John Faber

🚨 BREAKING: Confluent is looking to be acquired, sources say. Confluent is allegedly working with an investment bank on the process of being sold "after attracting acquisition interest". Reuters broke the story, citing three people familiar with the matter. Are you surprised? I'm not. Confluent’s future wasn't looking bright: • increased competition (Kafka is becoming commoditized) • big customers reducing spend (as shared in last earnings) • open sourcing of Diskless & Iceberg Topics (closing the latest proprietary feature gaps) • peanuts revenue from Flink (just $10M and recent) • stock performance has been an absolute failure (objectively speaking) A few months ago, its stock price cratered -40% overnight. As I mentioned back then, Confluent needs a miracle. A miracle in Flink/DSP spend to propel the company back into the rocketship it was. The numbers so far haven't shown that. 😔 Also, given the history of such attempts and clear lack of revenue to show for it - it wasn't looking pretty. The company marketing also gave it away. Marketing narratives included things like "beating anybody on price" (despite being famous for costing 2-3x more) or "building meal prep AI agents using 6 Flink jobs" 😂 Rumors about potential acquisition had been going on for a while, but they were just that - rumors from people who think that was the only way out. There were signs. 👉 Six months ago, Confluent overhauled its executive severance policy to favor "change of control" circumstances. That gives out full equity vesting & good payouts if execs get fired post-acquisition. As a reminder - no deal is guaranteed. There were similar rumors of a Snowflake 🤝 RedPanda acquisition half a year ago and that went nowhere. In any case, major consolidation in the streaming market is inevitable in the next 5 years. 🔮 You can quote me that I expect all major Kafka players you know today to be gobbled up by someone. I again repeat what I said a few months ago - acquisition would be a sad way to end Apache Kafka's commercial journey. Kafka benefits from a strong Confluent and Confluent benefits from a strong Kafka. Confluent is THE reason Kafka is where it is today. But that's how life turns out sometimes. 💔 Confluent could remain independent and it'd continue to live well. It has ~$900m in cash and is non-GAAP profitable. It can last a long time. But will it grow and perform well? 🤷‍♂️ My bet, though, is it'll get sold. Who do you think is buying $CFLT? #Confluent #ApacheKafka #CFLT #EventStreaming

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You’re already bleeding pipeline. Not because your product’s wrong. Because your GTM plays are 3 years out of date. The best teams aren’t “doing more calls” They’re running engineering-grade sales motions that: • Only target accounts that are real this week. • Rewrite messaging before buyers tune out. • Rescue deals before the flatline. • Hit warm paths before cold. If you’re not: ⚡Mapping every account to your network before you touch it… ⚡Auto-refreshing your TAM weekly from live signals… ⚡Letting AI fix missing qual data in your pipeline… …you’re competing with one hand tied. Here are the 9 GTM Engineering plays you’ve wanted for years (but thought were impossible). They’re running now in the fastest-growing teams: 1️⃣ Living TAM — dead accounts never even make the list. 2️⃣ Warm paths before cold — 3–4× open rates. 3️⃣ Self-healing qualification — forecast-grade without babysitting reps. 4️⃣ Follow-up that feels handcrafted — closes like it, too. 5️⃣ Pipeline movement, not reviews — deals rescued in real time. 6️⃣ Time GTM to buyer intent — meetings at 2× normal rates. 7️⃣ Messaging that rewrites itself — stays sharp every week. 8️⃣ Multi-thread before single-thread — fewer collapse risks. 9️⃣ Zero-context-loss handoffs — retention starts day one. This isn’t “someday tech.” It's Clay. It’s just not in your stack yet. 💬 Which one would make you money this month? P.S. Agree? Repost to share with your network ♻️. And follow Jay Mount for more insights like this.

