Today's briefing highlights the acceleration of several structural shifts we've been following. Fresh venture data confirms that capital concentration into megafunds is deepening, further starving early-stage founders of liquidity. Meanwhile, the Ethereum Foundation's recent restructuring has spun out a new entity to tackle institutional privacy, and legacy security players are finally rolling out practical infrastructure to solve the agentic trust bottleneck.
In a new paper titled 'The Agentic Economy,' Circle CEO Jeremy Allaire outlines a convergence thesis where AI agents and on-chain blockchain economies are set to merge, creating a new economic landscape. He posits that as AI drives down the cost of work, blockchain's role is to reduce the cost of value transfer and verification. For this to function, Allaire argues, three pillars are essential: on-chain identity and accountability for agents, a monetary foundation of fully-backed stablecoins, and new credit models built on transparent on-chain data.
Why it matters
Allaire's framework provides a coherent, macro-level synthesis for how the agentic AI and crypto ecosystems will necessarily intertwine, moving beyond theoretical discussions to outline the required infrastructure. For founders, this thesis offers a strategic map, highlighting that the core challenge isn't just building smarter agents, but also constructing the trust, payment, and credit rails they will operate on. It reinforces that the value proposition of blockchains in an AI-driven world is not as a consumer-facing product, but as a fundamental, machine-readable settlement and identity layer. The paper also contains a crucial warning about capital concentration, arguing that without on-chain mechanisms for ownership, the agentic economy risks becoming dominated by a few powerful entities.
Allaire's thesis is that AI agents, as autonomous economic actors, will require a native digital monetary system, verifiable identity, and open capital markets to function at scale. He foresees a world where agents not only transact but also access credit based on their on-chain history, creating new financial primitives. The paper argues this convergence is inevitable because the core functions of a market economy—identity, money, and credit—all require a level of trust and auditable state that blockchains are uniquely suited to provide for non-human actors.
Following Entrust CIO Rishi Kaushal's recent call to treat AI agents as a new 'first-class identity,' the firm launched its 'Agentic AI Trust Accelerator' on Tuesday. The co-development program partners with enterprises and tech partners to build foundational identity infrastructure for autonomous AI agents, focusing on verifiable identity, real-time authorization, and cryptographic trust to move deployments out of pilot phases.
Why it matters
The launch of a dedicated trust accelerator by a major identity incumbent like Entrust signals that the market is moving past theoretical discussions and into building the practical governance required for the agentic economy. This directly addresses the core bottleneck we've been tracking: AI capabilities are outpacing the trust infrastructure needed for their safe deployment. For founders building in this space, this initiative serves as strong validation, creating partnership opportunities and signaling that enterprises are actively seeking solutions for agent identity, authorization, and accountability. It confirms that the 'verification counterpart' to AI hype is becoming a distinct and fundable market category.
Entrust's CIO, James Bisset, stated that while enterprises are eager to deploy agents, they are 'rightly concerned' that governance has not kept pace. The program aims to establish a 'trust plane' for AI, ensuring agents operate within defined business and regulatory boundaries. SiliconANGLE notes this directly tackles the governance gap hindering AI deployment. This move is part of a broader industry push, with other players like Google and the Linux Foundation also launching standards for agent discovery and payments (ARD, x402), solidifying the multi-layered stack required for a functioning agent economy.
Continuing the rollout of its 'Web4' agent stack, Lithosphere announced on Tuesday that its decentralized naming and routing system, DNNS, now supports 'permissioned discovery.' Instead of being fully public or unlisted, services can now grant name resolution rights exclusively to specific AI agents with verified identities, leveraging Lithosphere's existing PPAL identity architecture.
Why it matters
This is a critical piece of the trust infrastructure required for secure B2B agentic commerce. Standard DNS is a public directory; permissioned discovery creates a 'private address book' for machines, controlled by cryptographic identity. This allows companies to expose sensitive APIs or data services to a select group of trusted partner agents without revealing their existence to the public internet. It directly addresses the need for confidentiality and access control in an agent-to-agent economy, reducing the attack surface and enabling more secure, high-trust interactions.
This capability is crucial for use cases involving sensitive data, regulated industries, or closed ecosystems where services must not be publicly discoverable. By tying discoverability to a verified agent identity, DNNS moves beyond simple network access control to a more granular, identity-based form of governance for machine-to-machine communication.
