Anna E. Molosky

After @Alchemy’s CoBuild, one thing is clear: the agentic economy for institutional finance is here. 150 execs from JP Morgan, Mastercard, Coinbase, Pantera & beyond all in one room mapping agentic AI + the future of onchain finance. @nikil and the team launched AgentPay live: protocol-agnostic payments so that agents can pay any merchant, anywhere. This is the infrastructure layer that turns pilots into enterprise revenue at a global scale. The momentum here is unmatched. This is exactly how we turn it into the revenue engine for the agentic economy. Who else is ready for this wave of institutional adoption? 🚀 @Alchemy

Standout conversations from @Alchemy CoBuild: @nikil + @thejoelau announcing the launch of AgentPay + AgentCard (@agentcardai) @danmorehead (Pantera Capital) on the next decade of AI + crypto + the macro impacts Bill Platt (Alchemy COO) moderating the future of payments + the underlying infra stack @nikil + @shayne_coplan on prediction markets as the thermometer for truth I enjoyed the thoughtful discussions with the team, including @gponcin (Alchemy’s CTO).

After @Alchemy’s CoBuild, one thing is clear: the agentic economy for institutional finance is here. 150 execs from JP Morgan, Mastercard, Coinbase, Pantera & beyond all in one room mapping agentic AI + the future of onchain finance. @nikil and the team launched AgentPay live: protocol-agnostic payments so that agents can pay any merchant, anywhere. This is the infrastructure layer that turns pilots into enterprise revenue at a global scale. The momentum here is unmatched. This is exactly how we turn it into the revenue engine for the agentic economy. Who else is ready for this wave of institutional adoption? 🚀 @Alchemy

Proud to be an alum of @CarnegieMellon, where interdisciplinary excellence and philanthropy have been at the heart of its work since 1900. #AI #Innovation

It’s refreshing to see that companies are increasingly using AI agents—like @GoogleDeepMind’s CodeMender—to increase security. #AI #Innovation #TechTrends

Read the full report: bit.ly/3Ib9MhY Additional organizations driving results with #AI: @DeutscheBank, @commerzbank, @atbfinancial, @BayerUS, @Bayer, @dasa_org, Drive Health, @globo, Golden State @warriors, @Morrisons, @indosat, @GlobeTelecomPH, @AlaskaAir, @Radisson Hotel Group, and @TrimbleCorpNews.

𝗪𝗵𝗮𝘁 𝗱𝗶𝗱 @Google 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗮𝘀𝗸𝗶𝗻𝗴 𝟯,𝟱𝟬𝟬 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗮𝘁 𝗴𝗹𝗼𝗯𝗮𝗹 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀, “𝙄𝙨 𝗔𝗜 𝗺𝗮𝗸𝗶𝗻𝗴 𝙮𝙤𝙪𝙧 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝙖𝙣𝙮 𝗺𝗼𝗻𝗲𝘆?”  Interested in turning AI into ROI? Keep reading. Below, I have summarized and enriched @googlecloud’s new report on the business impact of AI and how AI Agents (Agentic AI) are unlocking the next wave of AI-driven revenues, efficiency, and more. BUSINESS IMPACT OF AI As a direct result of generative AI (GenAI) and/or Agentic AI initiatives deployed, executives surveyed reported: ⬆️ 𝗥𝗢𝗜 (Agentic AI): 𝟴𝟴% of early adopters reported positive ROI within the first year ⬆️ 𝗥𝗢𝗜 (GenAI): 𝟳𝟰% reported ROI within the first year ⬆️ 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 (Both): 𝟱𝟯% noted increased revenue from AI (+6% to +10% Y-o-Y) KEY INSIGHTS 1️⃣ 𝟮𝟬𝟮𝟱 𝗚𝗲𝗻𝗔𝗜 𝗜𝗺𝗽𝗮𝗰𝘁 𝗛𝗼𝘁𝘀𝗽𝗼𝘁𝘀: Employee productivity (+70%), Customer Experience (+63%), Business Growth (+56%), Marketing (+55%), and Security Ops & Cybersecurity (+49%) 2️⃣ 𝗔𝗜 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲: 52% of executives whose organizations use GenAI have adopted AI Agents in production 3️⃣ 𝗔𝗜 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗔𝗰𝗿𝗼𝘀𝘀 𝗗𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁𝘀 ➜ 𝗥𝗢𝗜: 39% of executives saw ROI on GenAI use cases 4️⃣ 𝗗𝗮𝘁𝗮 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗖𝗼𝗺𝗲 𝗙𝗶𝗿𝘀𝘁: Data privacy and security are the top considerations when evaluating AI model providers 5️⃣ 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗕𝗮𝗰𝗸𝗶𝗻𝗴 ➜ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀: 78% of leaders with C-level AI sponsorship report seeing ROI on one or more GenAI use case(s) INDUSTRY INSIGHTS: AGENTIC AI The top Agentic AI use cases vary by industry: 🔹 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 & 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Financial Services; Retail & CPG; Manufacturing & Automotive 🔹𝗧𝗲𝗰𝗵 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Healthcare & Life Sciences; Public Sector 🔹 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 & 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Telecom; Media & Entertainment ENTERPRISE SUCCESS 🔹 @GeneralMills: $100ᴍᴍ+ saved with internal GenAI Q&A tools 🔹 @Wayfair: 99.7% decrease in GTM time for new features (1 year ➜ 1 day) 🔹 @seattlechildren’s: 50% reduction in monthly hospital length-of-stay per patient @seattlechildren ______ The most significant financial wins come from building AI Agents that can automate repeatable tasks to deliver clear ROI, especially in the areas of employee productivity and customer experience. To successfully deliver positive impact at scale, enterprise leaders must fund AI explicitly (including workforce upskilling), codify governance early on, and securely integrate AI Agents into core systems. ______ #AI #Innovation #artificalintelligence #TechNews Thanks to @neilhoyne for sharing the full report.

