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Articles by Derek
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Why EQ and IQ Alone May Not Be Enough
Why EQ and IQ Alone May Not Be Enough
Since coming to Credera I’ve had the opportunity to reach back into my network and re-connect with former clients and…
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Derek Knudsen shared thisAlways a good reminderHarness vs Loop vs Graph engineering; Your Transformation Has Too Many KPIs!Harness vs Loop vs Graph engineering; Your Transformation Has Too Many KPIs!Steve Nouri
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Derek Knudsen shared thisIs AI Hollowing Out Our Ability to Reason? I’ve been reflecting on Addy Osmani piece regarding Comprehension Debt—the widening gap between the systems we produce and the logic we actually understand. Addy describes human code reviews as a "productive bottleneck" for comprehension. I’ll be the skeptic here: In reality, many reviews are already surface-level or biased "rubber-stamping" exercises. The bottleneck was already leaky. The real danger? AI doesn't just bypass this gate; it obliterates it. To his point, when we can generate output faster than we can critically audit it, we trade systemic correctness for surface correctness. We stop even trying to understand because the machines make it look "good enough." This isn't just a developer problem—it's a biological one. The human brain is evolutionarily wired for cognitive ease; we naturally gravitate toward the path of least resistance to conserve metabolic energy. When an AI presents a solution that looks correct, our brains are biologically incentivized to stop interrogating and simply accept the "fluent" answer. The bottom line? Understanding complex, difficult things is a practiced skill. By defaulting to AI-driven shortcuts, we aren't just losing track of our "codebases" — we are atrophying our neural plasticity for deep reasoning. If we optimize for volume over depth, we lose the very ability to comprehend hard things. https://lnkd.in/gd48tCZCComprehension Debt - the hidden cost of AI generated code.Comprehension Debt - the hidden cost of AI generated code.
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Derek Knudsen posted thisEveryone is talking about speed in generative AI. Almost no one is talking about the user. A lot of what you’re hearing right now about generative AI in software is centered on velocity. “We’re shipping faster than we ever have!” “Our ProdEng teams are 10x more productive!” “We can finally build out all of those ideas that have been sitting in our backlog!” Sure, the narrative is directionally true. Generative tools ARE removing real constraints in the SDLC. But it’s also incomplete. Because it largely ignores where the constraint is actually moving — to the user. In SaaS, feature adoption has always been a challenge. Multiple studies over the years (Pendo, Standish Group, Productboard) have consistently shown that somewhere in the range of 60–80% of features are rarely or never used. That reality is one of the reasons movements like Lean Startup and Lean Product gained traction in the first place and I have been drinking that Kool-Aid for years. They weren’t about building faster. They were about building LESS — but with more precision. Because more features didn’t equal more value (despite what we like to tell ourselves). If anything, in B2B environments, the problem is even more pronounced. It’s not uncommon for customers to ask us (vendors) to slow down. At Notarize, we made the decision at one point to intentionally pace feature delivery because customers weren’t struggling with a lack of functionality—they were struggling to fully adopt what already existed. In the last year alone, I’ve had multiple enterprise customers ask for the same thing in different ways: “I know you’re excited about what’s new, but can you help us get more value out of what we already bought first?” Generative AI now changes the supply side of software in a potentially masive way. It dramatically reduces the cost and time required to build. What it does not change is the demand side. Users don’t suddenly gain more time. They don’t absorb new workflows faster. They don’t continuously relearn products without friction. So the constraint is shifting. Historically, it lived primarily in engineering capacity. Now it’s moving in two directions: - Upstream to product judgment and prioritization - Downstream to user adoption and value realization Which 1) makes product judgment a first-order problem - Generative AI doesn’t just increase output but also amplifies the consequences of poor prioritization - and 2) reframes the opportunity. The question is no longer: “How much can we build?” It’s: “How much can our users actually absorb and turn into value?” Generative AI will absolutely allow us to ship more — and it already has. But unless we account for the user as the limiting factor, we risk accelerating output while diluting impact. And we’ll find ourselves in a familiar place— Shipping more features…that fewer people use.
