Farrukh Khan

Big news: Snowflake Ventures is leading a strategic equity financing in AtScale. This investment validates the critical role of semantics in the modern enterprise data stack. As enterprises consolidate data on Snowflake AI Data Cloud, a unified, governed semantic layer is essential for consistent metrics and business logic. A semantic layer like AtScale makes accessing that data more accurate, accessible, and efficient by bridging the gap between business language and data language. AtScale’s Universal Semantic Layer provides this single source of semantic truth for customers like Fidelity Investments and The Home Depot. The company remains independent and interoperable, supporting a broad ecosystem of data platforms, BI tools, and AI applications. This is a good moment to reflect on the future of data and AI. New to semantic layers? I'll post a tutorial link below. And here are five big topics from the 2025 Semantic Layer Summit (link to Substack articles on these below): 1️⃣ Semantic layer standardization is coming 2️⃣ Semantic layers are BI’s Gift to AI 3️⃣ Semantic layers make AI 3X more accurate 4️⃣ Long live SQL (with semantic layers to help) 5️⃣ The great debate: Should semantics be in your database? This is a pivotal moment for the data industry, and I’m thrilled to be a part of it! https://lnkd.in/eVJ276Pp

88

10 Comments

We’ve been having a lot of conversations with teams about the results of the annual Proof Causal.ai / Fiduciari report on GTM Effectiveness in 2025. More than a few have been pretty agitated about the 53% ineffective statistic, and some have asked for corroborating evidence. Which is 100% fair. Here’s the evidence: If your CAC is exploding while 1. your deal volume is falling, 2. average deal size is collapsing, and 3. average deal velocity is extending, and 4. the number of deals that close without an outcome remains high, your GTM is ineffective in the context of your customers and the marketplace. The longer it takes for you to understand what’s effective and ineffective means your waste is compounded. In B2B, this lag is commonly 9-16 months, sometimes longer. This reality means the risk on the shareholder capital “loaned” to GTM is very high. Internally, this means that the payback of that loan is in arrears and probably approaching default. One CFO recently said that by this standard his company’s GTM operation was bankrupt. And pouring more shareholder money into the same status quo framework was money down the proverbial rat hole. Likewise, he said, pouring money into untested new approaches was a leap into the dark. That’s when we discussed using causal models to better understand the probabilities of success before any money was spent. Two things are very important here: 1. The effectiveness of B2B GTM is getting absolutely hammered by marketplace volatility and the high rate of change. Your GTM’s effectiveness is the “canary in the coal mine” that signals the future health of your business. It gets sick before the rest of the business. 2. As effectiveness falls, and GTM teams show no ability to explain the issues in detail and correct them, confidence in the revenue engine of the company falls. Investment falls with it. Yet this only accelerates GTM ineffectiveness, creating a self-fulfilling prophecy for the business. If your GTM leaders are saying that you can’t cut your way to growth, they’re right. See #2 above. But if you’re the CEO or CFO, and your GTM leaders can’t tell you what needs to be done differently to become much more effective, that’s your core issue. Because you can’t spend your way to growth either. Sangram Vajre Scott Brinker Tim Hillison Julia Nimchinski David LaCombe, M.S. Mark Ogne Lisa Sharapata Kerry Cunningham Ranee Soundara Robin Daniels Ayse Guvencer Achim Klor Tracy Borreson Marcus Cauchi Cynthia Lehman Mike Pastore Oliver Lash-Williams Michelle Killebrew Rob Huffstedtler Ross Graber Chris Hare

29

28 Comments

You need to be paying attention to Ohio tech. Here are some recent headlines... • Nationwide commits $1.5 billion to tech and AI transformation (h/t Jim Fowler) • Stargate Expands: OpenAI, Oracle, and SoftBank announce five new AI data center sites, including Ohio • Cleveland Clinic and DASI Simulations partner on AI ‘Co-Pilot’ for heart procedures (h/t Teri Sirset and Sean McKibben) • Ohio startups can now apply for JumpStart Inc.’s Trailblazer Software Accelerator (h/t Lorne Novick, Nicki Hubbard) • Sonic Fire Tech raises $3.5M Seed Round to bring NASA-inspired infrasound fire defense to market • Inside ‘Innovate Toledo’: How the University of Toledo plans to power the region’s Next Economy (h/t Sharon Speyer) • Cincinnati-backed Kernel (YC S25) raises $22M for powering AI’s new “browser layer” (h/t Rafael Garcia, Catherine Jue) • The O.H.I.O. Fund invests in Dublin-based EASE Logistics to expand AI innovation (h/t Peter Coratola Jr., Ray Leach Jeffrey Stern) • Cleveland Clinic, Khosla Ventures join forces to accelerate health tech innovation • Honda invests $2.6 million to expand research at The Ohio State University (h/t Walter "Ted" Carter) • Akron’s Polymer Bet: First startups join Synthe6 Accelerator (h/t Jessica Sublett, J.D., LLM, Hans Dorfi) • Robots in Every Classroom: Cleveland students get hands-on with AI future • Dayton’s DEXA (Drone Express) raises $15 million Seed Round to take drone delivery mainstream (h/t Beth Flippo) • Ohio to anchor U.S. nuclear revival as Centrus Energy Corp. plans major expansion (h/t JP Nauseef, JobsOhio) • JobsOhio, Ohio Life Sciences Association launch $30 million biomanufacturing workforce initiative (h/t Eddie Pauline) • Cleveland’s Splash secures $70 million Series C, backed by The O.H.I.O. Fund (h/t Steven Muszynski) • Cleveland startup Beacon launches to pay you for referrals (h/t Rob Reznick) -- Link to each story in the comments. Follow OhioX, Ohio Tech News, and Ohio Tech Events for more tech news and events. 🚀🚀🚀 Want this type of news straight to your inbox? Join 8,503+ Ohio tech execs/pros by subscribing here: https://lnkd.in/eSSgK5ix

