Best of LinkedIn: Go-to-Market CW 04/ 05

00:00:00: This episode is provided by Thomas Allgeier and Frennis, based on the most relevant LinkedIn posts about go-to-market in calendar weeks four and five.

00:00:08: Frennus is a B to B market research company helping enterprises gain the customer competitive and positioning insights needed to drive GTM success.

00:00:17: And today we're going to try and cut through all the noise from those last two weeks.

00:00:21: So weeks four and five of twenty twenty six.

00:00:24: That really is the mission.

00:00:25: I mean if you've been on LinkedIn lately it just feels like you're drowning in AI announcements.

00:00:30: It's overwhelming.

00:00:31: It really is.

00:00:32: But when you actually stop scrolling and look at what the smartest people are actually saying, the signal has shifted.

00:00:38: It's not just more AI anymore.

00:00:40: No, not at all.

00:00:41: The conversation has definitely moved past that whole.

00:00:44: look at this cool magic trick phase.

00:00:47: We're seeing this hard pivot now toward, I'd call it... Pragmatic simplification.

00:00:51: Pragmatic

00:00:52: simplification.

00:00:53: I like that.

00:00:54: Yeah, it's all about execution speed and maybe more importantly, fixing the insane complexity we've all accidentally built over the last few years.

00:01:01: Fixing the mess.

00:01:03: That's a great way to put it.

00:01:04: Okay, so we saw four Pretty distinct themes emerge.

00:01:08: We're going to talk about something called vibe coding, the confusing rise of the GTM engineer, this massive trend of de-bloating your stack, and then a big reality check on the strategy.

00:01:19: That was good.

00:01:20: So let's dive into that first theme, AI as an execution layer.

00:01:24: OK, so this is a really, really crucial distinction.

00:01:27: For the longest time, we were just chatting with AI.

00:01:30: You know the drill, right?

00:01:31: You open a window, you type a question.

00:01:32: You get some

00:01:33: text back.

00:01:33: You get text back.

00:01:34: But what we're seeing in these last two weeks is a move away from that.

00:01:38: We are now building with AI.

00:01:40: Okay, but what does that actually look like for, say, a sales leader who doesn't code?

00:01:46: Because building sounds intimidating.

00:01:48: It's actually the opposite.

00:01:49: It's about removing that intimidation.

00:01:51: It's not about being a prompt engineer anymore.

00:01:53: You're knowing the magic words.

00:01:55: It's about engineering actual workflows.

00:01:58: Chase Diamond had this fantastic example with Apollo's AI Assistant.

00:02:02: Right, I saw that one.

00:02:03: He was comparing the old way versus the new way.

00:02:05: And the old way was, I mean, it was a nightmare.

00:02:07: Just clicking through endless menus, configuring triggers.

00:02:11: Oh, and trying to stitch it all together with Zapier.

00:02:13: And you'd break it three times before it even worked.

00:02:15: Or you'd just give up and put in a ticket for an engineer and then wait two weeks.

00:02:19: But Diamond, he just described what he wanted in plain English.

00:02:22: Just normal

00:02:23: language.

00:02:24: Like, fine leads who look like this and then send them an email that says that.

00:02:28: Exactly like that.

00:02:30: He just talked to it, and the AI built this whole multi-step workflow in, like, minutes.

00:02:36: Wow.

00:02:36: And a big deal here isn't just that it's faster.

00:02:39: It's that the barrier to entry for building this stuff has just collapsed.

00:02:43: The fastest operators aren't clicking buttons anymore.

00:02:46: They're just talking to the software.

00:02:47: That removes a huge bottleneck.

00:02:49: It also leads to this term I saw from Jonathan MK, which I think perfectly defines this moment.

00:02:54: He calls it... Vibe coding.

00:02:56: Vibe coding.

00:02:57: It sounds, I don't know, almost like a joke.

00:02:59: It does, right?

00:03:01: But what are we actually talking about here?

00:03:02: Is this just Gen Z slang for programming?

00:03:05: It is a bit of slang, yeah.

00:03:07: But the idea behind it is really serious.