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Derek Knudsen shared thisLove what Ines Lourenço did here - building a full operating environment specific to your roles "work to be done". Just makes so much sense versus a bunch of one off skills/plugins/etc.Derek Knudsen shared thisStop prompting. Start building. I made a single file that makes Claude build your entire PM operating system — 38 files, 6 AI skills, 7 frameworks — in 2 minutes. Claude Cowork isn't a chatbot — it's an 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁. So I built a "PM Workspace Wizard" that makes Claude build its own operating system for you. And I'm giving it away for free. Here's how it works ↓ → Download the .skill file from my Featured section 📌 → Open Claude Desktop, switch to Cowork mode → Drop the file into your skills folder → Select a folder for your workspace → Type "set up my PM workspace" → Answer 6 clickable questions about your context → Wait ~2 minutes while the Wizard builds everything One file in, 𝟯𝟴 𝗳𝗶𝗹𝗲𝘀 out. What it builds ↓ → 38 files across 25 folders — a complete PM system → 6 auto-loading skills (PRD Writer, Socratic Challenger...) → 9 templates + 7 frameworks (incl. B2C behavioral psych) → 5 configured workflows — Weekly Reviews, Discovery Sprints, more Once it's done, say "Write a PRD for [feature]" and the system pulls your templates, your frameworks, your context. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆. Inspired by 🥞 Carl Vellotti & Aakash Gupta — plus years of PM muscle memory turned into files. Works for junior PMs. Senior PMs migrating from ChatGPT. Directors who want their team on one system. The .skill file is in my Featured section 📌 Grab it, drop it in your Cowork folder, and NO — you don't have to comment or DM or do anything else. Just go get it 🧙♂️ What's the most time-consuming part of your PM workflow you wish was automated?
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Derek Knudsen shared this"I see you spending a lot of money on GenAI tools but can't see what value we are getting from them." Using GenAI tools in the workplace can be an expensive proposition. When on one platform it's not terribly difficult to track and optimize usage; when people start expanding usage across different CLI tools, IDEs, etc., it gets harder to not just track usage but ROI. While you can build custom tooling for this (I did with Claude) it is great to see Factory integrating this into their already great platform. If you haven't looked at them I strongly recommend you take a look. https://lnkd.in/gmKuKsWc
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Derek Knudsen shared thisLegacy SDLC is dying. I am a fan of what Aakash Gupta writes regarding Product and a big fan of his posting on this subject. I have been approached by many companies trying to sell me on “agentifying” the SDLC but their vision is same old legacy process just with agentic capabilities integrated in the seams. I think there is a market for that now and acknowledge that the Claude team is a bit of an anomaly but the IMO the reality is this will be the new norm in a year at most. Every company should be incubating these new models and redefining their way of working. https://lnkd.in/gfmvB8NMThere's a New PM Skill. It's Called Taste at SpeedThere's a New PM Skill. It's Called Taste at Speed
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Derek Knudsen shared thisHaving to hand curate Github Action Claude PR review manifests is a pain so was fired up about this until I read the fine print: "Code Review optimizes for depth and is more expensive than lighter-weight solutions like the Claude Code GitHub Action. Reviews are billed on token usage and generally average $15–25, scaling with PR size and complexity". https://lnkd.in/ensT3yK4
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Derek Knudsen posted thisAgentic AI is amazing. It’s also exhausting. I've been noticing something. At the end of the day most days I was mentally wiped in a way that felt different. An eight-hour day started to feel like a twelve-hour day. When I mentioned it to a few developers, the reaction was the same: “Yeah… I’ve been feeling that too.” I figured out why. I’m a big lean process guy. One of the core ideas in lean is reducing work-in-progress. Historically, less WIP means faster flow because humans are terrible at juggling cognitive threads. Agentic tools are quietly changing that dynamic. When working with CC/Codex/etc., a lot of the workflow now looks like this: prompt… model thinks… prompt… model thinks. The human is effectively in a waiting state while the agent works. So what do we all naturally do? Start something else of course! If you’re like me, pretty soon you’ve got multiple agent sessions open. New feature idea? Sure! Architecture exploration? Why not?!? Another experiment? Of course! Two sessions become three. Then five. Then TEN! “Look how productive I am!” But there’s a cost. What we were all describing is something I’ve started calling Agentic Switching Fatigue. Context switching has always had a cost. Cognitive research shows switching tasks can reduce effectiveness by 20–40% and significantly increase mental fatigue. Agentic workflows amplify this