I just spent two weeks tracking what's actually working in B2B GTM vs. what we wish was working. The gap is wider than ever, wider than Grand Canyon and that stuff is BIG. Here's the uncomfortable reality no one's talking about: → AI SDRs sound great in demos, fail spectacularly in production → Most GTM systems are built around the wrong buying signals → Your "perfect" RevOps stack might be accelerating your decline → Every GTM playbook has an expiration date (and yours just expired) But some teams are quietly crushing it. They're not chasing #AI #hype. They're engineering systems around actual buyer behavior. I analyzed 18 posts from operators shipping real results: Stan Rymkiewicz's AI copywriting workflow that scores 0% on AI detectors Janis Zech's findings after talking to 450+ RevOps leaders about GTM AI Lillian Pierson, P.E.'s wake-up call: "GTM Engineer" isn't BS—it's real Jamie Walsh's brutal truth: systems don't fix unclear signals Dimitar Stanimiroff on why every playbook breaks (and always will) The pattern? Winners aren't building bigger stacks. They're finding clearer signals. They're not replacing humans with AI. They're making humans 10x more effective. They're not following last year's playbook. They're rebuilding for the environment that exists today. This week's newsletter breaks down: ✅ What AI can and can't actually do in GTM (with receipts from 450+ teams) ✅ The 5-step workflow that creates AI content that doesn't suck ✅ Why your buying signals matter more than your CRM ✅ How to measure ABM with a simple 50/50 split test ✅ The discovery framework that actually qualifies deals ✅ Why "GTM Engineer" is now a real role at 89+ startups ✅ What your 2026 budget should actually be (by sector) If you're frustrated with GTM right now, it's not you. The old playbooks died. The new ones are being written by operators who understand one thing: Clarity creates compounding. AI just accelerates it. 📬 Full breakdown in this week's newsletter (link in comments) What's your biggest GTM frustration right now? P.S. Shoutout to Scott Finden, Yurii Veremchuk, Janis Zech, Stan Rymkiewicz, Jamie Walsh, Tim Busschops, Dimitar Stanimiroff, Lillian Pierson, P.E., Canberk Beker, David Zeledon, Sumit N., Jen Allen-Knuth, Dan Ptak, Andrei Zinkevich, Kate Syuma, and Kieran Flanagan for the insights that made this newsletter possible.

Been excited to be at the Tekpon conference! Although I had some serious cold and fever the night before :( and missed on a lot of great convos. Some hot insights for you like my tea this morning. AI scales the playbook — but relationships still close enterprise deals. The new norm is at least 1 million $ per employee. Sara Maldon was talking about some pretty cool automations you can pull with make.com, cut the research time and increase the customer facing time with clients. Vincent Jong gave some good benchmark on how efficiency should look like in the AI first world. Also a very helpful table about what AI can do well and what it cannot do well. Frank Sondors 🥓 Bill Stathopoulos Jeko Kolev had a pretty cool conversation about AI's role in today's #GTM strategy. Todd Paris came with some incredible insights on difference in terms of ranking factors for LLMs, what do they like, their personality and how to optimize for that. Some personal thoughts: Found: AI wins when it automates sales ops (post-call follow-ups, CRM enrichment, research), freeing reps to focus on human conversations. Realized: ABM + co-created content = faster access to decision-makers and higher-quality pipeline. Identified: First‑party signals (hires, multi‑language site relaunches, investor moves) drive better account selection than noisy intent feeds. Found: Enterprise buyers want bulletproof solutions — GDPR, SOC2, and provable ROI beat flashy demos every time. Realized: ICP clarity is non‑negotiable — know who uses the product vs who signs the check (marketing vs CFO/ops). Identified: Product‑led, API‑first businesses that prioritize engineering scale revenue per employee and reduce dependence on large sales teams. Noted: Top-of-mind benchmark — aim for ~$1M revenue per employee in AI-first product companies; anything less risks being out-competed on efficiency and cost structure. Credibility quote: “AI helps SDRs by doing the grunt work — prospect research and CRM enrichment — but it doesn’t replace the relationship you need to close enterprise deals.” This left me with a question: If you’re scaling #GTM with #AI this year, are you prioritizing automation or proving measurable ROI to finance — and how are you or are you tracking revenue-per-employee as a success metric?

In this newsletter, I'm bringing you, as usual, the most actionable and truthful stuff I've found over LinkedIn, Twitter, and Substack. Subscribe more to get more insights and learn something. I hope you do learn something from this. Really! P.S. There are a few good tips, such as: Why you should have three potential lists in your ABM outreach How you can design a workflow which will help your product-led growth

Everyone's drowning in AI hype right now. "AI will replace your entire team!" "We built an AI that does everything!" "SDRs are dead!" But here's what nobody's telling you: most of it is bullshit. I went and found the real stuff. Not the hype. Not the promises. The real results. And what I found? It's way more nuanced than the LinkedIn gurus want you to believe. The First-Wave AI Companies Just Had Their Extinction Event 11x and Artisan raised $500M+ selling you on "AI employees that work 24/7." Now? They've completely scrubbed their messaging. They talk about "augmenting your team" instead. That's not a pivot. That's an extinction event. Your Reps Are Showing Up to Meetings with ChatGPT Prep (And Buyers Can Tell) Enterprise CROs are struggling with AI not because the technology is bad, but because their teams don't know when to use it and when to be human. Key pain points are missed. Critical deal context is ignored. One CRO put it perfectly: "We're enabling them on where human judgment is needed and when NOT to use AI." But Wait—AI SDRs CAN Work (If You Actually Know What You're Doing) Snowflake (200+ SDRs) built an AI workflow that gets a 7.5% reply rate vs 0.5% before. That's a 15x improvement. They open-sourced the entire playbook. The key? Specialized prompts, clean data, XML structure, and letting AI score its own work. They still have a full SDR team—but that team isn't drafting emails anymore. That's AI's job now. Here's What's Actually Working in December 2025: ✅ AI augmentation beats AI replacement - Every time. No exceptions. ✅ Human judgment is the moat - AI makes everyone sound the same. Your differentiation is knowing when NOT to use it. ✅ Governance beats tools - Most teams are overestimating AI's novelty and underestimating the operational backbone. ✅ Agentic thinking wins - Stop asking "How can AI help my team?" Start asking "Which outcomes can an agent own end-to-end?" ✅ Buyers are changing faster than you think - AI agents will filter your content. Generic content won't make it through. My latest newsletter breaks down:  → Why first-wave AI companies pivoted (or died) → Jason Lemkin's top 10 AI SDR learnings from SaaStr → How Snowflake, Personio, and Glean are actually using AI → LinkedIn's B2Believe insights on AI agents as buyers → The timeless marketing principles that still beat AI slop The companies that win won't be the ones chasing every shiny tool. They'll be the ones who understand that AI should amplify humans, not replace them. Read the full breakdown in this Newsletter, Part 2 is coming tomorrow! What's your take? Are you seeing AI augmentation work better than replacement in your org? #AI #GTM #B2BMarketing #SalesEnablement #AIinSales

All the kids in the block are flexing "180 meetings in 30 days" "Pipeline on Autopilot" while missing the actual game. The standard B2B sales cycle is 218 days. That's 7+ months. Official sources. Getting the meeting? That's the easy part. Like getting a date vs staying married. Here's what actually matters: Month 1-2: Discovery and alignment Month 3-4: Building the business case Month 5-6: Procurement hell Month 7: Legal review and signatures Stop optimizing for vanity metrics. The meeting means nothing if you can't manage the lifecycle. The real skill? Keeping deals alive for 218 days without losing momentum. Bet nobody teaches you that.