Stu Frost

New release from Packt Publishing… “Building Business-Ready Generative #AI Systems — Build Human-Centered Generative AI Systems with Context-Aware Agents, Memory, and LLMs for the Enterprise” See it at https://amzn.to/3Jdcio5 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🔵Build an adaptive, context-aware AI controller with advanced memory strategies 🟢Enhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT) 🟠Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fit

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Great talk with Rick Perry, Fermi America, and Mike Rowe, mikeroweWORKS Foundation. "FERMI will be a monument to the American trades and to American ingenuity." The future of artificial intelligence and BUILDING in the Physical World. NVIDIA Jensen Huang states Autonomy will drive the largest economy over the next 100+ years and impact the $100 trillion physical world. https://lnkd.in/exXzcJSt INTELLIGENT INFRASTRUCTURE will be our generation's Interstate Highway - Building a Stronger and More Connected Nation. https://lnkd.in/gNiA4WqR A national revival is ramping up in the United States, and the trades will be the critical leaders. Mike Rowe, mikeroweWORKS Foundation, is a national advocate for closing the WILL-GAP by bringing back the respect and importance of Community colleges, trade schools, apprenticeship programs, mentoring, and small businesses. https://lnkd.in/g56UQ4gE President Trump secured the private sector to invest in the largest AI refinery. The next step is deploying Intelligent Infrastructure Economic Zones in every community nationwide, urban and rural, to leverage the high-octane DATA.  Providing AI inference in the physical world, enabling Spatial Computing and Autonomous Systems. https://lnkd.in/gVVNZtXa INTELLIGENT INFRASTRUCTURE for America: https://lnkd.in/gqFhd-K9 Autonomy Institute https://lnkd.in/gzvBhWsV Building Our Greatest Tomorrow https://lnkd.in/gwv2pnXe

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We live in an era of manufactured growth. OpenAI now sits at the center of a fragile AI economy built around circular financing and valuation distortion. The last time we saw something similar was in 2000 that led to the collapse of Lucent and Cisco Background: In 1997, Lucent and Cisco discovered a clever trick to sell more routers and switches during the internet boom They began lending to their own customers (vendor financing) This resulted in massive short term growth, until the bubble burst When customers defaulted Lucent and Cisco were left with bad debt. As a result - Lucent’s stock fell 99% and CIsco’s 80% What was once being cited as business innovation was suddenly seen as a ponzi scheme Fast forward to 2025. OpenAI playbook looks eerily similar Hidden Revenue Loops that nobody is talking about : 1. Microsoft <> OpenAI : Microsoft owns 30% of OpenAI and almost half of OpenAI’s costs are on Microsoft Azure. Every invested dollar cycles back as revenues 2. Anthropic <> AWS : Amazon invested $4B into Anthropic and Anthropic committed to AWS as it’s primary cloud provider. Anthropic’s spending boosts AWS revenues - a closed loop growth flywheel 3. NVidia <> OpenAI : Nvidia pledges $100B to OpenAI so OpenAI can build 10 GW of data centers. Every $10B invested by Nvidia generates $35B in GPU sales - another self financed growth loop masqueraded as organic demand 4. AMD <> OpenAI : OpenAI agreed to buying AMD chips and received warrants equal to 10% in AMD shares. As AMD’s stocks rise due to new revenues from OpenAI, OpenAI’s paper value inflates - valuation distortion in action 5. Oracle <> OpenAI : OpenAI signs a $300B cloud deal with Oracle. Oracle buys Nvidia chips to power those data centers. Nvidia invests back into OpenAI. Capital -> Chips -> Cloud -> Capital. The manufactured growth loop completes Demand Domino Effect: If any one node falters, the illusion of infinite demand will collapse OpenAI is positioned to be the "epicenter" of any future AI crisis If AI bubble bursts and the demand for token declines, - OpenAI & Anthropic will face down rounds - Nvidia and AMD face inventory gluts - AWS, Azure, Oracle end up with under utilized infrastructure Takeaways: Our systems and institutions rarely handle checks and balances well Every generation comes up with their own language to justify excess and hype In 1999: It was the eyeballs and internet adoption In 2008, it was credit innovation and sub prime lending In 2026, it will be compute, AI and infrastructure spending    When capital moves in controlled loops, it creates the illusion of infinite growth and perpetual demand Until reality kicks in, leading to a crash However, It’s important to remember that crashes don’t kill innovation; they expose our limitations The dot-com bust didn’t end the internet; it made it stronger If and when the AI crash happens, it will not end AI Instead, it will give rise to some of the best AI companies of the future