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Revenue operators are not losing their lunch to GTM engineers. Context ⬇️ Last week Pavilion posted an article about GTM engineers eating the lunch of RevOps and it contained some blatantly false statistics (including a few about RevOps Co-op) But there weren't any sources cited. So with the help of Rome Thorndike, VP of Growth at Firmograph, we analyzed the findings to see if we could back up the claims in the article with actual data. So what did our findings tell us? The short answer? No, GTM Engineering is not eating the lunch of revenue operators. Here are the different areas we dove into from the Pavilion article: Claim 1️⃣ : GTM Engineering Roles Are Eating Up Headcount Budget We pulled a list of 725 B2B AI & SaaS companies and only 2 of them are hiring GTM Engineers right now. Meanwhile, 43 of those companies are hiring RevOps. Claim 2️⃣ : RevOps Titles Dropped by 1K in North America Last Year This could very well be true, but we couldn't find data to support it. What we do know is macroeconomic trends hit everyone, not just RevOps. They are navigating the same headwinds as other white-collar workers. Claim 3️⃣: LinkedIn Job Ads Fell 19% from May 2024 to May 2025 We looked at the LinkedIn July Workforce Report, specifically at industries that hire a lot of revenue operators, and saw that hiring in these RevOps-heavy industries is down 9% - 17% over the same time period. So if RevOps-heavy industries are hiring less, it's no surprise that RevOps roles are also down. This isn’t a RevOps crisis . . . it’s a market reality. Claim 4️⃣: Open RevOps Roles On Indeed “Plunged” 22% YoY Context still matters. If the rest of the job market is doing well, and only RevOps is declining, then job platforms like Indeed should be doing just fine, right? Wrong. Once you look at more data, it becomes clear that any RevOps drop was part of a broader trend of declining white-collar work.‍ Claim 5️⃣: the RevOps Co-op Community Stayed Flat at Roughly 18,000 We added 3.3k members in the last 365 days . . . but this is private data, so lets consider some public data, shall we? We gained 6.8K LinkedIn followers over the past year, a 40% YoY increase. We also had 400+ people add their membership to their LinkedIn profile over the last year, that's a 29% increase. Any of those things sound "flat" to you? 🤔 Here's the thing. GTM engineering is a part of revenue operations. And I don't like publicly fighting with other companies or people on LinkedIn. But when you publish false statistics without citing any sources to support an opinion you have about an entire profession (revenue operations) and an entire community of those people (RevOps Co-op), well, I'm gonna find that offensive. And if you want to see all of our sources fully cited with links and screenshots, then check out our full blog post on the ROC website (link below in the you know where). Happy Friday y'all ✌🏻 #revops #revenueoperations

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On Clay’s $100M milestone. I’ve worked in GTM tech for 15+ years. Never seen such a unique and disruptive company. Every other GTM vendor pitch is now met with a response like: “Ok, but can’t we build this in Clay?” It’s a different verb. We *build* in Clay. We *use* our other GTM tech tools. As a founder, I'm watching their community flywheel. They created a movement around "GTM Engineering" that feeds itself. As Keyplay CEO, I'm navigating the frenemy line. We integrate. They could displace us. We must be excellent in our niche. As they run toward $500M, here's what I'm curious to follow: 1.) How does ZoomInfo respond? -- Clay is broadly disruptive, but especially to ZI.  -- Opposite approaches. ZI owns data. Clay orchestrates a network. -- ZI takes the blame if 1 field out of 100M is wrong. Clay's off the hook. -- But ZI has control. They can invest $ to make data reliable. -- If ZI makes data ownership an undeniable advantage (instead of a liability) for the AI era, that's a counterpunch. -- This is a battle for mindshare. Clay is winning today. 2.) Beyond Enrichment?  -- Clay is loved for enrichment. Now they do signals, sequencing, audience management.  -- It's getting more "solution-y" vs the core.  -- Smart for Enterprise consolidation. But high bar. -- The friendly people from Brooklyn now look hyper aggressive vs Gong, Outreach, 6sense, and probably more coming. -- Will customers love Clay as the ultimate consolidator? 3.) GTM Engineers or AI Agents?  -- The GTM Engineering movement is a good bet right now. AI and GTM data is too complex for business users.  -- But easy to see a world where business users work with AI Agents instead of GTM Engineers. -- Clay's "Sculptor" does this. Workflows with a prompt. -- If the UX becomes "talk to an agent," how do GTMEs shift? -- Do business users adopt? Do power users move to n8n, Claude Code, whatever comes next? I won't try to predict anything. Exciting times in this world. 🍿   What else are you watching with Clay's rise and the GTM space?

