🧠 Education isn't an AI advantage

ISSUE #277

Happy belated New Year!

I'm excited to launch the first weekly roundup of 2026. Before we dive in, a few quick notes from the editor (aka, me!).

My big theme for this year is clarity, which means continuing to refine the focus and direction of the publication, without compromising on value.

While the mission remains the same (helping SaaS founders and GTM operators inform their next growth strategy), I'm still iterating on the structure and format (you know…new year, new me kind of vibes).

What the roundups look like:

• Social reads – posts I’ve bookmarked

• Industry observations – insights and how I think about them

• From the trenches – original content sourced from the community

• Articles to bookmark – resources to help build your next growth play

Core principles (what remains constant):

• Consistency is the exchange rate of all media

• The application of taste is what you filter out

• The form factor for valuable content is personal & lived experiences

I'll turn these bullets into a manifesto at some point. But in the meantime…

Let's dive in!

Ian at SaaS Weekly

SOCIAL READS
Short posts from LinkedIn

• AI products are still disappointing in GTM tech

• Profound pulled the data - ChatGPT’s search intent queries

• a16z announces its $15 billion raise - a Not Boring deep dive

INDUSTRY OBSERVATIONS
Insights and perspectives + how to think about them

The hill we’re willing to “die” on
Marketing budget | Carilu Dietrich, Hypergrowth Leadership

If you're in charge of marketing spend, then you've likely felt this tension before. You say you want to invest more in brand, but when budget cuts come, demand gets protected first. Why is that?

Carilu Dietrich & Ray Rike surveyed 168 B2B marketing leaders and found the gap: the median budget split is 70% demand and 25-30% brand.

But when asked about the ideal mix, CMOs said they'd prefer 50% demand and 40% brand. The problem is that only a third of companies can directly tie brand investment to pipeline. Which means, measuring the impact of brand spend is still the blocker.

This creates a vicious cycle: brand is under-measured, so brand is under-defended, so brand gets cut first, so demand efficiency suffers, and pressure increases.

The data is not revolutionary, and it points to a known problem – but hope is not lost. Here's what you could do about it:

• First, educate leadership on how brand spend drives awareness, market education, and demand efficiency (here's a good resource).

• Second, build internal workflows that connect spend to pipeline (e.g., using LLM orchestrations to allocate a percentage of pipeline dollars to brand attribution). Be careful, though. Understand that the data isn't deterministic – it won't drive the decision by itself, but it shows direction and impact, which could help defend the budget.

The education advantage in AI for any SaaS company
Brand Strategy | Jamin Ball, Clouded Judgement

Have you ever stopped and asked: what's the purpose of "educating" your audience? We may know why we produce, say, thought leadership content – but what’s the goal? What’s the principle behind it?

To me, the function of education is to build a following. To build a tribe. You achieve this not by teaching your audience what the product does or why they should buy it, but how to think about the problem.

When you build belief behind how the problem should be solved, then you compete less on features and more on a worldview – you compete on the way things should be done.

We've seen this pattern play out many times. Before Clay, GTM was thought of as execution-heavy, ad hoc, ops-owned. After Clay, GTM is seen as a system that could be engineered. Before HubSpot, marketing meant outbound and push campaigns. After HubSpot, marketing means inbound pull and earning attention through content.

So while AI companies are getting the spotlight (in this article), the principle applies to any company teaching how systems should be built OR how work should be done. The framing changed, but the principle remains the same.

The best content forces people to pick a side – where philosophy becomes the battleground. Clay's GTM Engineer and Seeking GTM Alpha posts are two resources to be inspired by. So next time you make a content bet, define your brand's worldview, not just what your product does.

FROM THE TRENCHES
Features from industry operators

Reach is not a vanity metric
Marketing strategy | Mike Northfield, Artifacts of Influence

ARTICLES TO BOOKMARK
Resources to design your next growth play

5 ideas to drive upgrades
Pricing & packaging | Rob Litters, Good Better Best

Driving organic upgrades to a higher tier isn't always easy. Sometimes there's a natural motion – more volume consumption means you pay more. Or users upgrade to higher tiers that offer “enterprise” features, like integrations.

The key is making the step change feel natural, not forced. Unreasonable commitments or lack of value in lower tiers, and you risk losing customers altogether.

PricingSaaS broke down five tactics to increase upgrades, pulled from real examples of companies using them – here are the first three:

• Use billing cycles to filter for commitment – AiSDR shifted to quarterly billing because their value isn't instant and requires setup.

• Shift limits from users to usage metrics – Arize removed user limits and tightened usage metrics instead.

• Introduce flexible add-ons – DeepL bundled Write Pro AI into Business but kept it as an add-on for lower tiers.

How Rally reimagined win analysis with AI
GTM Operations | Mandy Cole, GTM in Practice

Every company wants deeper insight into why they win and lose deals. But getting that insight is time-consuming – interviews take hours to schedule, conduct, and analyze.

Rally's CMO, Juliette Kopecky, tested using AI (aka, LLM workflows) to build a repeatable win-loss process at scale. Instead of scheduled buyer interviews, they fed ChatGPT their Gong call recordings to see if it could extract the same insights. The approach broke down into three phases:

Phase 1: Can LLMs replicate the win interview using sales calls alone? Rally processed all won deals from a quarter through Gong's AI analysis with custom prompts.

Phase 2: Can LLMS identify trends across deals? Rally aggregated insights across all call transcripts using a custom GPT.

Phase 3: Can LLMs predict outcomes? Rally is testing whether the model can predict deal risk and suggest next steps by analyzing the first 2-3 calls once a deal reaches a certain stage.

What this means: win-loss analysis used to be quarterly because of time constraints. With AI, it can be continuous with quicker feedback cycles. You can use the learnings to inform sales collateral, targeting, and product marketing faster.

TOP READS FROM LAST WEEK YEAR

1. Growth series: How to launch your funding announcement [SaaS Weekly]

2. A Founder's Guide to Go-to-Market Strategy [Tomasz Tunguz]

3. The Rise of Free SaaS Tools as Lead Magnets (& How Marketers Can Take Advantage) [HubSpot]

4. What happened to the roundups? [SaaS Weekly]

5. They Wrote The Rules of Digital Marketing... A Century Ago…Before Computers Existed [Modern Marketing Digest]