Midsize Enterprise CXOs: gain insight into deploying and adopting 45 emerging technologies across six technology domains. @Gartner_inc #ArtificialIntelligence #AI #Innovation

𝗪𝗵𝗮𝘁 𝗱𝗶𝗱 @Google 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗮𝘀𝗸𝗶𝗻𝗴 𝟯,𝟱𝟬𝟬 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀, “𝗪𝗵𝗲𝗿𝗲 𝗶𝘀 𝗔𝗜 𝗺𝗮𝗸𝗶𝗻𝗴 𝘆𝗼𝘂 𝗺𝗼𝗻𝗲𝘆?” If you’re curious about what’s generating returns in AI right now, check out the slides below. 𝗞𝗘𝗬 𝗜𝗡𝗦𝗜𝗚𝗛𝗧𝗦 1️⃣ Executives are seeing ROI on ≥1 GenAI use cases, especially 88% of early adopters of Agentic AI 2️⃣Data privacy and security are the #1 consideration for companies when evaluating LLM providers 3️⃣Google Cloud’s playbook for deploying AI Agents that deliver positive ROI is on Page 4 #AI #ArtificialIntelligence #Innovation –––––––– 𝘛𝘩𝘢𝘯𝘬𝘴 𝘵𝘰 @neilhoyne 𝘧𝘰𝘳 𝘴𝘩𝘢𝘳𝘪𝘯𝘨 𝘵𝘩𝘦 𝘧𝘶𝘭𝘭 48-𝘱𝘢𝘨𝘦 𝘳𝘦𝘱𝘰𝘳𝘵, 𝘸𝘩𝘪𝘤𝘩 𝘪𝘴 𝘦𝘯𝘵𝘪𝘳𝘦𝘭𝘺 𝘵𝘩𝘦 𝘱𝘳𝘰𝘱𝘦𝘳𝘵𝘺 𝘰𝘧 𝘎𝘰𝘰𝘨𝘭𝘦 𝘢𝘯𝘥 𝘎𝘰𝘰𝘨𝘭𝘦 𝘢𝘭𝘰𝘯𝘦.