539

34 Comments

Proof Causal.ai has a big report coming out later this month about the major drops in GTM effectiveness since 2018. A big part of the "why" is GTM team's failure to understand the headwinds that increasing volatility and high-velocity change are exerting on their efforts. But another big element of wasted spend is the desire to evangelize new categories, companies and tools in the midst of one of the most risk-averse environments since 2009. Simply put, evangelism feels heroic, but it's not. In fact, if you're having to do a lot of evangelism, it's a sure sign that you aren't connecting with your real ICP. Evangelizing the wrong ICP is like trying to talk someone into loving you. I have personally lived this. In late 2017, Proof launched a really important new causal analytics tool designed specifically for GTM teams. And while we had good adoption, most marketers ignored it in favor of MTA. Two years later, COVID hit and upended businesses everywhere, mainly due to radical shifts in the marketplace. Stuff that had worked for a long time stopped working, and new investments that had never been considered suddenly took center stage. CFOs started calling. Remember, we were focused on CMOs and CROs, so this was a real shift. Then in 2022, interest rates spiked. CFOs got even more interested. Volatility continued to build across almost every major vector -- tariffs, politics, war and peace, supply chain threats, social dissent, you name it -- and people started to realized that the marketplace patterns of the Past had less and less relevance to the Present or the Future. Correlation was coming face-to-face with the reality of an open system under stress. During this entire time, we evangelized #CausalAI, but the ICP changed radically. We still do a lot of GTM work, but we continue to see our business go horizontal via Finance, who just wants to know if the budgetary "loans" of shareholder money are on track to being "repaid with interest." We felt a bit like the physician who went to law school and is now a top malpractice lawyer, but the speed with which Finance says "Yes" is a huge signal. All of this was a hard lesson to learn, not just because it was costly but because our founding desire was to help GTM teams fix their problem. We had to come to grips with the fact that many of these teams didn't want our help, thought it was too hard, or thought it would expose them. But Finance saw it differently, particularly after the Delaware Chancery Court changed the rules on fiduciary duty. https://lnkd.in/gQZTAYmC

23

5 Comments

Fascinating new data from a16z on how CIOs want to buy AI apps: 1. CIOs now *prefer* usage-based models. So much for usage-based pricing being seen as a barrier to adoption 🤷‍♂️ My two cents: most AI spend w/ the CIO is on LLMs and compute. Both are usage-based categories where folks have become accustomed to granular pricing and not buying shelfware. With usage-based models, there’s also a connotation that prices will come down over time as vendors compete. Avoid picking a pricing metric that positions your app as a commodity w/o differentiation! 2. Seat-based models aren’t the key to unlocking budget. Only 21% said seat-based pricing was their preferred model. That’s slightly less than hybrid models (23%) even though hybrid models are inherently more complicated with more risk of overages/extra fees. My two cents: buying in a way that reflects the value received is >>> than having perfect budget certainty/predictability. And flexibility matters as folks are finding which AI apps are “experiments” vs ready for “production”. 3. There’s a lot of work to educate CIOs about how to pay for outcomes. 15% said outcome-based models are their preferred way to buy — although that’s still significant considering only *5% of companies* have outcome-based pricing today (based on the 2025 State of B2B Monetization report)! We’re still in the early innings. Among the concerns CIOs raised: (1) lack of clear, measurable outcomes for most products - 47%, (2) unpredictable or unscalabe costs - 36%, (3) difficulty with attribution to a specific tool - 25%. My two cents: I suspect line of business is much more open to outcome-based pricing than the CIO — after all, the CRO and the CMO are measured based on outcomes, too! But if you’re thinking about outcome-based pricing, you need outcome consistency, attribution, measurability and predictability (CAMP). — Cool to see this new data. Very fascinated to see how it changes for next year. #ai #genai #pricing