cost. When you return to a normal task, you just reload your context. When you return to an agent session, you have to figure out what happened while you were gone. What did it change? What assumptions did it introduce? Did it drift from the original intent? What exactly am I looking at here? So the switch isn’t just rehydrating context. You’re rehydrating, interrogating, and reconstructing the state of the work. Do that across five or ten parallel sessions all day and you start to feel it. Agentic Switching Fatigue. Academically, the cleanest solution is obvious—and a little uncomfortable: remove the human from the loop entirely and let agents run end-to-end systems. But until that world fully arrives, we’re still in the loop. So here are a few things I’ve started dogfooding every day to manage the fatigue: 1. Before leaving a session, I have the agent produce a quick state summary—what changed, key decisions, and open questions. 2. I limit how many agent threads I run. Lean still applies. Just because ten agents can run doesn’t mean I should track ten contexts. I try to keep it around three. 3. I have the agent maintain notes or checkpoints so I’m not reconstructing the full state of the work every time I come back. Humans have always shown an incredible ability to adapt to new tools and ways of working. I suspect this shift to agentic workflows will be no different, and it will be fascinating to see the practices and habits that emerge.
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Derek Knudsen shared thisI’ve spent the last few years building on these models, and this update feels less like a heel‑turn and more like "capitalism cashing in its veto". Once infra and capital commitments get this large, “we’ll stop ourselves” becomes a story the balance sheet can’t support and not just for Anthropic. Will be very interested to see how Anthropic navigates the DoW ultimatum, it's next ethical challenge. Perhaps foreshadowing of the outcome? https://lnkd.in/g8RVcHZH
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Derek Knudsen liked thisDerek Knudsen liked thisI'm 38 years old. My kids are in the decade when they'll need me most. My career says these should be my highest-earning years. My body is changing, and my marriage is at the stage that deserves real investment, not what's left over at 9 pm. I do corporate strategy for a living, and if a client handed me a roadmap where every major initiative peaked in the same window, I'd flag it in the first meeting. But I NEVER SAW THIS COMING in my own plan until I was standing in the middle of it, overwhelmed and running on fumes to keep every ball in the air. But here's the thing about strategy... IT CAN CHANGE. Our systems were built for a family structure that no longer exists, but that doesn't mean we have to stay stuck in them. We don't have to subscribe to a life of survival. We can choose to simply look at the roadmap differently.
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Derek Knudsen liked thisAI can write code, pass the bar exam, and fold proteins. But the thing that's actually going to change your Tuesday? Knowing which duplicate record to keep. That's the unglamorous, hour-eating, second-guessing work nobody puts in a keynote. And it's exactly what our new AI Deduplication experience solves — not just a match score, but the signals behind it, the conflicts flagged, a confidence score, and the reasoning for which record to keep. Your team still makes the call. Cleaner data. Faster decisions. Hours back in your week. The best AI isn't the flashiest thing in the room. It's the thing that makes something frustrating feel effortless. This one does. #HigherEd #AI #Element451 #AIinHigherEdDerek Knudsen liked thisDuplicate records have always been easy to flag. Knowing which one to keep? That's the hard part. After 15 years working in higher ed, our VP of Product, Eric Range, felt that pain firsthand: the hours lost manually comparing conflicting records, the second-guessing over which one was right, and the nagging sense that something still slipped through. So we built a better way. Element451's new AI-powered Deduplication experience gives teams more than a match score. It surfaces the signals behind a potential duplicate, flags conflicting information, assigns a confidence score to each recommendation, and explains its reasoning for which record to keep. Your team reviews the recommendation and stays in control of the final decision. The result? Cleaner data, faster decisions, and a whole lot less time spent untangling duplicate records. See it in action 👇 And read Eric's take on what 15 years of manual deduplication taught him about building AI you can trust: https://lnkd.in/e2JGVkFr #HigherEd #AI #HigherEdTech #DataManagement #Element451
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Derek Knudsen liked thisDerek Knudsen liked thisHappy International Friendship Day! 🧡 We're grateful to work alongside talented people every day, and even more grateful for the friendships we've built along the way. Here's to the teammates who make work a little more fun, celebrate the wins, support each other through the challenges, and remind us that the best workplaces are built on great people.
