AI vendors can't price what they can't define yet, which means your 2025 software budget is quietly becoming a guessing game.

This week's edition covers:

  • Kevin's Take: why AI pricing pages are a mess, and what it signals about vendors who don't yet know what they're selling
  • The Signal: Signal: what 821 SaaS case studies reveal about proof that actually lands, plus why digital channels are getting too noisy to carry your demand gen
  • Tools & Tactics: the Google Ads seasonality setting quietly hurting Black Friday campaigns, and a founder's Claude Code recipe for viral LinkedIn infographics
  • Quick Links: AI's GTM impact nearly doubles in a year, brand budgets climb as AI flattens differentiation, LinkedIn posts turned into TechCrunch coverage

Kevin's Take

The AI pricing page is broken because the vendors don't know what they're selling yet

Every time I land on an AI product pricing page lately I have the same reaction, which is that I have no idea what I'm actually buying. Elena Verna's piece on AI pricing and packaging got at something I've been feeling but hadn't quite put words to, which is that the vagueness isn't a design failure, it's a sign that the vendors themselves haven't figured out what the unit of value actually is yet.

Seats worked for SaaS because a seat was a human doing a job, and you could roughly reason about what that human was worth to you. Tokens, credits, messages, workflows, agent runs, none of these map cleanly to a buyer's mental model of value, so the pricing page ends up being this hedge where the vendor is basically saying, we don't know what you're going to do with this, so we'll give you a bucket, and when the bucket runs out we'll talk.

I'd push this further than Elena does, because I think it's a bigger problem for the buyer than it is for the vendor. When I can't parse your pricing I can't build a business case, and when I can't build a business case I can't get budget approval, and when I can't get budget approval I either don't buy or I buy small and hedge, which is why so many AI tools are stuck in pilot-forever mode right now.

I've been on both sides of this in the last six months, trying to price our own agentic work for customers inside Flow, and the thing we landed on isn't elegant. We price against the outcome the customer was going to pay a human to produce anyway, and we let the token math sit on our side of the ledger. It's not a model that works on a self-serve pricing page but it's honest, and it closes.

If you're sitting on a pricing page right now that reads like a telecom bill, the question I'd be asking isn't how do we make it clearer, it's do we actually know what we're selling, because clarity on the page comes after clarity on that.

That's it for this week. Talk soon.

— Kevin Kerner, CEO, Mighty & True

The Signal

What 821 B2B SaaS case studies reveal about convincing proof (1 min read)

DataDab analyzed 821 case studies from 226 B2B SaaS companies to identify patterns in how vendors present proof of results. The research quantifies what elements separate convincing case studies from generic ones.

Why it matters: If your case studies read like every other vendor's, this data shows you exactly which proof points and formats actually build credibility with buyers.

New research says digital channels are getting too noisy for B2B (1 min read)

Original research comparing noise levels across organic social, cold outreach, search, and paid media finds digital channels increasingly saturated, while citing 6sense data showing 94% of B2B buying groups now research independently before engaging sales. The study argues events may be regaining relative value as online channels get noisier.

Why it matters: If your demand gen mix is still weighted toward digital-only tactics, this data gives you a concrete reason to reassess budget allocation toward events and high-signal channels.

Tools & Tactics

Optmyzr's analysis of 5,000+ Google Ads accounts found that automated seasonality adjustments often hurt Black Friday performance rather than help it. The report recommends manual bid strategy overrides and AI agent support for BFCM 2026 planning instead.

Why it matters: If your team runs Google Ads for a B2B tech company with seasonal campaigns, this data tells you to rethink automated seasonality settings before your next high-stakes push.

How one founder built viral LinkedIn infographics in Claude Code (1 min read)

A founder generated 805K LinkedIn impressions in 28 days and 85% of signups using animated infographics built with Claude Code, and the author shares a hands-on DIY guide plus open-source skills to replicate it.

Why it matters: You can replicate a proven LinkedIn content format in Claude Code without hiring a designer, with the exact skills shared for free.

Strategy

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