00:03:09: Vibe coding is using AI to write code based on the, you know, the vibe or the general idea of what you want instead of writing every single line of syntax.

00:03:19: Okay.

00:03:20: And Jonathan's point is that the winners in twenty twenty six, they won't be the people with the best prompt libraries.

00:03:26: They'll be the ones who can vibe code a tool faster than the IT department can even schedule a kickoff meeting.

00:03:31: So it's all about speed.

00:03:33: That's the leverage.

00:03:34: Exactly.

00:03:34: Think about it.

00:03:35: A sales rep is on a discovery call.

00:03:37: The prospect asks about ROI for their specific use case.

00:03:41: And the rep usually says, let me get back to you on that.

00:03:44: Right.

00:03:45: But in twenty twenty six, that rep.

00:03:47: VibeCodes, a custom ROI calculator in a tool like Riplet right there on the call in three minutes and just shares their screen.

00:03:54: That's a total game changer.

00:03:55: You're not setting a PDF later, you're handing them a custom tool live.

00:03:59: Or a marketer who spins up landing page variants in something like Lovable during a meeting instead of waiting for a two week dev sprint.

00:04:05: It just shrinks the time between idea and reality.

00:04:08: I do want to add a layer of caution here though, because if everyone is VibeCoding tools all over the place, aren't we just creating a ton of You know, junk.

00:04:18: Maja Voja brought up this really important idea.

00:04:20: she calls engineering context.

00:04:22: Maja is always so good at focusing on the revenue impact, not just the cool tech.

00:04:27: What was her take?

00:04:28: So she basically spent fourteen days living inside Claude Code, just geeking out on workflows.

00:04:34: And her big takeaway wasn't, I built this fast.

00:04:36: It was about building workflows that compound knowledge.

00:04:40: Okay,

00:04:40: I like that.

00:04:41: The one that really stood out to me was this idea of every call updates everything.

00:04:45: Every call updates everything.

00:04:46: Break that down for me.

00:04:47: Okay, so normally a sales call happens, maybe it gets recorded, and then it just dies in the CRM.

00:04:52: It's gone.

00:04:53: CRM preview, yep.

00:04:54: Maja built a workflow where a single call transcript autumn automatically updates the battle cards for sales and it updates the ideal customer profile signals.

00:05:03: It's engineering the context back into the system, so the next rep is just automatically smarter.

00:05:08: Wow.

00:05:08: So data becomes a living asset, not a static record.

00:05:11: Exactly.

00:05:12: And she also built a living objection library.

00:05:15: So instead of a Google Doc, the system ranks objections by how often they come up and automatically pairs them with the winning rebuttal from recent calls.

00:05:25: It's so smart.

00:05:26: And this empowers people to solve their own problems.

00:05:28: Dave Block had a perfect example.

00:05:30: He's not a software engineer.

00:05:31: He's in solutions engineering.

00:05:33: But... He used AI coding agents, Codex, Claude Code, to build a Google Calendar to Salesforce integration in seven days.

00:05:42: Seven

00:05:42: days for a Salesforce integration.

00:05:43: That's usually a six-month project in a budget meeting.

00:05:46: I know, right?

00:05:47: And he did it because his solutions engineers just were not logging their activity.

00:05:51: They live in their calendars, not in Salesforce.

00:05:54: So he built a tool that syncs the calendar events right to the opportunities.

00:05:58: He solved the age-old problem of, if it isn't in Salesforce, it didn't happen.

00:06:01: He did.

00:06:03: takeaway isn't that everyone needs to code.

00:06:05: It's that GTM leaders can now build working prototypes to prove an idea works without waiting for engineering to give them a spot on the roadmap.

00:06:13: But that brings us to a huge friction point.

00:06:16: If everyone is building tools, if the sales VP is coding, if the marketing manager is building apps, who actually owns this?

00:06:24: Because we are seeing this new title just explode everywhere, the GTM engineer?

00:06:29: It

00:06:29: is definitely the buzzword.

00:06:30: of early twenty-twenty-six.

00:06:32: Matt Schulman from Pave, she had some really interesting date on this.

