Nelson Couvertier

On CNBC Closing Bell Overtime with Morgan L Brennan, Jon Fortt, and Michael Santoli, I weighed in on #IBM’s results. The stock is down—not because the quarter was weak, but because expectations were elevated, particularly around software. IBM’s valuation has expanded this year as investors rewarded the company for shifting toward a higher recurring revenue model—now over 50% of total revenue. But software merely met expectations, after softness in the last two quarters. The Street was looking for a beat. Business looks solid, and GenAI is gaining traction—but it’s still a small part of their overall story. The takeaway: when a stock trades at a premium, even solid results need to exceed expectations. Catch the full conversation here: https://lnkd.in/exdaxGkD #Investing #StockMarket #EarningsSeason #TechInvesting #IBM #BarbaraDoran #BD8Capital #CNBC

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In Case You Were Worried About the Timing of Coming US Recession: Try not to worry too much about the timing of the next US recession. While the loudest voices claim the recession has already begun, the Atlanta Fed GDPNOW estimates Q2 to be tracking at a 4.6% annual clip. Latest estimate: 4.6 percent — June 02, 2025 The GDPNow model estimate for real GDP growth (seasonally adjusted annual rate) in the second quarter of 2025 is 4.6 percent on June 2, up from 3.8 percent on May 30. After this morning’s releases from the US Census Bureau and the Institute for Supply Management, the nowcasts of second-quarter real personal consumption expenditures growth and real gross private domestic investment growth increased from 3.3 percent and -1.4 percent, respectively, to 4.0 percent and 0.5 percent. It is not hard to understand why the S&P had such a robust May. In my opinion, it won’t take much for S&P to have strong performance in June and Q3. Tighten seatbelts please! #GDPNOW #recessionnotyet #noJuneratecut #tomsowanick #TACOman

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🚨 The AI Capex Super-Cycle is underway. From hyperscalers revising spend higher every quarter to semis and industrials racing to keep pace, the stakes have never been bigger. We sat down with Doug O'Laughlin (SemiAnalysis) and Michelle Brophy (AlphaSense) to unpack who’s really winning across the AI ecosystem—and why it matters for investors. Watch the full webinar now: https://lnkd.in/eUat9Nyu Disclosures: *Not Investment Advice.  **I have evened out my AI exposure to META vs NVDA. (Thesis is not a call on timing the AI cycle, but a relative value way to play the company that is able to improve their ecosystem returns and customer experience vs a company that is currently generating most of the industry rent that has a history of being cyclical) #InvestmentPodcast #Webinar #InvestmentWebinar #Finance #AI #NVDA #PTPM 

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The ConnectPay Engineering & Product teams recently came together for a full-day town hall at our corporate office in Mansfield, MA and it was nothing short of energizing. What stood out most was the level of collaboration and shared focus on what really matters: building smarter, faster, and more reliable payroll solutions for small businesses. The pace at which ConnectPay is innovating across AI and the public cloud is remarkable, but more importantly, these advancements are set to meaningfully transform how small businesses manage payroll. We’re not just improving processes... we’re helping business owners save time, reduce complexity, and focus on growing their companies.

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A lot of AI initiatives look successful on paper and fail in reality. Cycle times go down. Throughput goes up. Cost per task improves. And yet the business impact is marginal. Because productivity is being measured at the task level, not the decision level. If a process runs faster but still produces inconsistent or low quality decisions, you haven’t improved performance. You’ve scaled noise. This is where many organizations get misled. Early metrics show progress, so more investment follows. But the underlying decision logic hasn’t improved, so the gains plateau quickly. The constraint isn’t speed. It’s decision quality and consistency. AI doesn’t fix that by default. In many cases, it exposes it. The question isn’t how much faster the process runs. It’s whether the decisions coming out of it are actually better. Where have you seen productivity improve without a corresponding improvement in outcomes?

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Exciting collaboration between BNY and Carnegie Mellon to advance #AI research and empower future innovators in finance and beyond! 🚀

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AI Agents Need Help. Here’s 4 Ways To Ship Software Reliably As Alex on the Dagger team likes to say: No matter the model or framework, you can’t outrun prompt engineering. This post is a written deep-dive of the talk I gave at the 2025 AI Engineer World’s Fair in San Francisco. Based on my experience with software delivery and agents as co-founder of Dagger and first employee at Docker. As LLMs generate ever more code — from more intelligent autocompletion to fully autonomous coding agents — the cost of writing code plummets. Pressure then shifts to the delivery pipeline, because every new line must still clear the test suite and code review on its way to production. Software-delivery automation has always been a bottleneck — that’s why we launched Dagger — but the bottleneck is tightening now that anyone can churn out large volumes of decent code. https://lnkd.in/ekHv7nbv Stay Connected to Sidharth Sharma, CPA, CISA, CISM, CFE, CDPSE for content related to Cyber Security. #CyberSecurity #JPMC #Technology #InfoSec #DataProtection #DataPrivacy #ThreatIntelligence #CyberThreats #NetworkSecurity #CyberDefense #SecurityAwareness #ITSecurity #SecuritySolutions #CyberResilience #DigitalSecurity #SecurityBestPractices #CyberRisk #SecurityOperations 

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The US Corporate Bond market lacks transparency and great tools. So we built AIQ. It's been rewarding to work beside Kevin Rutter and Brandon Stiles to bring this to market. We started AIQ as a bond search tool, and will continue to evolve to price discovery and execution. Fluid Technology Solutions (and Ashfia Tough) and I have been providing advice on product, strategy, gtm, architecture, and hands-on engineering of agents and massive data solutions. Yes of course we use AI and agents, but have been thoughtful on the architecture to make use the power of non-deterministic LLMs, together with more traditional technologies, to hopefully use the smallest amount of the right technologies to delight our users. If you are interested in fixed income markets, please do create an account and give us some feedback. web url: https://lnkd.in/ekbTzwu7 login: https://lnkd.in/eJc2ZzMa #FixedIncome #CorporateBonds #CreditInvest #Fintech #AIQInsight #nollmslop