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99% success rate on a site we’d never seen before. Not bad for launch 🚀 Developers build on Deck to interact with websites that have no APIs, reading or writing data wherever users need it. What’s remarkable is the quality and consistency, even across new, unseen systems. Auth Agents are redefining what “integration” means. Check our guides if you're ready to start building: https://lnkd.in/eG4h6aMS

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What's the MACH Alliance for? It's a question I've been asked a great deal and to take it back to first principals it was to promote architectures (unashamedly technical) to support customer choice, excellent user experience and interoperability between best of breed vendors. Has that worked? That's a debate that can last hours - the good MACH projects, the ones on stage at the various MACH events show that it absolutely can work. Have there been horror stories - of course, plus ça change in IT. However, one thing that has been announced and now released is the MACH Common Data Model. Search my LinkedIn and you'll find a link to a (now unavailable) blog I wrote in 2019 (I think; Stacey Trimnell-Ritchard remembers it I hope as she said she used it in a marketing conversation back then so it did happen 🤣) calling for this and so it's brilliant to see it now exists: https://lnkd.in/eTFVgYKX What is it? In their own words: "The MACH Alliance Open Data Model (ODM) initiative is the natural next step in enabling interoperability across composable ecosystems. It serves as a semantic interoperability layer: a "Rosetta Stone" for understanding how to translate data between systems in hybrid commerce architectures. It provides canonical reference models and implementation guidance that enable architects to design effective data integration across commerce platforms while preparing organizations for AI-powered ecosystems." I'm interested and signed up and I know we're looking at how we can incorporate the ideas into Patchworks (the platform already supports this of course and it will help our AI mapping tools no end). Find out more by attending MACH X in October: https://lnkd.in/epHx2JVp

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Every org chart tells you where the next systems of record will emerge. Look for the roles that exist because software failed. RevOps exists because someone has to reconcile sales, finance, marketing, and customer success. DevOps exists because someone has to bridge development, IT, and support. Security Ops sits between IT, engineering, and compliance. These "glue" functions are a tell. They emerge because no single system owns the cross-functional workflow. The org chart creates a role to carry the context that software doesn't capture. An agent that automates that role doesn't just run steps faster. It can persist the decisions, exceptions, and precedents the role was created to produce. Palantir Technologies built what’s now a $400B+ company on prescribed ontologies: define the schema, then map messy enterprise data into it. The next $400B+ company will be built on learned ontologies. This is what Jaya Gupta and I mean by “context graphs”: not graph databases or structured memory, but living records of how decisions get made, learned bottom-up from how the work actually happens. cc Animesh Koratana