The Masumi Network, a collaboration between NMKR and Serviceplan Group that began in late 2024, has officially launched its decentralized infrastructure on the Cardano blockchain. The network is designed to enable AI agents from different companies to collaborate, monetize services, and maintain verifiable on-chain reputations. It aims to solve the core problems of accountability and interoperability in an agent-driven economy, allowing machines to securely transact with each other using stablecoins.
Why it matters
Masumi represents another critical data point showing that the future of blockchain infrastructure may be less about human-facing dApps and more about providing the core operating rails for a machine-to-machine economy. By creating a framework for verifiable identity, reputation, and settlement, it tackles the trust problem that currently limits B2B agent collaboration. For builders, this reinforces the thesis that crypto's most significant role in the AI era will be providing the 'boring' but essential backend for agentic commerce, where auditable transactions and identity are non-negotiable.
Masumi's architecture allows agents to operate with their own wallets and on-chain identities, creating a persistent and auditable record of their interactions. This enables a level of trust between agents from different organizations that is difficult to achieve with traditional, siloed API integrations. The choice of Cardano suggests a focus on transaction cost predictability and formal verification for its smart contracts.
Following the launch of its agentic security framework last month, Akamai released a new report identifying the commerce sector as the primary target for sophisticated AI-driven cyberattacks. The rise of agentic commerce pushed AI bot activity to 47.9% of all commerce traffic by December 2025, detailing a surge in API attacks and agentic fraud tactics like synthetic identity creation and hijacking.
Why it matters
This report quantifies the security risks of the emerging agentic economy. As autonomous AI agents begin to conduct commerce, they also become prime targets and tools for fraud. This necessitates a fundamental shift in security posture from blocking simple bots to discerning malicious agentic behavior that mimics legitimate human activity. For any business engaging in e-commerce, building a robust trust and verification infrastructure is no longer optional; it's a prerequisite for survival in an environment where nearly half the 'customers' could be machines.
The report stresses that traditional security models are insufficient. It calls for businesses to become 'agentically ready' by implementing risk-based bot governance and integrating cybersecurity with fraud prevention. The findings show that attackers are exploiting the very tools of agentic commerce to create synthetic identities and hijack legitimate agent sessions, turning the technology against itself.
Vieu announced on Tuesday the launch of its 'Business Graph,' a new platform that maps professional relationships based on observed, verifiable connections rather than self-reported data from social profiles. The system analyzes data points like shared work history, board memberships, and joint ventures to build a graph of trusted relationships. This verifiable data is intended for B2B use cases like strategic account planning, identifying churn risk through executive departures, and recruiting, with an API designed for integration with AI assistants.
Why it matters
As AI-driven sales and outreach tools proliferate, the quality and verifiability of the underlying data become the primary bottleneck. The Business Graph tackles this directly, creating a source of B2B social proof grounded in observed reality, not self-reported claims. This is a critical piece of trust infrastructure. For founders focused on GTM, it represents a potentially more reliable way to identify warm paths into target accounts and for AI agents to make decisions based on trustworthy relationship data, moving beyond the noise of platforms saturated with AI-generated content.
Vieu's approach contrasts with platforms like LinkedIn, where relationship data is user-generated and can be noisy or inaccurate. By focusing on verifiable connections, the Business Graph aims to provide a higher-fidelity map of the business world. This is particularly relevant as AI agents are deployed for tasks like automated prospecting, where relying on flawed data can lead to wasted effort and damaged reputation.
The saturation of automated B2B content we've been tracking is accelerating. While earlier estimates put AI-generated long-form LinkedIn posts at 41%, a new study by Pangram found that nearly two-thirds (62%) of content on the platform is now likely machine-written. A separate analysis by Webiano.Digital noted that posts over 250 words are significantly more likely to be fully AI-generated, fueling concerns about the erosion of authenticity.
Why it matters
This data quantifies a structural shift in B2B content and distribution that many have anecdotally observed: professional platforms are becoming saturated with low-cost, AI-generated content. For founders using founder-led sales and content strategies, this is a critical counter-signal. The value of authentic, human-generated insight increases, but its ability to cut through the noise diminishes. It forces a strategic question: do you lean into AI for scale, or double down on verifiable authenticity as the key differentiator? This fundamentally alters the mechanics of building B2B social proof.
