Seema Sheth-Voss

GTM advisor for technical founders. I help sharpen the story, work your product launch, and find the GTM motion that fits.
Data, AI, Developer tools

[Ross Simmonds @TheCoolestCool]

Distribution is the only moat left in B2B SaaS.

If you're a B2B company founder, here's exactly what you should be focusing on👇

1. AI search (get recommended by ChatGPT before buyers Google you)

2. Multichannel (LinkedIn creates demand, Reddit builds trust, Google gets the credit)

3. Day one shortlists (get picked before buyers even talk to sales)

4. Reddit (earn trust in communities that hate being sold to)

5. Content distribution (make your best ideas impossible to miss)

6. Founder-led media (turn your expertise into an audience that buys)

7. AI citations (find out WHY ChatGPT cites your competitors)

8. Zero-click discovery (influence buyers who never visit your site)

9. AI content filtering (surface that single useful insight buried in 100 AI posts)

10. Dark social (capture buying signals hidden in Slack, DMs & private groups)

11. Niche creators (reach 5K relevant buyers instead of chasing 500K impressions)

12. Research syndication (turn one original report into months of exposure)

13. AI buying agents (get shortlisted when AI compares vendors for buyers)

14. Customer advocacy (turn happy customers into your strongest referrals)

15. Search-to-social (show up across Google, Reddit, YouTube & AI answers)

16. Category positioning (stand out when every SaaS competitor sounds identical)

17. Content repurposing (turn forgotten webinars & podcasts into fresh assets)

18. Outcome-based content (optimize for pipeline, demos & revenue, not post counts)

19. Distribution OS (connect research, creation, publishing & measurement in one system)

20. Branded demand (make buyers search for YOUR company, not just your category)

[J.B. @VibeMarketer_]

the "Super IC" is replacing the bloated 10-person marketing department

one operator owning positioning, shipping daily on X, writing the weekly newsletter, and turning customer calls into YouTube breakdowns that close inbound deals

zero handoffs or status meetings, 10x the output

this is the model every lean company is hiring for next

[Oliver @remoteoliver]

Even if AI developed taste,
most people using it don’t have it.

[Gokul Rajaram @gokulr]

I read this somewhere and agree with it: "Too many AI startups are selling picks and shovels. Not enough are actually mining the gold."

[Seema Sheth-Voss @SShethVoss]

Early-stage founders often say they need a marketer who can wear many hats.

Fair enough. But the key skill isn't switching between positioning, launches, content, or GTM engineering.

It's knowing which hat to wear first, and what can wait.

[Seema Sheth-Voss @SShethVoss]

Been studying founder-led content lately. Lots of attention goes into format: podcasts, video length, production, cadence. Less attention goes into what the founder actually has to say.

You can borrow someone's production format. But please, don't borrow a point of view.

[Corey Quinn @QuinnyPig]

...does nobody see a problem with giving this dude a database containing details on every kid in America?

[Sen. Bernie Sanders @SenSanders]

This is what billionaire ownership of the media means:

Mark Zuckerberg’s Meta shut down the No Kings account for hours during the height of their protests.

Meta suppressed accounts supporting Brazil President Lula da Silva in the recent election.

Larry Ellison’s TikTok blocked advertising for a documentary criticizing Elon Musk.

Fight back. Defeat oligarchy. Preserve democracy.

[Jason ✨👾SaaStr.Ai✨ Lemkin @jasonlk]

One thing that was “supposed” to happen was AI would turn AEs into Super Reps.

It should have happened:

- AI can do all the meeting prep for you
- AI can do the follow-up
- AI SDRs for outcome do work, trained right
- AI BDRs for inbound can qualified leads better than almost any human
- AI can extract all key meeting details, add to you CRM automatically, etc.
- AI training tools can help train you on how to sell and how your app really works
- AI SE and knowledge tools can help you with tougher technical and product questions

All that came to be

Yet, I have few-to-none AE Super Reps calls or demos.

Basically, humans have put about the same energy into sales as before. The best still crush it, and often crush it even more.

The main benefit seems to be somewhat smaller sales teams. But even there, the data is conflated by AI-native leaders with extremely strong market demand, running leaner teams. And the classic email based SDR may no longer be necessary.

But IMHO experience at least, AEs haven’t really gotten much more … Super.

[Aaron Levie @levie]

The biggest misread of anyone predicting the end of software engineering was imagining that the world only needed a certain amount of software, and bringing automation to the field would reduce the need for labor.

Exactly the opposite is happening where essentially most things are actually turning into software. Because agents can use tools, work with computers, write software on the fly, and process data like humans (but far faster), we can now throw “software” at problems that never could reasonably be digitized before.

What that leads to is the role of engineering taking on greater importance in the economy, because we can now engineer far more systems and processes than were possible before.

If you’re a bank trying to do new forms of trading, engineers are often your first bet. If you are in life sciences, you expect more and more research and lab work to be engineering centric. If you’re in manufacturing, you’re trying to automate more of the supply chain, all that is engineering. More and more work is shaped like software problems.

Yes we’re not going to be coding manually in the future. But almost everything will be code, which means those that understand it are in a fantastic position.