00:06:35: He's seeing a steep adoption curve.

00:06:38: Companies are hiring for this role like crazy.

00:06:41: So what's the money look like?

00:06:42: Is there a premium on this new title?

00:06:44: It's interesting, they're making about nine percent less than a regular software engineer.

00:06:48: But, and this is the key part, they're making nineteen percent more than a traditional sales or marketing ops person.

00:06:54: Okay, so that signals a skill gap.

00:06:56: The market is saying we need more than just ops, but we don't need a full-stack

00:07:00: product developer.

00:07:01: Exactly, but it also sounds like a really messy undefined role.

00:07:04: And Jeremy Ross, he waved a big red flag here.

00:07:07: He warned that GTN engineer is hiding three totally different jobs.

00:07:11: I can see that.

00:07:11: How did he break them down?

00:07:13: So first is the GTM hacker.

00:07:14: Their job is just figured out fast.

00:07:16: They're the vibe coders.

00:07:18: Then you have the GTM systems engineer.

00:07:20: Their job is make it repeatable.

00:07:22: They're connecting all the pipes.

00:07:24: And finally, the GTM platform engineer who was there to scale the infrastructure.

00:07:29: So if you hire a hacker when you really need a platform engineer,

00:07:33: it's a disaster.

00:07:34: You either get spaghetti code that breaks in a week because the hacker didn't think about scale, or you get someone who overengineers a simple test because they have that platform mindset.

00:07:44: It's a mismatch.

00:07:45: There's another risk here too, which Noamie J pointed out about the whole LinkedIn bubble.

00:07:50: Oh, this was such a good point.

00:07:51: She kind of went after the vanity metrics.

00:07:53: She really did.

00:07:54: She said, we're confusing visibility with expertise.

00:07:57: Just because someone posts a viral video of some crazy complex workflow in clay.

00:08:02: Right,

00:08:02: or gets two hundred likes.

00:08:03: It doesn't mean they're a good GTM engineer.

00:08:05: And her point was that the real GTM engineers are too busy shipping systems that actually make money to post tutorials about it.

00:08:12: That's such a crucial insight for anyone hiring.

00:08:15: A workflow that looks cool but breaks in production isn't expertise.

00:08:19: Expertise is a system that just works.

00:08:22: Look for the quiet builders, not just the loud posters.

00:08:25: And speaking of who to hire, Tori needles through a bit of a curveball.

00:08:30: She's arguing that if twenty twenty five was the year of the GTM engineer, then twenty twenty six is the year of the AI GTM strategist.

00:08:38: I really like that distinction.

00:08:39: Because you still need someone to connect all the dots.

00:08:42: Exactly.

00:08:43: The strategist isn't in the weeds coding.

00:08:45: They're focused on the big picture.

00:08:47: You know, system-wide friction, governance.

00:08:50: Making sure all these vibe-coded tools actually serve the business strategy and aren't just a bunch of cool science projects.

00:08:55: Which is the perfect segue to our third theme.

00:08:57: Because if everyone is building and buying all these tools, you eventually just hit a wall.

00:09:01: The bloat becomes a real problem.

00:09:03: And we saw this huge shift in sentiment towards de-bloating.

00:09:06: GTM stack simplification.

00:09:08: It's like the morning after a software buying binge.

00:09:11: You wake up with a headache and a credit card bill full of subscriptions you don't even use.

00:09:15: Huh, exactly.

00:09:16: The consensus is that stacks are just way too heavy.

00:09:18: Perry Tomar and Immanuel Gabirajji.

00:09:21: both really hit on this.

00:09:23: The stack doesn't need more tools.

00:09:24: It needs fewer better tools that actually work.

00:09:27: And what's wild is when you look at the lean stacks that people are sharing, the same or few names just keep popping up over and over.

00:09:34: It's becoming this sort of consensus stack for twenty twenty six.

00:09:38: It

00:09:38: really is.

00:09:38: It's so consistent.

00:09:40: You see clay for all the data and enrichment.

00:09:43: Yep.

00:09:43: You see Apollo for sourcing the contacts.