𝗧𝗵𝗲 “𝟵𝟱% 𝗼𝗳 𝗚𝗲𝗻𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀 𝗳𝗮𝗶𝗹” 𝗵𝗲𝗮𝗱𝗹𝗶𝗻𝗲 𝗰𝗼𝗻𝗳𝘂𝘀𝗲𝘀 𝗻𝗼𝗶𝘀𝗲 𝗳𝗼𝗿 𝘀𝗶𝗴𝗻𝗮𝗹. 🏆 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗼𝗽 𝟱% 𝘂𝘀𝗲 𝘁𝗵𝗿𝗲𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝘁𝗼 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗚𝗲𝗻𝗔𝗜 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁𝘀 𝗶𝗻𝘁𝗼 𝗽𝗼𝘀𝗶𝘁𝗶𝘃𝗲 𝗔𝗜 𝗥𝗢𝗜: 1️⃣ REALLOCATE EARLY AI SPEND TO BACK-OFFICE AUTOMATION Executives, on average, allocate 𝟱𝟬% of their GenAI budget to Sales and Marketing according to MIT’s 𝘎𝘦𝘯𝘈𝘐 𝘋𝘪𝘷𝘪𝘥𝘦 2025 study¹. However, back-office process automation yields the clearest near-term ROI.  Target process-specific automations and integrate robust AI solutions—not all of which require GenAI—with existing systems to reduce spend and minimize business process outsourcing. For example, @ATT saved 16.9 million minutes of manual effort per year, recognized “hundreds of millions of dollars in annualized value”, and delivered 𝟮𝟬𝘅 𝗥𝗢𝗜 by scaling an enterprise-wide AI automation program across finance and operations. 2️⃣ LEVERAGE EMPLOYEES’ APPETITE FOR AI EFFICIENCY A thriving “shadow AI economy” exists within enterprises; 𝟵𝟬% of employees surveyed use personal AI subscriptions—such as @OpenAI’s ChatGPT or @AnthropicAI’s Claude—to automate significant portions of their daily work. However, 𝗼𝗻𝗹𝘆 𝟰𝟬% of companies surveyed distribute LLM subscriptions to employees. For AI success at scale, determine which tasks employees already automate, prioritize based on the value added before procuring (or building) new AI tools, and then embed the highest-value workflows into robust, customizable, enterprise-grade tools. 3️⃣ SET REALISTIC TIMELINES FOR ENTERPRISE AI PROJECTS Major technology deployments within multinational organizations typically span 𝟭 𝘁𝗼 𝟯 𝘆𝗲𝗮𝗿𝘀, depending on the scope and depth of integration. The study’s 𝟲-𝗺𝗼𝗻𝘁𝗵 observation window is insufficient to project long-term AI ROI for enterprise-wide deployments. Recalibrate and set organization-wide expectations for the speed of enterprise deployment. _____ Reframe the “95% failure²” statistic as a maturity signal for the 5% that have achieved positive AI ROI, not a stop sign for the 95% yet to realize AI ROI. To maximize enterprise P&L impact and AI ROI, shift early-stage AI spend to back-office automations, analyze shadow-AI usage to identify high-value use cases, and set realistic expectations for enterprise deployment velocity. _____ #ArtificialIntelligence #AIInnovation #AIStrategy ¹ Challapally, Aditya, et al. 𝘛𝘩𝘦 𝘎𝘦𝘯𝘈𝘐 𝘋𝘪𝘷𝘪𝘥𝘦. @medialab Massachusetts Institute of Technology Media Lab, Project NANDA, July 2025. ² Failed AI investments, as defined by the MIT study and confirmed by The @NewYorker, are projects that were not deployed beyond the pilot stage and/or those without measurable return or P&L impact within six months.

𝗧𝗵𝗲 “𝟵𝟱% 𝗼𝗳 𝗚𝗲𝗻𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀 𝗳𝗮𝗶𝗹” 𝗵𝗲𝗮𝗱𝗹𝗶𝗻𝗲¹ 𝗶𝘀 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗶𝗻 𝘁𝗵𝗲 𝟱% 𝗿𝘂𝗻 𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝘁𝗼 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗔𝗜 𝗥𝗢𝗜. 📈 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄, per @MIT’s 𝘛𝘩𝘦 𝘎𝘦𝘯𝘈𝘐 𝘋𝘪𝘷𝘪𝘥𝘦 2025 study: 1️⃣ REALLOCATE EARLY AI SPEND TO BACK-OFFICE AUTOMATION Executives surveyed allocate 50% of GenAI budgets to Sales and Marketing on average; however, back-office automation yields the clearest, near-term ROI. Target process-specific operational automations and integrate robust solutions with existing systems to reduce spend and minimize business process outsourcing. 2️⃣ LEVERAGE EMPLOYEES’ STRONG APPETITE FOR AI EFFICIENCY A thriving “shadow AI economy” exists within enterprises; 90% of employees surveyed use personal AI subscriptions—such as @OpenAI’s ChatGPT or @AnthropicAI’s Claude—to automate significant portions of their daily work. However, only 40% of companies surveyed have enterprise LLM subscriptions. For AI success at scale, determine which tasks employees already automate, prioritize based on the value added before procuring (or building) new AI tools, and then embed the highest-value workflows into robust, customizable, enterprise-grade tools. 3️⃣ SET REALISTIC TIMELINES FOR ENTERPRISE TECHNOLOGY INTEGRATION Major technology deployments within multinational organizations typically span 1 to 3 years, depending on the scope and depth of integration. The study’s 6-month observation window is likely insufficient to project long-term AI ROI for enterprise-wide deployments. Recalibrate org-wide expectations for the speed of enterprise deployment. _____ Reframe the “95% failure” statistic as a maturity signal, not a stop sign. To maximize enterprise P&L impact and AI ROI, shift early-stage AI spend to back-office automations, analyze shadow-AI usage to identify high-value use cases, and set realistic expectations for enterprise deployment velocity. #ArtificialIntelligence #AI #AIStrategy #GenerativeAI #Leadership ¹Original study: Challapally, Aditya, et al. The GenAI Divide: State of AI in Business 2025. @MIT Media Lab, Project NANDA, July 2025.