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Derek Knudsen liked thisDerek Knudsen liked thisWrapping up the Encoura national conference where I got to share more about Encoura Connect and the power of Element451's Bolt AI engine. Always great to share the stage with Matthew Ellis.
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Derek Knudsen liked thisDerek Knudsen liked thisAfter a lot of thought, and more than a little internal debate, I've decided it's time to begin the next chapter. My last day in my current contractor role will be August 14, 2026. This wasn't an easy decision. The team I've had the privilege of supporting has been nothing short of exceptional. They are talented, collaborative, supportive, and genuinely enjoyable to work with. Working alongside people like this makes stepping away much harder than I ever imagined. As I look ahead, I'm embracing semi-retirement. For me, that doesn't mean I'm finished working. It simply means I'm choosing to work differently. Over the past 30 years, I've had the opportunity to lead complex programs, guide organizations through significant technology initiatives, build high-performing teams, and help solve difficult business and compliance challenges. Those experiences are something I still enjoy sharing. Going forward, I'm interested in opportunities where I can provide strategic advice, trusted guidance, and experienced consulting to organizations that need an extra set of seasoned eyes. Whether that's helping executive leadership shape strategy, advising on CMMC and NIST SP 800-171 compliance, providing program or project oversight, mentoring project managers, evaluating organizational challenges, or serving as a fractional advisor, I enjoy helping organizations navigate complexity and make informed decisions. I'm open to short-term, part-time, remote consulting engagements where experience, perspective, and practical solutions bring value. I'm not looking for another traditional 9-to-5 role. Instead, I'm looking for opportunities where I can contribute in meaningful ways while maintaining the flexibility that this next stage of life offers. To everyone I've had the privilege of working with throughout my career, thank you. Your trust, partnership, and friendship have made this an incredibly rewarding journey. And to the outstanding team I've been supporting most recently, thank you for making this one of the most enjoyable chapters of my career. I'll always be grateful for the opportunity to work alongside such an exceptional group of people. Here's to the next chapter. I look forward to spending more time with family and friends, enjoying life a little more, and continuing to help organizations succeed when my experience can make a difference. If you know of an organization that could benefit from strategic consulting, executive guidance, program leadership, CMMC expertise, or an experienced advisor for a short-term engagement with part-time hours, I'd welcome the opportunity to connect. #SemiRetirement #StrategicConsulting #TrustedAdvisor #ExecutiveAdvisor #FractionalLeadership #ProjectManagement #ProgramManagement #PMO #CMMC #NIST800171 #Cybersecurity #InformationSecurity #Leadership #RemoteWork #Part-Time #Veteran
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Derek Knudsen liked thisDerek Knudsen liked thisAnother incredible customer milestone! 🚀 Huge congratulations to Urban College of Boston and everyone on our team who helped bring this launch to life. It's always rewarding to see the impact our customers are making for future students. #HigherEd #AI
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Derek Knudsen liked thisWe’re continuing to grow at Element451 and are looking for great people to join us! If you’re exploring your next opportunity or know someone who might be a great fit - take a look at our open roles. We’d love to connect!Derek Knudsen liked thisWe’re hiring across the board at Element451 We’re the AI-native CRM built for higher education, and we’re growing fast. That means a lot of open seats across the business: • Business Development Representative • Product Manager • Senior Campaign and Content Manager • Senior Front End Engineer • Senior Platform Engineer • Senior Software Engineer, Integrations • Quality Engineering Lead • Team Lead, Delivery Management If you want to build the future of student engagement with a team that moves fast and actually ships, I’d love to talk. Check out all open roles and apply ⬇️