00:09:46: You instantly or smartly for the actual email outreach, then then you see NADN as the glue.

00:09:52: Let's pause on NADN.

00:09:53: For anyone who doesn't know, it's a workflow automation tool, kind of like Zapier, but on steroids.

00:09:57: It lets you build much more complex, custom logic.

00:10:00: Right, and it seems to be the thing holding these lean stacks together.

00:10:03: It lets you connect all these best-in-class tools without needing some massive, expensive, all-in-one platform.

00:10:08: And speaking of expense, MarcoStrom Kadegbach laid out a full GTM stack from start to finish for under three thousand dollars.

00:10:16: wait

00:10:17: like three thousand a month

00:10:18: about twenty six hundred a month yeah for a full enterprise motion.

00:10:22: that's just.

00:10:23: it's crazy cheap compared to the six-figure contracts people used to sign.

00:10:27: it is and sim it in took this even further.

00:10:28: he shared this really extreme example of consolidation.

00:10:33: he turned eleven different gtm tools into a single weekly email.

00:10:37: what was his rule.

00:10:38: it's ruthless.

00:10:40: if a tool doesn't move pipeline or signal it gets cut

00:10:44: period.

00:10:44: I love that discipline.

00:10:45: But there is a technical trap here that Aurelian O'Bear pointed out.

00:10:49: He warned about these closed stacks.

00:10:52: This is such a critical point that gets missed in the demo.

00:10:55: O'Bear's argument is that most of these new AI for GTM tools are just a chatbot glued on top of a closed database.

00:11:02: So

00:11:02: they look great on the surface.

00:11:03: They look amazing when the salesperson shows you, but they totally fail in production.

00:11:07: Why

00:11:07: do they fail?

00:11:08: because they can't access your real data, your customer truth that's in your warehouse, and they can't reliably write back to your CRM.

00:11:14: The differentiator isn't the AI anymore.

00:11:16: It's whether the platform is, as he puts it, open or a cage.

00:11:19: Open or a cage.

00:11:21: That's a great line.

00:11:22: You don't want your customer data trap, no matter how smart the chatbot

00:11:25: is.

00:11:26: Exactly.

00:11:26: If you can't get your data out to analyze it somewhere else, the tool is a dead end.

00:11:31: OK, so let's recap.

00:11:32: We have vibe coding for fast execution.

00:11:34: We have GTM engineers building the systems.

00:11:37: We have simplified open stacks.

00:11:39: But all of this tech is completely useless if the strategy behind it is garbage.

00:11:45: Which brings us to our last theme, the anti-hype.

00:11:48: This big call to get back to the fundamentals.

00:11:51: This was so loud in weeks four and five.

00:11:53: There's this feeling that AI is just exposing bad strategy.

00:11:57: HLS put it very bluntly.

00:11:58: He said, quote, AI won't fix your broken GTM.

00:12:01: It's that amplification effect.

00:12:03: AI is just a multiplier.

00:12:04: If your positioning is bad, you don't know who you're selling to.

00:12:07: Well, AI just helps you scale that bad positioning faster.

00:12:10: So you can spam ten times as many people with a bad message.

00:12:12: Which is noise, not growth.

00:12:15: And he also said that a feature is not the same as differentiation.

00:12:17: You can't just automate your way out of having a product nobody understands.

00:12:21: And this ties back to what Andreas Wernicke was saying about context.

00:12:24: He calls context the stable shape in all the chaos.

00:12:28: I love that phrase.

00:12:29: Can you expand on it a bit?

00:12:30: He's saying that the tech changes constantly.

00:12:33: The models update, the tools come and go, but your company's taste, its values, its specific knowledge.

00:12:40: That's the stuff that can't be abstracted away by an algorithm.

00:12:44: That's what keeps you stable.

00:12:46: And Thomas Harrison from Sherpa had some scary numbers to back this up.

00:12:49: He said the whole volume game, the spray and pray is struggling.

00:12:52: Cold email response rates are down to five percent.

00:12:55: Five percent.

00:12:56: That's basically the death of that strategy.

00:12:58: It

00:12:58: is.

