Digital Master Channel @DigitalMasterCh

In the second episode of our four-part Future of Finance series, @Wharton's Itay Goldstein speaks with @bankofengland's Sarah Breeden about the risks AI could pose to financial markets and how regulators can support innovation while maintaining stability: whr.tn/4yMBy8W

In Anthropic CEO Dario Amodei’s recent essay on advocating for the slowdown of AI, he turns to the concept of “embedded evaluators” as one way to moderate the risks associated with this powerful technology. But what is an embedded evaluator? And would it work? HKS's @StephenLCasper explains what evaluators aim to do, what transparency they need, and what real accountability looks like.

What Is #AI Agent Memory? Short-Term, Long-Term, Episodic & Semantic Memory Explained buff.ly/PveS50e @UniteAi Cc @jblefevre60 @sallyeaves @AkwyZ @ahier @rvp @gvalan @HaroldSinnott

Your AI agents are making decisions your security team doesn't know about. Buying software. Accessing systems. Moving data. All autonomously. All without a human in the loop. 48% of security pros say agentic AI will be the #1 enterprise attack vector by end of 2026. We built zero trust for humans. Nobody secured the bots. If you haven't run a tabletop exercise with an AI agent as the threat actor, you're already behind. This is the new perimeter.

It's happening. 👀 See you tonight. #UiPathFUSION

You have heard that AI consumes energy. Fine. But what exactly is using that energy while you type a prompt and wait for the answer? What you see on screen is only the result, so the energy behind it goes beyond the few seconds spent generating the answer. Microblog @antgrasso There is no single energy cost hiding inside an AI answer. Some electricity is already being used before your prompt arrives. The infrastructure may be powered and ready to serve requests. Then you press Enter. The model has to process your prompt before it can answer. Longer inputs, especially when they include a lot of context, can require more computation. Then comes generation. The model produces the response step by step, using computation as the output grows. Longer responses generally require more work. The model is not working alone. Memory and networking keep data moving through the system, while cooling uses electricity as the hardware runs. Which part uses the most energy? There is no fixed answer. It depends on the model and the workload. Model size, prompt and response length, modality, and infrastructure efficiency can all change the total. That is why saying “one AI prompt consumes X” can hide more than it explains. The energy is in the whole slice. Like a cheesecake, you enjoy the result without necessarily thinking about what went into every layer. #ArtificialIntelligence #EnergyEfficiency

📊 Is digital advertising still a “free market”… or a concentrated game? The reality is striking: 👉 Just 3 platforms generate over 50% of global ad revenue 👉 In some markets, they capture up to 90% of ad spend 👉 And the industry? It has already surpassed $1 trillion

"Accounting really matters. If we do don't account for something, we don't value it." HKS's @LJBilmes has spent decades putting a price tag on the cost of war. Her upcoming book, "The Ghost Budget," reckons with the complexities that come with the American military's multi-billion dollar budget. ken.sc/4Aqvnt9

“The window is open, but you have to embrace it now,” @ArvindKrishna, @IBM CEO on the opportunities with AI in business growth. Companies effectively leveraging AI have a significant advantage. The gap between companies leveraging AI is growing fast - those that don’t adopt it now will fall behind. #Think2026

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Novartis, Ionis drug failure spurs questions cc @Nanoappsm @cellrepair777 @tantriclens #NanoApps #Research #SDGs #Novartis #DrugResearchFailure nanoappsmedical.com/novartis-ionis…

Today, D-Wave announced the appointment of veteran financial technology executive Bernard Gavgani to its Board of Directors and Cybersecurity Committee. Gavgani currently serves as Senior Advisor for Technology and Innovation to BNP Paribas Group Executive Management and previously served as Group Chief Information Officer (CIO) of @BNPParibas. He joins D-Wave's Board in a personal capacity and brings deep experience in global technology strategy, operational transformation, artificial intelligence governance, cybersecurity, and workplace and network modernization. Read the press release here: dwavequantum.com/company/newsro…

The biggest mistake people make in life is not trying to make a living at doing what they enjoy most. -Malcolm Forbes

Deel links adding >$140M in ARR over 90 days without increasing headcount to its internal use of Akai. What interests me is how Akai learns the path through a workflow, including edge cases, and turns it into something teams can reuse and adapt.

Alex Bouaziz @Bouazizalex

a day ago

EXCITED TO LAUNCH: Akai (akai.run) Deel added >$140M ARR in 90 days without increasing headcount by automating~600 Full Time Employees' equivalent in work with Akai. Akai was an internal tool to automate our painfully repetitive operations in Finance, HR,

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If you are making room on your desk for a quantum computer, stop. You do not need one there: quantum computing is a specialist, not a faster PC. Use it when a problem may benefit from a different way of computing, not just because you want more speed. Microblog by @antgrasso Most of the work will still be done by classical computers. Take medical research. A classical system can manage the data and the workflow, while a quantum processor is assigned a molecular simulation that may benefit from quantum computation. The result then returns to the classical workflow. Hybrid approaches like this are already being explored in drug discovery and biological research. Weather research gives another example. Forecasting still depends on classical computing, but researchers are testing quantum-classical methods for parts of the process such as data assimilation. The quantum processor tackles a specific task; it does not replace the forecasting system around it. In a hybrid system, classical computing keeps doing what it already does well. Quantum computation enters where its different way of computing may offer an advantage. So the question is not: when will a quantum computer replace the machine on my desk? A better question is: which part of a problem might justify calling in a quantum specialist? Quantum computing is not the next PC. It is another kind of compute for specific jobs. #QuantumComputing #HybridComputing

AI can review thousands of lines of code in minutes. But software quality is still a human responsibility. Follow @LindaGrass0 for more insights 💡

how iot works

A1) customer segmentation is really risky with trends and purchasing behaviors changing so rapidly today. Not to mention online behaviors with the possibility of purchases being for a family member or friend. Who are you serving and why? Who are they buying for and why? #custserv

Marsha Collier @MarshaCollier

2 weeks ago

Q1 | #custserv Do you respond to - or even segment - customers by generation? If yes, why? If not, why not?

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SK Hynix has already sold out its entire 2026 HBM chip output. Every unit. Gone. DRAM supplier inventories fell from 13-17 weeks of supply in late 2024 down to 2-4 weeks by October 2025. The chips that power AI are being pre-sold years in advance and supply is tightening by the quarter. This isn't a supply chain blip. This is structural scarcity at the foundation of the AI race. Every hyperscaler including Microsoft, Google, Amazon, Meta are competing for the same limited pool of HBM. The companies that locked in contracts early have a real infrastructure advantage over those scrambling to find allocation now. I've been watching enterprise AI deployments slow down not because of budget constraints or lack of use cases, but because the hardware isn't available. That's a new constraint most C-suite leaders didn't see coming 18 months ago. The AI infrastructure race has a raw materials problem. And it's not going away anytime soon.

New injectable treatment helps the brain rebuild after stroke cc @Nanoappsm @cellrepair777 @tantriclens #NanoApps #Research #SDGs #BrainRecovery #HealthTreatement #HealthTech #MedTech #brainstroke nanoappsmedical.com/new-injectable…

Collaborative robots are moving automation from isolated cells into daily production activities beside human operators. Factories adopting cobots reorganize safety procedures and line management to gain steadier execution with less physical strain on teams. Rt @antgrasso.