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Derek Knudsen liked thisDerek Knudsen liked thisHere is the most expensive belief about burnout: that the cure for being depleted is rest. 😴 For some people it is. For most, it is not, and they find this out the hard way. Two real weeks off, and within days of being back they are sitting exactly where they started. If that has ever happened to you, you were not resting wrong. You were treating the wrong problem. #burnout #mentalhealth #highperformance #chronicoverload #capacity #productivity
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Amaresh Tripathy
AuxoAI • 9K followers
LLMs are going vertical → and functional. We’re moving from “everyday AI” to functional AI: domain-specific agents embedded in real workflows where enterprise value is trapped Proof the shift is led by LLM providers themselves: Banking: OpenAI is partnering directly with banks (e.g., BNY Mellon’s multiyear deal to upgrade its Eliza platform; NatWest’s UK-first collaboration). These are not generic chats, they’re deeply embedded, regulated-industry builds. Life sciences: Anthropic’s Claude for Life Sciences adds connectors to tools like Benchling, PubMed, 10x Genomics and offers domain skills: from protocol QA to bioinformatics workflows. That’s vertical by design. Healthcare: Google’s MedLM + Vertex AI Search for Healthcare targets clinical documentation and medical record retrieval: out-of-the-box isn’t enough; it’s workflow-native. Industrial: Siemens Industrial Copilot (with Microsoft) is scaling across factories and engineering teams. LLMs tuned to PLC code, Teamcenter, and shop-floor realities. The takeaway: The real value isn’t a model. It is configuration and customization: grounding in your systems of record, domain ontologies, governed connectors, policy guardrails, eval harnesses tied to domain KPIs, and change management. Off-the-shelf chat interface won’t clear the bar for accuracy, compliance, or UX in complex functions. Verticalization is the on-ramp. Customization is the unlock. #EnterpriseAI
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Patrick O'Shaughnessy
Positive Sum • 15K followers
Dan on why hyperscalers will be a worse business model over time even as growth accelerates: "AWS, Azure, GCP are going to accelerate for a while just because their customer base -- Anthropic, OpenAI -- are growing at an enormous pace. The problem is you went from a dynamic where their customer base was like every corporation in the world and therefore they had fragmentation and it was a really good business. Going forward it's highly unlikely that LLMs are not concentrated in the hands of 4 or 5 companies. Those companies right now, are cashflow negative, and therefore they're looking for compute anywhere they can get it. In the next 5 to 10 years, they'll be generating enormous amounts of free cash flow. When that happens, they're likely to insource the compute. Right now, they look at the hyperscalers as more of a financing mechanism. But I don't think they are better than the LLMs at building data centers. Then you have this dynamic of neoclouds. I don't think they're going away like people thought, because they're better at running GPU clusters than the traditional hyperscalers, and there's a lot of interest from NVIDIA to make sure that their customer base is diversified."
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Janior Valle
Streamlyne • 336 followers
This is why we built FundFit 👇 With federal funding increasingly competitive and uncertain, non-federal opportunities like this are critical for research sustainability. Yet research offices tell us they spend 40+ hours/month searching for relevant opportunities, only to miss the non-obvious matches. Our AI just found 3 institutions with 82-83% compatibility for a single non-federal opportunity - connections a human search would likely miss. In today's funding climate, the future of research development isn't about finding more opportunities - it's about finding the RIGHT opportunities, especially beyond federal sources.