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HTOTW (Hot take of the Week): When Airbnb launched in 2008, all the tech industry commentators were tripping over themselves to predict the imminent downfall of the hotel industry. Here in the present, I’m about to head out on a two week trip to visit clients and partners in Tokyo, Auckland, Sydney, and Melbourne. I’ll be staying in a few hotels and even catching a few nights with some friends at their house in Croatia. But while I have stayed at plenty of AirBnB’s in the past, I won’t be hitting any on this trip. Nothing against AirBnB, it’s just sometimes hotels are a better fit. When I'm traveling for business and landing at midnight, I want my room ready and coffee in the lobby at 6 AM. I want to earn my points and know exactly what I'm getting. But sometimes I also want that unique apartment in Brooklyn or the farmhouse in Vermont that gives me something totally different. Both exist because they solve different problems. We're making the same mistake with AI right now. I keep hearing "We're just going to build an agent architecture that does everything" or "Traditional software is obsolete." But why? There's so much software that already works. It's reliable, cost-effective, and gets the job done. Now we want to replace it with AI models that are riskier, more expensive, and consume massive amounts of natural resources... because it's flashy. That's not where we're going to end up. Netflix killed DVDs because streaming was better in every way—cheaper, more convenient, same quality. There was literally no downside (okay, maybe you miss the DVD booklets). But Uber didn't kill taxis. Sometimes Uber is great. But sometimes I don't want to wait 20 minutes at the airport when there's a cab ready to go. The real question isn't "Can AI do this?" It's "Should AI do this?" AI will absolutely fill gaps and let us do new things we couldn't do before. But it's going to complement what's already working, not replace everything just because it can. Stop asking "How do we replace our systems with AI?" Start asking "Where can AI actually solve problems our current systems can't?" The future isn't AI-only. It's AI-integrated. Bon voyage!!!

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Hot take: the GTM Engineer trend is nothing more than the Growth Engineer 2.0. The major risk I see with this trend is the excessive fixation on building systems versus delivering actual outcomes. Why is that a problem? Let's break this down. Back in the early 2020s, when cash was cheap, I saw companies overspending on Growth Engineers, costing them upwards of $1M per year to build systems from scratch and collect as much data as possible. Yet most of those projects failed to drive real impact. It's easy for GTM ops and growth teams to get lost in data collection, integrations, and automations—it feels productive. But it only solves part of the problem. The real challenge of most GTM teams is not the data collection part—it's activation. What actually drives GTM performance: - Getting data in the right place - 15% - Structuring the data for action - 10% - Prioritizing items for action - 25% - Enabling the GTM team to use the data - 25% - Tracking and optimization activation - 25% So when roles like Growth Engineers—or the newer GTM Engineer—primarily focus on data systems and tooling, the risk is tackling those first 15% only but expecting an end-to-end fix. Unfortunately, most of these roles are still only focused on moving data around—setting up tools and systems instead of driving real outcomes. Is this just a trend, or is it a true game-changer? Are we really making a change across how organizations think about go-to-market, or just rebranding an old role with a newer title?

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Coffee thoughts. Most economic buyers don’t care about QA. They care about the outcomes QA enables. They’re not focused on defect counts, automation percentages, or test case execution graphs. They are focused on: Churn Revenue protection Growth velocity Customer trust Efficiency gains Upsell potential Referral and NPS impact Every one of these is directly shaped by product quality, even if most organizations never make that connection explicitly. And that’s where the real challenge lies. For better or worse, QA lives in one of the most misunderstood parts of the business. We’re often treated as a cost to be minimized instead of a lever to be invested in. Many companies don’t naturally link QA activities to the business outcomes their leadership cares about. But part of the problem is structural: a lot of QA professionals were never trained on how to communicate these connection, or even on the foundational QA principles themselves. Add in the organizational politics and the emotional intelligence required to manage up, and it’s easy to see why QA’s value often goes invisible. The QA teams that succeed are the ones that take ownership of this gap. They: Translate QA work into business impact Speak to leadership in economic terms, not QA jargon Make risk, revenue protection, and retention visible Proactively educate upward to close the understanding gap If QA doesn’t tell its story, it won’t get told. Quality is a growth engine, a retention driver, and a trust builder, but only if we learn to communicate its impact in the language the business already understands. #qa #leadership #metrics

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Despite the many benefits of Data #CleanRooms they may turn participating parties into Data Brokers as per California’s DELETE Act if they happen to bring 3rd party data (no direct relationship) into the platform, triggering a registration and tech alignment requirement (and hefty fines if dates are missed). (Presentation by California Privacy Protection Agency and Ben Isaacson on the DELETE ACT and the new #DROP platform that integrates all data brokers for consumers to place a single request.) Privacy Law Section of the California Lawyers Association. #AdTech

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