[Matt Turck @mattturck]

Special treat for the @andy_pavlo fans out there: an in-depth discussion about what happens to databases when billions of AI agents hit them, his move to build a research lab at @ClickHouseDB, and plently of hot takes on the database market (Postgres, vector, graph, GPU etc)

00:00 Cold open & Intro
01:21 From vector databases and RAG to the age of agents
04:09 Neon's stat: agents create 80% of databases? 07:25 Why agents keep deleting production databases 08:19 Guardrails: the toddler-and-stairs rule
10:51 The four eras of database volume: 10–100x more queries
15:10 Agent memory: files vs. databases ("everything is a database")
18:24 Which database do AI models recommend?
19:25 "It cites me back to myself"
23:14 MCP for databases, and text-to-SQL from 60% to 99.5%
27:09 Trust an agent the way you'd trust a junior developer
27:47 Can AI build an entire database? Opus 4 and the CMU projects
29:57 60% of open-source databases now have AI commits
30:43 Ten years of self-driving databases, Peloton to today
33:52 What LLMs changed: 85% of the tuning in 15 minutes
35:42 Should anyone still study databases?
38:27 Free CMU courses, the DJ, and the Wu-Tang final exam
42:09 Why start a research lab inside ClickHouse?
45:53 Why ClickHouse looked like vaporware in 2016
47:46 What makes ClickHouse fast: columns, vectors, Snowflake's lineage
52:22 Why Databricks, Snowflake and ClickHouse all added Postgres
58:15 Vector, graph and GPU databases: thumbs up or down?
1:08:34 Is the database market stagnant? "A cheetah on cocaine in a Ferrari"
1:10:14 The relational model is arithmetic; SQL as the new assembly
1:11:59 Larry Ellison, Linux, and why databases still matter

[Pierre-Eliott Lallemant @pierreeliottlal]

“I don’t know where to find customers.”

- People commenting on your competitors’ posts
- People asking for tool recommendations
- People complaining about their current solution
- People engaging with content about the problem you solve
- Companies hiring for roles your product supports
- Companies that just raised funding
- Decision-makers starting a new role
- Former customers moving to new companies
- Prospects attending niche webinars
- People asking questions in industry communities
- Past opportunities worth reopening
- Referrals from your best customers
- Partnerships with complementary products
- Founder-led LinkedIn content
- Personalized outreach based on buying signals
- Follow-ups that give people a reason to reply
- AI agents finding relevant prospects with @gojiberryai

I could go on and on. If you genuinely don’t know where to find customers, you might be cooked.

[Seema Sheth-Voss @SShethVoss]

I'm all for AI-assisted GTM automation. In fact, I'm enjoying working through the Clay wedge cohort and seeing how much research and prospecting work can be automated. But I'm increasingly convinced that the first few accounts deserve deep, manual research.

Not just firmographics, tech stacks and contact lists. You need to understand how the business actually operates. What legacy software is entrenched? What workflows have grown around it? Who owns the problem? What would have to change, organizationally and technically, for a new solution to take hold?

That research builds intuition. You start recognizing patterns, exceptions, and assumptions worth testing.

Only then do you know what to automate, which signals matter, and what questions to ask of the next 50 accounts.

Deep research builds the hypothesis. Automation tests whether it scales.

[Seema Sheth-Voss @SShethVoss]

The Semantic Web had the right vision and the wrong tools. Thirty years later, the tools have caught up. https://t.co/GCfjpy2tTp

[Seema Sheth-Voss @SShethVoss]

Building a manual list of 50 look-alike buyers today. Remembering DOE from grad school.

Define the test, the effort, and what failure looks like before getting enamored with tools and scale. If it fails, was it the channel, audience, or message? What did we learn?

[YC Insights. @YCInsight]

The "should I pivot or persist?" framework

Pivot when:
→ Core assumption is proven wrong by data
→ Users consistently use the product for something else
→ You've tried 5+ variations and none retain

Persist when:
→ Retention is improving even if slowly
→ A small segment loves it deeply
→ You haven't fully explored the core problem yet

- The mistake: pivoting from impatience.
- The mistake: persisting from ego.

Let Data decides & Not your feelings.

[YC Insights. @YCInsight]

What YC actually means by “product-market fit”

• People would be genuinely disappointed if your product disappeared
• They keep coming back because it solves a real problem for them
• They tell you what to build next without being asked

That’s PMF.

It’s not a feeling in the founding team.

It’s a behavior you can see in your users.

[Charles Burdett @chxrles]

please don't do this.

it's called "eyebrow" text and it's the latest vibecode-slop tell.

it serves no useful purpose. again, it's this "gesturing" LLMs do and you need to be aware of it. it puts this eyebrow text there because it's a landing page typography trope that bamboozles your brain into thinking "yes this looks like a good and proper landing page!"

and you think "the more detail = cleverer model!"

but it's dumb. all this design clutter has no actual thought - it's still stochastic parrot without a trained & experienced designer's guidance. the clever but hard thing is reduction. reducing and eliminating. good design is simple. great design is invisible.

this eyebrow test is just more unnecessary visual clutter the Opus 5.5 is obsessed with.

do not accept the default. take care with your work.

every element is unnecessary until proven essential.

[Dave Gerhardt @davegerhardt]

Weird growth tactic that is working well for us right now:

Focusing on quality.

We're not doing crazy things with AI. We don't have some secret growth hack or viral loop.

Our business is:

- A newsletter
- A podcast
- Webinars
- Events
- A private community

And I'd say the single biggest lever we've pulled in the last 12 months is a focus on quality.

This means:

- More prep calls with guests and speakers (yes, it works - putting in more effort up front)
- More editing cycles on newsletters and articles
- More prep work on webinar setup, run of show, event logistics
- Better research; deeper understanding of our audience and what people want

In a world where it's become incredibly easy to just blast out and AI generate email to 50,000 people on our list, we're going the opposite direction. Leaning in to everything before publishing. Better research. Better prep. Better guests. Pushing for less fluff and more specifics, real examples, metrics, stories from marketing professionals who actually did the work and had something on the line.

Don't let your lust for Claude get in the way of quality.

[Kyle Lacy @kyleplacy]

Nobody in revenue-generating orgs truly has the time to care about your brand manifesto if it doesn’t show up in their number.