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Salem Bagami
Metatalent.ai • 43K followers
CB Insights recently highlighted the evolving landscape of consulting as the Big Four firms strategically integrate AI agents into their operations. Key points to note include: 1. Accenture: Recognized as the most interconnected firm, Accenture has partnered with significant players such as Salesforce and NVIDIA. They have committed $3 billion to AI and created a new "reinvention services" business line through the merger of five units. 2. Deloitte : Focused on AI through its Zora AI initiative in partnership with NVIDIA and HPE, Deloitte claims implementation leads to a 25% cost reduction and a 40% boost in productivity internally. 3. KPMG: Launched its Workbench platform across 95 member firms, hosting 50 operational AI agents with nearly 1,000 in development, alongside investments in startups aimed at automating consulting processes. 4. EY : Deployed 150 specialized tax agents to enhance the efficiency of 80,000 professionals and developed the EY Agentic Platform with NVIDIA, utilized monthly by over 240,000 employees. 5. PwC: Introduced Agent OS, featuring 250 agents and supporting 31 million AI interactions, backed by a $1 billion platform aimed at automating audits in collaboration with Microsoft. These firms are increasingly embedding AI agents into existing software ecosystems like Salesforce and SAP and investing in data companies to enhance agent capabilities. Moreover, they are restructuring teams to improve efficiency and effectiveness. Notably, KPMG has leveraged AI to justify a 14% reduction in auditors' fees, indicating a competitive pricing strategy among firms. This suggests that as AI becomes more prevalent, procurement teams might have leverage in negotiating costs based on AI-driven efficiencies. Currently, however, only 14% of companies are reported to be utilizing AI agents in accounting and finance, highlighting a significant gap in AI adoption that could evolve as firms continue to innovate. Credit : Wouter Born Repost by : Salem Bagami
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Michael Noel
DeReticular • 24K followers
DeReticular is effectively attempting to hijack (benevolently) the CodeLaunch pipeline. By training founders to pitch specific RIOS-native concepts, DeReticular is transforming CodeLaunch from a generalist startup competition into a specialized Industrial DeepTech Incubator. Here is how this influx affects the internal operations, brand positioning, and strategic output of the Venture Forge. https://lnkd.in/gJDfmeas
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Kjael Skaalerud
SuperCat Solutions • 35K followers
While everyone was debating whether AI would replace consultants, OpenAI just became one. Their new consulting arm charges a minimum of $10 million per engagement, embedding Forward-Deployed Engineers directly into client organizations to build custom GPT-4o implementations. This isn't just another enterprise sales play. It's vertical integration disguised as consulting. Despite OpenAI reaching $10 billion in annual revenue and serving over 500 million weekly users, they're still burning through approximately $5 billion annually. API subscriptions alone aren't cutting it. If OpenAI, which started the ChatGPT revolution, needs to move into services, what does that mean for smaller AI startups? Google, Anthropic, Meta - they're all building comparable models. The differentiation isn't in the core technology anymore. It's in implementation, integration, and delivering measurable results. So what's the play for everyone else? 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 → 𝗢𝘄𝗻 𝘁𝗵𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 → 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲. OpenAI is shifting from the first to the second. The real money is in the third. The future belongs to vertical solutions that understand specific industries deeply enough to deliver turnkey outcomes. Not APIs. Not platforms. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀. This is why I'm betting on micro-SaaS in specialized verticals. When AI becomes commoditized infrastructure, domain expertise and implementation capability become crucial differentiators. Pure technology plays are getting harder and harder to defend. APIs are becoming commodities. Implementation is the new IP. I share more musings on my substack >> https://t2m.io/4aDyNCpu For the love of the game 🏴☠️⚡️ #ai #futurism #chatgpt #openai 𝘐𝘮𝘢𝘨𝘦 𝘚𝘰𝘶𝘳𝘤𝘦: 𝘚𝘵𝘢𝘳𝘵𝘶𝘱 𝘈𝘳𝘤𝘩𝘪𝘷𝘦
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Han Zhu