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Over the holiday break, I spent some time revisiting some of the foundational work shaping the field of scientific AI. One paper that stands out—published back in 2021—was co-authored by Damien Guého, now a Senior R&D Engineer at Geminus. The paper tackles a timeless challenge: how do you uncover the true physics of a system when all you have is sparse, noisy, imperfect data? Damien and his co-author's approach is both clever and elegant. Instead of a brute force deep learning strategy, they leveraged clever math to uncover a physics-grounded structure with interpretable dynamics—from messy, nonlinear signals. The result is a robust, reliable and generalizable solution that uses a fraction of the computational effort. They first solved the problem using a deep learning approach that required 7,720 parameters. They then applied their method that used just 3 terms and was 10,000× more accurate. This mindset is core to Geminus' mission to simplify engineering and operational decision making in complex systems where uncertainty abounds and data is sparse and messy. Work like that of Damien and his colleagues continues to inspire where we’re headed next. Proud of the team at Geminus Labs—and excited for what’s coming. Check out his paper here. https://lnkd.in/eHt-5tXX Watch this space - be sure to follow Geminus to be part of the conversation.

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Why should Active Metadata Tags be the backbone of your AI strategy? Active Metadata Tags (AMTs) are used for automatic enrichment of metadata. Euno supports two types of custom metadata enrichment capabilities, both of which generate AMTs: #1 Extract structured metadata from unstructured metadata. This involved parsing metadata from elements like code and descriptions. A common application would be to enforce data modeling guardrails. Examples: → Implicit columns schema: Analyze dbt model code to determine whether the resulting schema includes implicit or explicit columns. → Verification checks: Assess naming conventions, detect use of approximate count distinct when querying large tables, evaluate documentation agility, flag full outer joins, and more. #2 Compose new AMTs. This approach combines metadata of a resource, and of any related resources, to set a new property on a resource. Examples: → Non-trivial custom SQL: Tag Tableau Custom SQL nodes that do not add logic. → Certified data source: Tag Tableau data sources as ‘certified’ if they don’t contain non-trivial custom SQL. → Certified dashboard: Tag Tableau dashboards as ‘certified’ if they only depend on certified data sources.   This capability is essential for governance workflows in large organizations where different teams maintain different parts of the stack. So team AI can build on something that team B created. Consider this scenario: → The team maintaining dbt can create a custom property to flag models for deprecation (e.g., models lacking both ‘prod’ and ‘prod_ready’ tags). → Analytics teams can then use this signal to create custom properties for their BI assets to drive workflows to migrate away from those models. Why does this also matter for AI agents? By implementing AMTs to surface trust signals, AI agents can actually tell the difference between exploratory work and official definitions. Example: → Euno’s MCP server surfaces to AI agents only Tableau logic that meets your trust standard and is considered certified such as: ‘depends only on dbt models from the gold layer and was in use in the past 60 days.’ *** Expect to hear much more about Active Metadata Tags! They’re quickly becoming the key to scale AI beyond the sandbox in your organization. #ActiveMetadata

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What are Just-In-Time Semantic Models??? My co-founder, Tal Segalov, shared a post now on a new concept Solid is pioneering: Just-In-Time Semantic Models (JIT-SM for short) The idea is simple: 1️⃣ Today, you either have one really big semantic model that you use to help AI access your data, or a lot of smaller ones (as Databricks and Snowflake recommend you do). 2️⃣ Either way, you need to maintain a ton of model information manually, and it gets stale very quickly. 3️⃣ As we announced last week, you can use Solid's platform to generate the model 💪 , but that means there are dozens or hundreds of models constantly maintained in your environment. 💥 So now, Solid can instead generate the semantic model on demand - when it is needed to generate SQL or pull certain data. This way, every request gets its own, highly-curated, model. The model can remain in the cache, or get thrown away after use. No more stale models. No more situations where models don't exactly fit your question, or your question spans multiple models (which isn't supported by the DWH's at the moment). Instead - the model your query needs is generated for it... just, in, time. 🎉 And we're starting with support for Snowflake's semantic models, and its Cortex Analyst capability. Check it out: https://lnkd.in/gJAe-gfq