𝗛𝗼𝘄 𝗱𝗼 @Upstart 𝗮𝗻𝗱 @canva 𝗺𝗮𝘅𝗶𝗺𝗶𝘇𝗲 𝗔𝗜 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘃𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝘄𝗵𝗶𝗹𝗲 𝗰𝘂𝘁𝘁𝗶𝗻𝗴 𝗰𝗹𝗼𝘂𝗱 𝘀𝗽𝗲𝗻𝗱? They automate cloud-rate savings and translate cloud spend into unit economics—so margins increase with usage. 📈 @Gartner_inc projects Generative AI (GenAI) spend to reach ≈ $644Bɴ by the end of 2025 (an increase of 76.4% Y-o-Y).  If organizations fail to manage cloud costs like profit and loss (P&L) levers, margins will shrink. 🚀 𝗖𝗟𝗢𝗨𝗗 𝗖𝗢𝗦𝗧 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗟𝗘𝗩𝗘𝗥𝗦 1️⃣ LOCK IN CLOUD RATES → STABILIZE & EXPAND GROSS MARGINS Establish a baseline for your organization’s usage and spend with cloud-native tools from AWS, @Google Cloud, and @Microsoft @Azure. Convert volatile on-demand spend into contracted rates with Savings Plans (SPs) from AWS or Azure, Committed Use Discounts from Google Cloud, and Reserved Instances (RIs) from all three hyperscalers.  ◆ 🛠️ Cloud Tools: @prosperopshq and @nopsio (automated RI/SP coverage ◆ 🎯 KPIs and Targets: Coverage: 85%–95%; Effective Savings Rate (ESR) ≥ 40% 2️⃣ AUTOMATE CLOUD OPS → OPTIMIZE VARIABLE SPEND Cloud compute demand varies drastically among workloads; #GenAI workloads require significant resources. Integrate automation tools to transform best engineering practices—such as rightsizing, autoscaling, and off-hours shutdown—into a durable, long-term mechanism that reduces compute and storage costs with minimal operational overhead. ◆ 🛠️ Tools: @spot_flexera (manages RI usage at scale); @nvidia / @NVIDIAAI Run:ai (GPU utilization for #AI training) ◆ 🎯 KPIs: Utilization: 65%–75%+, Idle Time: <5%, 3️⃣ UNIT ECONOMICS → INCREASE MARGINS & FREE CASH FLOW (FCF) Visibility and unit cost breakdowns enable leaders to see margins by product line, customer segment, or feature—not just an opaque multimillion-dollar invoice. Map infrastructure spend to units of value—per 1k tokens, per transaction, and per customer—then allocate spend and prioritize accordingly. ◆ 🛠️ Tools: @CloudZeroInc (cloud bills → unit economics); @flexera (hybrid architectures) ◆ 🎯 KPIs: Cloud COGS % (cloud spend/revenue); Forecasting Accuracy (+5%–10%); FCF 🏆 ENTERPRISE RESULTS ◆ Upstart: $20ᴍᴍ reduction in cloud spend; AI delivery velocity maintained ◆ Canva: $3ᴍᴍ+/yr savings on cloud storage; cut compute costs by 46% while shipping new GenAI features ——— 💡 Cloud spend and resource optimization 𝗿𝗲𝗱𝘂𝗰𝗲𝘀 𝗖𝗢𝗚𝗦, 𝗲𝘅𝗽𝗮𝗻𝗱𝘀 𝗺𝗮𝗿𝗴𝗶𝗻𝘀, 𝗮𝗻𝗱 𝘂𝗽𝗹𝗶𝗳𝘁𝘀 𝗙𝗖𝗙. As AI drives unprecedented demand for cloud infrastructure, executives who prioritize AI cloud spend optimization will set the pace. #ArtificialIntelligence #Innovation #Leadership #BusinessStrategy

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Your cloud bill increased (again). Did your margins? 📈 @Upstart, @canva, and I use a proven 3-lever AI ROI strategy to scale the *right* figures → COGS↓, Margin↑, FCF↑ Discover how: bit.ly/47LTBls #AI #Leadership #CloudComputing

Driving successful AI initiatives != running traditional software projects. Instead, market leaders @Google and @Microsoft maximize innovation and ROI with the dual AI Foundaries and AI Factories framework. 🚀 Learn how: bit.ly/4mGOasA #AI #GenerativeAI #DigitalTransformation