Adot • 244 followers
The hardest part of commercializing AI isn’t building a better demo. It’s earning an organization’s permission to put the product into real work. In a recent episode of Lenny’s Podcast, JJELLYFISH co-founder Jen Abel explained how $100K+ enterprise deals actually move forward: - Don’t lead the first call with a demo. Listen first. - Map the decision-maker, internal champion, security, legal, and procurement teams. - Demo only the 20% of the product that addresses the customer’s real problem. - Design short pilots around clear users, use cases, and success criteria. Treat contracts, data boundaries, and accountability as part of the product—not administrative cleanup. My biggest takeaway: enterprise procurement is a form of micro-governance. Companies aren’t buying model parameters. They’re buying outcomes that can be measured, explained, and defended internally. For AI founders, the language of commercialization must shift from “how powerful is our model?” to “what business result can we reliably deliver?” A signature closes the deal. But commercialization truly begins when the product stays inside the organization—and becomes part of everyday work. #AI #EnterpriseAI #B2BSaaS
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Ken Shaw
Sapience • 10K followers
Here's a hot-take: at this point in the AI adoption lifecycle, the best exec to lead AI transformation in small to mid-sized orgs (up to about ~2000 people) is the CFO. Not the CEO. Not the CIO or CTO. Why? I'm a nerd. I'm a software engineer and I'm an "AI guy". But I'm also a recovering CEO and Private Equity guy. And now I help CEOs and PE firms with AI strategy and rollout. So, I've seen this puzzle from many angles, as a: CEO, Board member, Investor/PE/Owner, and Advisor. So where SHOULD it go? We are running a webinar next week to go deep on this (details below). Come and participate in the conversation. But here's the short version... In the *majority* of cases, the best place is the CFO. The exception would be if you have a world-class CIO/CTO. But those people cost $500k+ so you likely do not. But, as the cartoon depicts, this is NOT what is happening out in the wild. The CFO is often the *last* exec read into AI projects, and rarely the 1st. But this is a leading reason why 9 out of 10 AI projects "fail" when measured on ROI (MIT, 2025). What we at Sapience know: 1. AI can fundamentally lower your cost to build/deliver/serve; but making that real is hard. Almost no companies achieve it, because of who was given the project and how they ran it. 2. Most tech leaders don't understand how their work touches the P&L or the balance sheet. With AI transformation that's the start and endpoint of every project. Don't ask a dolphin to drive your car. He's out of his element and doesn't have the skills. 3. Doing AI properly is not a tech play. Its a capital allocation play. It’s about choosing a small group of bets, running parallel projects, killing early the ones that aren't hitting their numbers, and re-writing OpEx rules and assumptions all over the business. The bets have to be informed by data; they have to move the needle if they work. Every idea should be trace-able to a targeted change in revenue, OpEx, EBITDA or all three. 99% of CTOs/CIOs are incapable of this. 4. The CFO is best *placed* and the best *suited* for the task. They have the data, mindset, and discipline required. They have the title and authority. What they don't have is the confidence, mandate or the right technical folks in their corner. We would like to change those things. If you're a CFO watching AI projects all around you, recognizing the chaos but not knowing how to get control of it, or how to shape the efforts, this webinar is for you. AI Agents are the greatest EBITDA and EV lever the world has ever seen. It's not going away. The pace and the complexity is only going to increase. Come along and start charting a course for your company to the rarified air of provable-ROI with AI, and the incredible gains it can deliver if managed properly. Learn more here: https://lnkd.in/gdq9vUMu #ai #sapience #strategy #leadership #finance Or register through LinkedIn here: https://lnkd.in/gE2EqtTi
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Ian Schick, PhD, Esq
Paximal • 14K followers