Pangram's analysis suggests the economics of content production have fundamentally changed, with AI collapsing the cost of producing long-form text that was once a signal of effort and expertise. This trend makes it harder for users to distinguish between genuine insight and automated content marketing, potentially devaluing the platform as a source of reliable professional information and connection.
Developer Robert Pelloni has documented the creation of 'TormentNexus,' an autonomous AI agent designed to execute a complete sales cycle. Written in Go, the agent can discover leads, enrich contact data, generate personalized multi-channel outreach (email and social), handle follow-ups, and manage the entire sales pipeline, including closing deals and processing payments. In a striking proof of concept, the agent successfully sold its own services to a customer, demonstrating a fully automated GTM motion.
Why it matters
This project serves as a concrete, working example of a fully autonomous GTM strategy, moving far beyond the simple AI-assisted outreach that is becoming common. It provides a specific, albeit experimental, playbook for how a founder can leverage a system of agents to handle the entire sales process, from lead generation to billing. It's a powerful signal of the structural shift underway in sales and distribution, where the human role may transition entirely to system design and oversight rather than execution.
Pelloni's detailed write-up on dev.to outlines the agent's architecture, including its use of various APIs for data enrichment and communication channels. The success of the agent in selling itself highlights the potential for AI to not only perform tasks but to autonomously create economic value, representing a significant step toward the 'zero-employee company' concept.
Following the Ethereum Foundation's recent 20% staff cuts and the launch of the Joseph Lubin-backed Ethlabs, former members of the EF's privacy and institutional teams have officially spun out EthSystems. Backed by Lubin and corporate ETH holders Bitmine and Sharplink, the for-profit company aims to solve the confidentiality problem for banks using the public Ethereum network, becoming the third major commercial spin-out from the restructuring.
Why it matters
By commercializing ZK-privacy solutions, EthSystems creates a viable path for banks and regulated entities to perform operations like bond issuance on-chain. This strategic unbundling of the EF's functions marks a significant maturation, shifting from a research-oriented model to a commercial one designed to capture institutional capital. As we've tracked, this directly addresses the 'institutional capture risk' by providing a native, crypto-aligned privacy layer rather than ceding it to outside TradFi vendors.
According to CoinDesk, the founding team includes former heads of the EF's Institutional Privacy Task Force. ForkLog reports the new company will focus on enabling confidential transactions and data for large financial entities. This move is seen as a direct response to the EF's dissolution of its applied cryptography unit, allowing the core expertise to be commercialized to meet a clear market demand. TechTimes emphasizes that this could unlock trillions in tokenized assets by providing the auditable-yet-private infrastructure that institutions require.
Putting hard numbers to the ongoing debate over Ethereum's L2 value capture, new data shows the Robinhood Chain retains the vast majority of its transaction fees, settling only about 0.15% back to the Ethereum mainnet. As we've tracked, Ethereum co-founder Joseph Lubin continues to defend this low L1 fee model against community criticism, arguing it is a strategic feature that encourages long-term growth and broader application development.
Why it matters
This is the central tension in Ethereum's modular roadmap: L2s drive adoption and scale, but they also intercept the majority of economic activity, raising long-term questions about the L1's economic security and value accrual. The Robinhood Chain is a high-profile example of potential 'institutional capture,' where a large, centralized L2 operator benefits from Ethereum's security while contributing minimally to its revenue. For builders, this dynamic is critical to watch, as it will shape the long-term incentives and sustainability of the entire Ethereum stack.
DeFi Planet highlights that while L2s increase overall transaction volume, the minimal fee flow-back to the mainnet challenges Ethereum's economic model. In his defense, as reported by Crypto Briefing, Lubin argues that long-term value will accrue to ETH through its monetary premium and burn mechanism, rather than high direct fees, positioning the L1 as a public good settlement layer.
Adding to the record H1 2026 venture data we've been tracking, a new CNBC report highlights that megafunds (over $1B in AUM) accounted for 72% of all deal value, a sharp increase from 25% in H1 2025. Further data from The Fund CFO confirms 263 mega-rounds accounted for 81% of all Q2 funding, with AI alone attracting 70% of that capital.
Why it matters
This extreme capital concentration confirms that the venture market has bifurcated into a 'winner-take-most' landscape. For founders, this is the critical structural reality: access to capital is increasingly determined by a small number of large funds making fewer, larger bets on established players in strategically important sectors like AI, defense, and deep tech. This dynamic starves the 'missing middle' and makes it significantly harder for first-time founders or those outside the AI epicenter to secure funding, directly shaping what gets built by creating a distorted pricing problem for capital.