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Looking forward to joining leaders across technology, product, and innovation tomorrow at #Phorum2026 in Philadelphia.   In my session, “How AI Is Changing the Way We Build, and What We Build at Vanguard,” I’ll share how AI is reshaping not just our technology stack, but how teams approach product development, from early experimentation to agents at scale. We’ll look at what’s changing and what enterprises need to rethink as AI becomes embedded in everyday workflows and decision-making.   #Phorum2026 #AIEnterprise #TechLeadership #ProductDevelopment #EnterpriseAI

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Proud to see NFSG's Ken Mahoney on Bloomberg Television this week providing valuable insights on the current state of the financial markets. We built a robust and intuitive technology platform behind our advisors so they can focus on what they do best.

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Peapack Private’s Senior Vice President & Chief Technology Officer, John Kowal, was recently featured in New Jersey Business Magazine. In the article, John offers insights on the evolving technology landscape for the banking sector, from cybersecurity challenges to how banks can work together to protect clients and deliver exceptional digital experiences. Read More: https://ow.ly/tQyw50XmEFV

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Many GenAI demos look impressive. But without access to enterprise knowledge, most of them quickly reach their limits in real business environments.   Week 5 · Day 2 Retrieval-Augmented Generation (RAG) Today’s focus was understanding how GenAI systems connect models with enterprise knowledge sources. Large language models are powerful at reasoning and language generation, but they do not inherently know an organization’s internal policies, documents, or operational rules. This is where the retrieval layer becomes essential. Instead of relying purely on model training data, modern GenAI architectures retrieve relevant enterprise information and inject it into the model context before generating responses. User question → retrieve relevant enterprise knowledge → provide context to the model → generate grounded response From an architecture perspective, this layer is often the difference between an impressive demo and a reliable enterprise AI system. Because in many real-world deployments, the biggest challenge is not how well the model generates answers — it’s whether the system retrieves the right knowledge to reason with #AIArchitecture #EnterpriseAI #GenerativeAI #RetrievalAugmentedGeneration #AIEngineering #AIArchitect

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Smarter AI isn’t about bigger models it’s about better, specialized data... This Chicago Booth article highlights how in finance, trusted, domain-specific information makes AI safer, more accurate, and truly valuable. To unlock this potential, organizations need to build and maintain expert-curated data that’s unbiased, reliable, and scalable. That means investing in modern, trustworthy data pipelines that combine: • Automated data processing • Expert validation • Semantic layering for context • Continuous quality monitoring • Clear data provenance and traceability Together, these elements create a strong foundation for AI models that businesses can truly trust and rely on for real impact. #AI #GenAI #DataStrategy #Banking #FinancialServices #TrustedData #Innovation https://lnkd.in/eZS5-HGf

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Pirum selects Bhaskaran as chief product officer. Joining the firm with two decades of industry experience, Vinod Bhaskaran will focus on AI-powered data analytics capabilities and T+1 https://lnkd.in/e_A9XKNY #SecuritiesFinance #Banking

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Recently, during the Jefferies Data and U.S. Research team’s inaugural summit on “Practical AI Views for Buy-Side Decision Making,” I had the opportunity to sit down with Brent Thill , the investment banking firm’s Tech Sector Leader of Software and Internet Research, for a fireside chat on how artificial intelligence is reshaping capital markets. Our discussion covered AI’s role in informing investment decision-making, the efficiencies GenAI is helping to create in research workflows, and how buy-side professionals should be thinking about where they can use this technology in asset management. Special thanks to Ethan Geismar for inviting me to speak, Michael Eastwood for co-hosting the summit, and Grace Hung and her team for curating the content of the event. #AIinFinance #GenerativeAI #ArtificialIntelligence #GenAI #AI #EquityAnalysts #InvestmentBanking

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We automate what’s visible, not what’s hard. Most transformation efforts start with volume — transactions, steps, throughput, cost per unit. That makes sense. It’s what we can see and measure. But visible volume isn’t the same as effort. The real work in most operations lives elsewhere: in exceptions, judgement calls, undocumented client needs, and the interpretation required when reality doesn’t fit the model. That work doesn’t sit neatly in process maps. It doesn’t show up cleanly in metrics. So it rarely becomes the target of automation. Instead, we automate around it. The system handles what fits the standard path. Everything else gets pushed to the edges — absorbed by people, workarounds, and “temporary” fixes. On paper, the transformation looks successful. In practice, the hardest work hasn’t disappeared. It’s just become less visible. And when we optimise only what we can see, we shouldn’t be surprised when complexity survives the transformation — intact, undocumented, and waiting downstream.

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Excellent video whether you are new to investing or if you are a seasoned investor. Benjamin Felix dives into foundational topics to consider as you invest.

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As part of our commitment to providing the agile tools that modern quants and analysts demand, we’ve created the CME Group Continuous Price Series – the definitive, research-ready historical time series designed to reflect true market price dynamics. How this new offering enhances data usage Liquidity-driven logic: View historical time series by either the most liquid Active contract or nearest to expiry Front contract. Massive breadth: 72 continuous series spanning 6 asset classes including price, volume and open interest metrics – from standard benchmarks to the newest Crypto indices. Integrity at scale: Every data point is sourced directly from our regulated exchange, providing a "golden record" for rigorous backtesting and risk management. Evolve your data consumption by making existing data work harder for you. Stop cleaning data and start analyzing it. Ready to find your series and build on a stronger foundation? Download the sample data: https://lnkd.in/gKQvUb4f

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