00:12:59: If ninety five percent of your market is ignoring you, you have a serious problem.

00:13:03: He says the new currency is context.

00:13:05: You have to know why you're reaching out to this person right now.

00:13:08: But fixing this isn't just about data.

00:13:10: It's a leadership problem.

00:13:12: Christopher Campbell and Heidi Hattendorf both talked about an ownership crisis.

00:13:16: Campbell's point was so sharp.

00:13:17: He said AI projects aren't failing because the tech is bad.

00:13:20: They're failing because no one owns the GTM outcome.

00:13:23: It's

00:13:23: a leadership void.

00:13:24: People treat it like a capability upgrade.

00:13:26: Like, oh, we have AI now.

00:13:27: We'll just do the same thing, but faster.

00:13:29: Exactly.

00:13:30: And Hattendorf added that revenue motions are not the same as GTM motions.

00:13:36: That sounds like semantics.

00:13:37: What's the real difference?

00:13:38: It's all about system design.

00:13:40: A GTM motion is an activity, like we do outbound.

00:13:44: A revenue motion is the engineered system for how a stranger becomes a customer.

00:13:49: Growth breaks when your GTM motion isn't actually mapped to a clear revenue outcome.

00:13:55: So the difference between doing things and making money?

00:13:59: Pretty much, yeah.

00:14:00: To wrap this up, I want to bring in Bill Stathopoulos' framework on the awareness spectrum, because it solves a problem I see constantly.

00:14:06: Oh,

00:14:06: this is so practical.

00:14:08: His whole point is that teams are using level one messaging on level five buyers.

00:14:13: Okay, define those levels for us.

00:14:14: So

00:14:14: level one is reassurance.

00:14:16: It's messaging like, we're the best, trust us.

00:14:19: That works for someone who already knows they have a problem and knows who you are.

00:14:22: And level five.

00:14:23: Level five is, I have no idea who you are, and I don't even know I have this problem yet.

00:14:27: Which is where all outbound lives.

00:14:29: Cold emails, cold calls, it's all level five.

00:14:32: Exactly.

00:14:33: But teams write emails like they're talking to a warm referral.

00:14:36: They pitch the solution right away.

00:14:39: Stathapoolis says you have to match the message to the awareness level.

00:14:42: If they're cold, you don't sell the solution, you sell the problem.

00:14:46: It sounds so simple.

00:14:48: But AI makes it so easy to forget because you can just generate generic level one copy all day long.

00:14:54: That's the trap.

00:14:55: AI is a yes man.

00:14:56: It'll write a pitch if you ask it to.

00:14:58: It won't tell you, hey, maybe start with some value first.

00:15:01: That strategic part, that has to be human.

00:15:03: And that brings us right back to the beginning.

00:15:05: We started with AI as an execution layer, using tools to build faster.

00:15:10: But we end with this realization that if you build the wrong thing or send the wrong message, you're just failing at the speed of light.

00:15:17: It's a huge paradox, isn't it?

00:15:19: The tools are faster than ever, which means the need to slow down and actually think is higher than ever.

00:15:23: So what's the big takeaway for you listening right now?

00:15:26: I think it means the opportunity in twenty.

00:15:28: twenty six is massive, but it takes discipline.

00:15:31: the barrier to building is gone.

00:15:33: You can vibe code a tool, you can automate a workflow, you can get your stack down under three grand, that power is there.

00:15:40: But the barrier to thinking is higher than ever.

00:15:42: You need that GTM engineer mindset, whether it's your title or not, you have to focus on context, on open stacks that don't cage your data, and on matching your message to your buyer's reality.

00:15:53: Don't just prompt, build.

00:15:55: but make sure you build with a blueprint.

00:15:57: That is the perfect place to leave it.

00:15:59: If you enjoyed this episode, new episodes drop every two weeks.

00:16:03: Also, check out our other editions on account-based marketing, field marketing, channel marketing, MarTech, social selling, and AI in B to B marketing.

00:16:10: Thanks for listening.

00:16:11: And don't forget to subscribe for the next deep dive.

00:16:14: See you then.