According to CNBC, the dynamic creates a feedback loop where large funds get preferential access to the best deals, further cementing their dominance. AlleyWatch data for June shows that just two deals, Baseten ($1.5B) and AppsFlyer ($1B), represented a significant portion of the month's total. An analysis from yeandel.co.uk argues this AI funding bubble will eventually correct, but warns the correction would paradoxically deepen US control over the AI stack by starving smaller, independent challengers who can't survive a capital winter.
Blockchain.com announced a partnership on Tuesday to embed Polymarket's prediction markets directly into its brokerage application for its 43 million verified users in the EU. The integration allows users to trade on real-world event outcomes, such as the World Cup semi-finals, using their existing crypto balances without leaving the app or undergoing a separate onboarding process. The move comes as Polymarket reports generating over $4.2 billion in volume on football matches alone.
Why it matters
This partnership marks a significant strategic shift for Polymarket and the broader prediction market space, moving from a standalone platform to a distributable infrastructure layer. By embedding its functionality into one of the largest retail crypto applications, Polymarket dramatically reduces user friction and gains access to a massive, pre-funded user base. This 'meet the users where they are' strategy is a powerful distribution play that could significantly increase market liquidity and accelerate mainstream adoption, especially in jurisdictions like the EU with clearer regulatory paths than the US.
PaymentExpert.com notes this turns Polymarket into a 'distribution layer' for other platforms. Finance Magnates highlights the advantage of tapping into an existing user base without requiring fund transfers. This follows a broader trend of embedding financial services, with the goal of making prediction markets a native feature of existing financial apps rather than a niche destination.
Coinbase's AI service sent a notification on Tuesday erroneously announcing that Norway had defeated Brazil 3-2 in the World Cup, before the match had even begun. Norway did go on to win 2-1, but the incident of a major platform distributing false, AI-generated information as fact has raised new credibility concerns for prediction markets and their information-gathering role, especially amid ongoing regulatory scrutiny of the sector.
Why it matters
This incident is more than just a technical glitch; it strikes at the core 'epistemic' value proposition of prediction markets. For a market to be a reliable forecasting tool, the information environment surrounding it must have integrity. When a major, trusted platform like Coinbase injects AI-generated falsehoods into that environment, it undermines the entire premise. This provides potent ammunition for regulators and critics who argue these markets are closer to gambling than reliable information discovery, and it highlights the potential for motivated reasoning to be amplified, not corrected, by flawed AI oracles.
24/7 Wall St. notes the incident is particularly damaging given the ongoing lawsuits and regulatory probes against Coinbase, Kalshi, and Polymarket. The error fuels the narrative that these platforms are not yet mature enough to be considered reliable sources of truth, complicating their path to mainstream regulatory acceptance.
A new analysis from Insight Partners defines the emerging role of the 'Forward Deployed Engineer' (FDE). FDEs are technical experts embedded within customer-facing teams to bridge the gap between a powerful but incomplete AI platform and a specific, high-value customer use case. The framework, based on insights from Databricks, Postman, and ServiceNow, explains how FDEs build last-mile solutions, capture market 'terrain' by solving real problems, and feed customer insights back into the core product roadmap.
Why it matters
The FDE model represents a crucial GTM and organizational strategy for founders building complex AI products. It directly counters the 'if you build it, they will come' fallacy by creating a formal mechanism to close the 'interest is not readiness' gap with early enterprise customers. For early-stage companies, an FDE-like function—even if it's the founder—is essential for securing design wins, de-risking the product roadmap with real-world feedback, and proving value beyond a generic platform demo. This is a structural approach to product-market fit that prioritizes solving a concrete customer problem over simply shipping features.
According to Insight Partners, FDEs are not just pre-sales or support; they are a strategic resource for product discovery and market capture. Unlike consultants, they build reusable solutions and directly influence the core product. The framework details when to hire the first FDE (typically post-PMF, around the first 5-10 enterprise customers) and how to scale the team as the company grows. The rise of this role signals that enterprise buyers increasingly expect vendors to deliver outcomes, not just tools.
In a new analysis, venture advisor Brent Harrison outlines how startups can quietly lose Product-Market Fit (PMF) even after achieving it. He identifies three primary causes: ICP (Ideal Customer Profile) drift, product signal decay (when the product no longer solves the core problem effectively), and messaging drift. The piece provides a diagnostic framework for founders to determine if a growth stall is an execution problem or a more fundamental PMF issue, and offers paths for correction or deliberate recalibration.
