accountable.
The author is building an open-source experiment called WebAZ to explore these ideas. The approach is heavily influenced by Buterin's concerns about the dangers of ZK-wrapping a single, persistent identity, instead favoring systems that allow for selective disclosure and context-specific personas.
Researchers at Stanford University have released Biomni, which they claim is the first general-purpose biomedical AI agent. The agent is capable of autonomously performing complex scientific tasks, including interpreting multimodal data (like microscope images), generating experimental protocols, planning experiments, and analyzing vast amounts of scientific literature. The university claims Biomni can significantly accelerate research that would typically take human scientists weeks to complete.
Why it matters
Biomni represents a potential paradigm shift in the mechanics of scientific discovery. By automating not just data analysis but also experimental design and literature review, it could dramatically increase the pace of research in fields like biotech and longevity. For the DeSci movement, such agents could lower the barrier to entry for conducting research and democratize access to high-level scientific reasoning, though questions of verification and trust in agent-generated science will become paramount.
This development aligns with Anthropic's recent move to launch its own internal drug discovery programs, signaling a trend where AI companies are not just providing tools but are becoming active participants in scientific research. The key challenge will be ensuring the reliability and reproducibility of discoveries made by autonomous agents.
Liberland, a micronation situated between Croatia and Serbia, is experimenting with a governance model where voting power is directly tied to wealth in its native cryptocurrency, Liberland Merits (LLM). The project, which has drawn backing from prominent crypto figures like Justin Sun and has been associated with Peter Thiel, is framed as a libertarian utopia. However, critics argue it's a vehicle for a wealthy oligarchy to exert control, drawing parallels to 'Dark Enlightenment' philosophies.
Why it matters
Liberland is a real-world, albeit fringe, experiment in the kind of governance models being discussed in network state and intentional community circles. It moves the concept of 'one token, one vote' from DAO governance to actual territorial governance. For those tracking these experiments, Liberland is a case study in the tensions between libertarian ideals, crypto-anarchism, and the practicalities of creating a functioning state where political influence is explicitly a function of capital. Its successes and failures will provide valuable data on the viability of such models.
The project recently awarded Vitalik Buterin its highest honor in what was seen by some as a strategic move to gain legitimacy. The project's proponents see it as the future of governance, free from the inefficiencies of traditional democracy. Critics see it as a dystopian project that formalizes plutocracy.
A Decentralized Adjudication Layer for AI Agents Is Forming A consortium of 27 firms, including OKX, MetaMask, and ZKsync, has launched the 'Internet Court,' an open standard for resolving contractual disputes between AI agents. This new protocol aims to provide machine-speed payment, escrow, and arbitration, filling a critical gap in agentic commerce that traditional legal systems cannot address.
International Bodies Are Building Global AI Trust Frameworks Reflecting the seriousness of the agent governance problem, the UN's International Telecommunication Union (ITU) has established a new Focus Group to develop a global framework for AI agent trust and identity. This follows a wave of enterprise and national-level initiatives, signaling a coordinated international push for standards.
The Venture Capital Market's K-Shaped Structure Is Now Undeniable Data from H1 2026 confirms the extreme bifurcation of the VC market. While US VCs deployed a record $412.7 billion, 86% of it went to AI companies and 91% to mega-rounds of $100M or more. Meanwhile, seed funding has plummeted 27%, creating a 'K-shaped' reality where capital is abundant for a select few AI giants and scarce for everyone else.
Ethereum's Role as an Institutional Settlement Layer Crystallizes A series of major institutional moves, including J.P. Morgan tokenizing $800M in funds on the network, underscore Ethereum's growing adoption as a core settlement layer. The focus is shifting from speculative use cases to leveraging its security for tokenized real-world assets, a trend reinforced by the launch of new institutional-focused nonprofits and products like Robinhood Chain.
'Harness Engineering' Emerges as the Key Discipline for Reliable AI Agents As models become more capable, the focus is shifting from the agent itself to the 'harness'—the scaffolding of verification, memory, and authorization controls around it. New frameworks from AWS and analyses from thought leaders emphasize that architectural solutions, like deny-by-default rules and multi-agent quorums, are how enterprises will manage agent fallibility and deploy them safely.
What to Expect
2026-07-31—Deadline for comments on proposed CFTC rules restricting violence-based prediction market contracts.
2026-08-01—Minnesota's proposed ban on prediction markets is scheduled to take effect, pending CFTC legal challenges.
2026-09-15—Cloudflare's new policy to block mixed-use AI crawlers by default is set to take effect.
2026-11-01—Apple's 30% commission on Patreon iOS app earnings is scheduled to be implemented.
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