248

39 Comments

We are excited to announce that Stack Overflow Knowledge Solutions is joining the Databricks Marketplace! Trusted and accurate data is the foundation of AI, and through our new integration, customers will have access to Stack Overflow's 16 years of curated knowledge to power their data and AI ecosystems. Learn more about our latest partnership here: https://lnkd.in/e4r6Fkjb Ali Ghodsi David Meyer Jay Bhankharia Arsalan Tavakoli

156

7 Comments

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

2

When I brief candidates for CIO and AI platform roles, I ask three questions. What workflows do you own? What control surfaces do you manage? What KPIs do you report? Gartner’s 2026 technology trends map those control surfaces, trend by trend. Taken together, they form an executive operating map for the next 5 years. 1. AI native development platforms What it changes: software delivery becomes an orchestration problem across copilots, reviews, and guardrails. What to measure: cycle time, defect rate, engineering capacity reclaimed 2. AI supercomputing platforms What it changes: advantage shifts toward compute governance and workload economics What to measure: utilization, cost per training and inference unit, and spend envelope adherence. 3. Confidential computing What it changes: sensitive data can be used in AI and analytics with stronger assurances. What to measure: protected datasets onboarded, auditability, reduction in exposure surface 4. Multi-agent systems What it changes: agents become an execution layer across functions, introducing new leverage and failure modes. What to measure: end-to-end workflow coverage, incident rate, escalation volume 5. Domain-specific language models What it changes: accuracy and compliance improve through specialization by industry and work. low What to measure: benchmark delta versus general models, exception rate, compliance outcomes 6. Physical AI What it changes: intelligence moves into fleets, factories, logistics, and field operations. What to measure: throughput, yield, safety metrics, downtime reduction 7. Preemptive cybersecurity What it changes: defense becomes prediction- and prevention-driven by AI and tele.metry What to measure: dwell time reduction, blocked attack classes, and loss exposure trend. 8. Digital provenance What it changes: trust becomes verifiable across software, data, and generated content What to measure: signed artifacts coverage, provenance verification rate, dispute resolution time 9. AI security platforms What it changes: centralized policy, visibility, and control across models, agents, and AI applications What to measure: coverage across AI surfaces, policy enforcement rate, third-party risk closure 10. Geopatriation What it changes: geography becomes a constraint in architecture, vendors, and data strategy. What to measure: concentration risk reduction, migration milestones, compliance readiness The thread across all of them 2026 technology leadership centers on ownership, governance, and measurable results. Success involves turning trends into specific workflows with designated owners and KPIs, subject to audit by finance and operations.

38

7 Comments

Bill Magnuson just zigged. On Braze's Q3 call, an analyst asked if they were getting access to dedicated AI budgets given their accelerating growth. Bill's response: "The key thing with decisioning is not necessarily accessing AI budgets, but the fact that we're selling performance." I loved that response. While competitors position as "AI-powered" to tap experimental tech spend, Braze is proving dollar for dollar uplift at critical journey moments - head to head against flows that were already optimized. "That generates real money for those customers... Better than accessing experimental AI budgets, we are selling performance." It's about the result. The distinction: → "AI-powered" is a feature → Performance is a business outcome I've seen countless brands waste money on shiny new objects that never deliver. The "AI budget" is becoming the new innovation theater. Brands will always find budget for things that prove ROI. They're done funding science projects. They want the numbers.

130

4 Comments

**Databricks Secures a Staggering $100B Valuation on $4B ARR** Databricks' impressive rise to a $100 billion valuation, following a recent $1 billion funding round, demonstrates the robust demand for cloud data platforms. As we observe this growth, it prompts a reflection on the scalability of data solutions in the current market. At ElevenX Capital, we believe that investments in companies like Databricks highlight the importance of harnessing data for innovation. How are you strategizing your portfolio in the evolving landscape of data technology? #investing #innovation #venturecapital #entrepreneurship

1

I often think back to my discussion on Mission Matters Podcast with Adam Torres about the fundamental flaw in legacy retail pricing. The core math — what most traditional systems and consulting practices rely on—is rooted in classic Price Elasticity models. These models, essentially unchanged since the 1930s, simply track a direct, high-level correlation between historical price changes and sales volume. They are average at best. That approach completely misses the context that matters today: location, nationality, competition, cross-product affinity, and real-time demand shifts. At Competera Pricing Platform, we built Contextual AI to address this "dumb pain." We let the machine analyze the entire context using modern, model-driven intelligence, freeing up human talent to focus on high-impact strategy. You can't optimize modern retail with Depression-era economics. Listen to the episode: https://hubs.ly/Q03SNSF10

23