Why it matters
This provides a valuable structural analysis for founders navigating the often-ambiguous $1M-$10M ARR stage. It offers a clear, methodical way to diagnose the root cause of slowing growth, preventing the common mistake of treating a strategy problem (loss of PMF) with a tactical solution (hiring more sales reps). By breaking down the subtle signals of PMF erosion, the framework equips founders to ask the right questions and avoid compounding a fundamental misalignment with their market.
Harrison's 'Focused Chaos' newsletter piece emphasizes that founders often misdiagnose PMF decay as an execution failure. The diagnostic assessment helps differentiate between customer-definition, product-clarity, and distribution problems. The proposed solutions involve either correcting accidental drift to restore the original PMF, or deliberately recalibrating the entire business model to find a new fit.
On Tuesday, Next Net and Sundial Media & Technology Group launched the Standardized Agentic Intelligence Ledger (SAIL), a new framework built with NVIDIA to help AI systems access, attribute, and compensate premium content creators. The initiative, which includes partners like ESSENCE and Refinery29, aims to give publishers control over how their content is used by AI models and ensure fair payment, while also allowing them to bake in their own editorial judgment and cultural standards to prevent misinterpretation by AI.
Why it matters
SAIL offers a potential third path for publishers beyond either blocking AI crawlers or engaging in one-off licensing deals. By creating a standardized ledger for content usage and payment, it attempts to build a scalable, market-based solution to the content monetization crisis facing creators and publishers in the AI era. For the creator economy, this is a critical experiment in building distribution and monetization mechanics that respect provenance and value, moving from a parasitic relationship with AI to a symbiotic one.
AdExchanger notes this initiative gives publishers a 'say' in how AI uses their content, addressing concerns about cultural nuance being lost. MarTech Series reports that the framework is built on NVIDIA's accelerated computing platform, suggesting a focus on scalable, high-performance integration. This provides a collaborative model where publishers can participate in the AI economy rather than just being consumed by it.
Following the recent rollout of its 'Zero Trust for AI Agents' framework, Anthropic plans to introduce mandatory identity verification for some use cases of its Claude AI. The process will require users to provide a government-issued ID and a facial selfie via the third-party service Persona, sparking a debate about the trade-offs between mitigating misuse and creating new risks related to centralized surveillance.
Why it matters
This is a significant step in the evolution of AI governance, moving from API key management to verifying the human user behind the agent. It establishes a direct link between a person's legal identity and their AI interactions, creating a strong deterrent against misuse but also raising significant privacy concerns. This sets a precedent for how AI platforms might manage high-stakes or sensitive capabilities, potentially bifurcating users into verified and unverified tiers and forcing a choice between privacy and access. It directly intersects with the need for verifiable identity but implements it in a centralized, personally-identifiable way.
Supporters argue that requiring ID is a necessary step to prevent AI from being used for malicious activities like generating disinformation or performing illegal tasks. Critics, however, warn about the creation of massive, centralized databases of user information tied to their AI activity, the potential for data breaches, and the risk of normalizing surveillance as a prerequisite for accessing powerful technology.
An expert panel advising the European Commission on online child safety has recommended against using biometric age checks for social media access. Instead, the panel favors privacy-preserving methods like zero-knowledge proofs (ZKPs) to enforce a proposed EU-wide minimum age of 13. This approach would allow platforms to verify a user's age without accessing or storing personal data like identity documents.
Why it matters
This is a major policy endorsement for ZK-proofs in a large-scale, practical application, signaling a significant regulatory preference for cryptographic verification over invasive data collection. For builders in the ZK and identity space, this recommendation from an EU advisory body is a powerful tailwind, potentially establishing a large, mandated market for privacy-preserving identity solutions. It validates the core premise of ZK tech: the ability to prove facts without revealing the underlying data is crucial for building trustworthy digital systems.
The panel's recommendation, reported by ID Tech Wire, aligns with the architecture of the forthcoming EU Digital Identity Wallet. The move is seen as a way to balance the need for regulatory compliance with the EU's strong data protection principles under GDPR, setting a potential global standard for how to handle age verification online.