The acceleration of innovation we’re seeing right now isn’t incremental — it’s structural. As USPTO Director John A. Squires put it last Friday: “AI is the Roy Kent of modern technology — it’s here, it’s there, it’s everywhere.” And the Patent Office is moving just as fast to meet that moment: “We are doubling down… with AI, we’re going to re-invest… The doors to the USPTO… are wide open.” From the launch of AI-assisted examination pilots to record backlog reductions, the USPTO is clearly preparing for a new era of invention — one where #AI isn’t just a subject of patents, but a driver of exponential innovation. 📈 At Paximal, we’re seeing the same pattern firsthand — organizations that draft #patents are filing more, filing faster, and keeping outcomes strong. The flywheel is already turning. 🔗 Read Director Squires’ full #AIPLA remarks here: https://lnkd.in/gNPnxvkt #AI #Innovation #Patents #LegalTech #USPTO #Paximal #AgenticAI #aiplaAM25 #IPLaw #LegalTech #Agentic #AI #LegalInnovation American Intellectual Property Law Association (AIPLA) Paximal Idea Clerk by Paximal
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Tomasz Tunguz
Theory Ventures • 408K followers
Why was the Fivetran-dbt merger all but inevitable? Fivetran & dbt Labs announced their merger yesterday. The all-stock deal combines two companies into an entity approaching $600 million in ARR. The beauty of the modern data stack was the explosion in choice. As the cloud exploded onto the scene, the legacy data warehouse was replaced by a collection of fast-moving platforms. In that era, specialization won. The pendulum is now swinging back towards consolidation. Why? The answer lies in compute economics & revenue scale asymmetry. The table below shows why. There are three different categories of software within this subset of the ecosystem: 1. Ingestion takes data from software & moves it into a cloud data warehouse. Snowflake acquired Datavolo, which commercializes the open source product Apache NiFi, calling it Openflow. Databricks acquired Arcion for ingestion through change data capture, calling it LakeFlow Connect. Fivetran focuses exclusively on this layer. 2. Transformation means reformatting the data within the cloud data warehouse. Snowflake launched native dbt Projects on Snowflake. Databricks offers Delta Live Tables, native SQL, & Python, plus supports hosted dbt through Databricks Workflows. dbt Core/Cloud is the leading independent transformation tool. 3. Compute revenue is generated when we ask questions of our data. Snowflake remains one of the leaders in structured data analysis with their cloud data warehouse. Databricks’ compute is their own as well. Here’s the asymmetry in one number. Compute represents 72% of the overall market ($7.6B of $10.6B). As a result of their massive operations, Snowflake & Databricks exert significant gravity within the ecosystem. They have expanded beyond the compute market to impose their presence & capture marginal revenue within customers, pressuring the competitive ecosystem. That’s not to say these components are independent. George Fraser analyzed Snowflake workloads in September 2024, finding transformation represents 40-45% of total Snowflake compute, which means even at smaller scales, startups can have significant impact on these behemoth businesses. The Fivetran-dbt merger is an inevitable evolution of a maturing market. Two unicorns must partner to compete against two decacorns. They solve two of the three customer problems. But not yet compute. One could surmise this consolidation signals the end of the modern data stack. I view it differently. The MDS has succeeded beyond our expectations. The stakes are higher now. Broad platforms, fast growth, & AI-native architectures define the next phase. Expect more consolidation. Category revenue estimates based on public disclosures & company filings. Ingestion: Informatica ($1.64B FY2024), Fivetran ($300M est.), Talend ($350M), others ($200M est.). Transformation: dbt Labs ($300M est.), others ($200M est.). Compute: Snowflake ($3.6B FY2025), Databricks ($4.0B ARR Aug 2025).
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Rubén Domínguez Ibar
The VC Corner • 336K followers
When One Engineer Is 1000x At Sequoia Capital, early product risk comes down to people. In this clip, Pat Grady and Alfred Lin, with Jack Altman, explain why one exceptional engineer can define a company’s DNA. ▫️ ServiceNow went public with most of its core code written by Fred Luddy ▫️ Airbnb was largely built early on by Nate airbn alone These weren’t 10x engineers. They were closer to 1000x. That early technical ownership shapes everything that follows. I broke this down in the Sequoia Playbook, including 10 systems most firms never had the patience to build, here: https://lnkd.in/e9ubxeyM Where have you seen one builder change everything? Do you agree?
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