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𝗧𝗵𝗲 𝟯 𝗞𝗲��� 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗵𝗮𝘁 𝗖𝘂𝘁 𝘁𝗵𝗲 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗔𝗜 𝗪𝗲𝗮𝘁𝗵𝗲𝗿 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝘀 𝗯𝘆 𝟵𝟬% Last month we held an oversubscribed webinar describing how Earthmover helped customers cut the cost of an AI-weather-model inference pipeline by 90%. We've now turned this into a short blog post summarizing our key findings. (Hint: it's all about I/O optimization.) Post link in comments. If you're doing AI for weather and environmental modeling and are not achieving full GPU utilization, your I/O is probably the culprit. Please reach out to learn how Earthmover can help modernize and optimize these workloads.

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My latest CMSWire article is an update on the latest #prompt engineering techniques for #AI, featuring a few examples on Gemini and ChatGPT. "Prompts, the key instruction for large language models, are how people talk to their AI of choice. But how do people plan what to say to their AI? The starting point is #promptengineering." Read further to learn what you can do to plan better #AI usage #Tech #data #marketing https://lnkd.in/gtVnZpJr

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Google just unveiled Gemini 3, their most intelligent and advanced model yet. It delivers state-of-the-art reasoning, deeper context understanding, and the ability to interpret even the most nuanced ideas with minimal prompting. Gemini 3 is rolling out across Search (on day one), the Gemini app, AI Studio, Google’s new agentic development platform Antigravity, and enterprise ecosystems like Vertex AI and Gemini Enterprise. In just two years, AI has gone from reading text and images to literally reading the room. Exciting times ahead for builders, innovators, and enterprises! GWC Data.AI a proud Google Cloud partner.

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Spear AI, a startup founded by U.S. Navy veterans, has raised its first round of outside capital to apply AI to submarine data. Spear AI specializes in preparing passive acoustic data (gathered by underwater listening devices) for AI algorithms. The startup's hardware and software platform helps the U.S. military and commercial clients label and sort complex acoustic data to help submarine operators understand whether an object is a whale, a rain squall, or a potential threat. The company raised $2.3 million from AI-focused venture firm Cortical Ventures and private equity firm Scare the Bear Capital. The funding will be used to double the company's headcount to support its government contracts (including a recent $6M U.S. Navy deal) and commercial business prospects, such as monitoring underwater pipelines. Congratulations to co-founders Michael Hunter and John McGunnigle, and the Spear AI team. Reuters: https://lnkd.in/dFqzEFSH #DefenseTech #AI #MaritimeSecurity #AcousticData #NationalSecurity #Funding #GovTech

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A lot of new venture capital is flowing into defense startups now. Most of it will probably be wasted. It’s generally not because the founders are bad or the tech isn't real. It’s because the standard playbook doesn't really work in this space. Hence a follow-up to my last brain-dump on building a deftech startup, explaining why: "What VCs Get Wrong About Defense." Founders will probably agree; investors might not. Thoughts? Full article here: https://lnkd.in/dmS7D6Vk

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Great read on AI strategy that features a quote from my colleague Charlie Apigian, our lead AI Strategist at LBMC . The truth is, real AI adoption doesn’t start with tools — it starts with intent. Organizations that win with AI have clarity on what they want to accomplish and a strategy that aligns their data, people, and processes to get there. At LBMC , we focus on helping companies build that strategic foundation so they can move forward with purpose instead of randomly collecting AI tools that never deliver impact. Thanks to Lisa Namm and Suzanne R. on our marketing team for helping with this article. https://lnkd.in/ev_hsC5S

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