On Wednesday, Latent Labs launched Latent-Y, an AI drug design agent, making it available to researchers worldwide with free daily access and on-demand credits for larger projects. The platform enables scientists, even those without computational expertise, to design novel antibodies, nanobodies, and macrocycles using natural language prompts. The company reports that its platform has already demonstrated high success rates in lab-validated designs.
Why it matters
The broad, free-tier accessibility of a powerful, validated AI drug design tool like Latent-Y represents a significant step in democratizing drug discovery. This could act as a potent funding and acceleration mechanism for decentralized science (DeSci) and academic labs, enabling them to compete with large pharmaceutical companies by dramatically lowering the initial cost and expertise required for early-stage therapeutic design. It shifts the bottleneck from initial molecule design to validation and clinical trials.
VentureBeat reports that Latent-Y aims to compress drug design workflows from months to days. This launch comes as institutional capital floods into AI-for-drug-discovery startups like Chai Discovery, which recently raised $400M. Latent Labs' distribution model, however, focuses on broad accessibility, potentially fostering a wider base of innovation outside of heavily capitalized ventures.
Malaysia's Home Affairs Ministry has launched an investigation into the Network School, a tech co-living community in Forest City founded by ex-Coinbase CTO Balaji Srinivasan. The probe was triggered by social media allegations that Israeli nationals were circumventing Malaysia's strict immigration ban by using second-country passports to join the community. On Wednesday, immigration officials confirmed that the 266 residents checked so far possessed valid travel documents, though deeper scrutiny continues.
Why it matters
This incident puts the abstract concept of a 'network state' into direct conflict with the concrete realities of national sovereignty, immigration law, and geopolitical sensitivities. The investigation by Malaysian authorities serves as a real-world stress test for intentional communities operating globally. The outcome, which so far appears to favor the community's legal standing, will set an important precedent for how host nations regulate such experiments, particularly when they touch upon sensitive foreign policy issues.
According to The Straits Times, the probe aims to verify identities and compliance with local regulations. Reuters reported the initial clearance from the Immigration Department, stating residents had valid documents. However, critics, as noted by Sinar Daily, have linked the controversy to 'Tech Zionism,' highlighting the ideological tensions at play. Founder Balaji Srinivasan had previously stated Malaysia was chosen for its stability and quality of life as Silicon Valley decentralizes.
Agentic AI's Trust Layer Attracts Enterprise Incumbents Major security and identity firms are launching dedicated initiatives to build the governance infrastructure for autonomous AI agents. Entrust's 'Agentic AI Trust Accelerator' and Google's 'Agentic Resource Discovery' specification are designed to provide the verifiable identity, authorization, and accountability layers needed to move agents from pilots to secure production environments.
Venture Capital Consolidates into Megafunds and AI Infrastructure New H1 2026 data confirms a powerful market trend: venture capital is concentrating in megafunds (over $1B), which now account for 72% of all deal value. This capital is flowing into a narrow set of late-stage, capital-intensive AI infrastructure, defense, and deep tech companies, creating a 'winner-take-most' dynamic and making it harder for early-stage founders outside these domains to secure funding.
Ethereum Ecosystem Spins Out Commercial Privacy Solutions for Institutions Following the Ethereum Foundation's restructuring, former members have launched EthSystems, a for-profit company focused on providing ZK-based privacy and confidentiality solutions for banks and asset managers. This move, along with the recent launches of 'Ethereum Institutional' and 'Ethlabs,' signals a strategic commercialization of the ecosystem's expertise to capture institutional finance.
Prediction Markets Expand Distribution via Mainstream Platform Integrations Prediction market platforms are shifting from standalone destinations to embedded layers within established financial apps. Blockchain.com's integration of Polymarket allows its 43 million users to trade on event outcomes directly, bypassing separate onboarding and funding. This strategy aims to increase liquidity and mainstream adoption by meeting users where their assets already are.
B2B Outreach Playbooks Evolve as AI Saturates Channels With studies showing over 60% of LinkedIn content may be AI-generated, B2B GTM is shifting. Successful playbooks now emphasize automating operational drag with multi-agent systems and terminal-based orchestration, while focusing human effort on high-conviction, signal-based outreach and building verifiable relationship intelligence.
What to Expect
2026-07-17—US Senate hearing on the Digital Asset Market Clarity Act, which aims to provide a regulatory framework for digital assets.
2026-07-22—Dr. Andes Lai speaks at the Global Health Sri Lanka Conference on the shift to longevity-focused healthcare.
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