Product Growth

Author of 1 newsletter deep dive and 2 podcast episodes a week on how to succeed as a PM, product leader, or aspiring PM.

Lead a team of folks.

I'm the product guy - making sure everything hits our ever increasing quality bar.

Here's a sample of recent content:

Course 1: Mastering the PM Career

Unit 1: The Key Outputs

Product requirements documents
Roadmaps: Advanced techniques
The feature results writeup
The product review bible

Unit 2: Hard Skills

Advanced discovery techniques
How to identify and validate product risk
Advanced customer interview techniques
How to ramp a product team without strategy

Unit 3: Soft Skills

How to say no
Handling conflict as a PM
How to bridge the divide between stakeholders and users

Unit 4: Career Skills

The PM Career Ladder: Your Unofficial Guide
How to get a raise in this market
Why politics isn’t a dirty word

Course 2: Mastering Growth

Unit 1: Lessons From The Greats

How Miro grows
How Canva grows
How Klarna grows
How did Intel do so Badly?
Everything about Midjourney
What you need to know about Temu

Unit 2: Systematizing Our Learnings

Build a detailed growth model
Gaming growth playbook
When to A/B test
Impact Sizing
Growth loops
How to build a flywheel
How to use Code Interpreter

Unit 3: Success in Specific Product Teams

Information Architecture
Product-led sales
Design Systems
Product Copy
Onboarding
Expansion
Activation

Course 3: Product Leadership

Unit 1: How to set up your product team

How to set up your product team
Do you really need a North Star Metric
Pitch a growth role to senior leadership
How founders think differently from PMs

Unit 2: How to scale your team

Billion-Dollar Execution
How to scale a product team from 0 to 15
Reference checks can supercharge your hiring

Unit 3: How to be a great leader

7 currencies of a product leader
The 7 failure patterns of managers
How to Make Your Ideas Stick
Product leadership in India

Newsletter Author, Podcast Host

Apollo.io

Product leader when the company became a unicorn ($750M->$2.5B valuation).

I led several product teams, including the growth team, pricing & packaging, activation, expansion, information architecture, acquisition, and engagement.

Raised at a unicorn valuation while I was there.

Ended up leaving to focus full-time on my newsletter.

VP of Product

Affirm

Member of the senior product leadership team. Led growth team when company revenue grew from $300M to $1.2B/year.

Head of Growth Product

Epic Games

Led data driven growth for Fortnite during some it's best performing years, including the Travis Scott Concert and launch of "Chapter 2."

Lead Product Manager

thredUP

Built and led the team's product growth team when it raised its series D.

We worked on the following key initiatives:

- Pricing optimization: we re-architected the pricing system to optimize for conversion, new customers, and stockthrough levels, driving industry-leading inventory turns
- Conversion rate optimization: we drove 2.5x in first visit to purchase conversion via tools like outfits, dynamic merchandising, ad campaign customization, and the aforementioned pricing system
- Referrals channel: we grew this channel back when it actually worked, with things like $10-$10 and also a famous $0.99 sales
- Paid advertising growth: we built an ad-technology system to handle budgeting and bids cross-channel across Facebook, Pinterest, and Yahoo ads

Overall, I helped the company eventually go public by setting the foundations for its growth, growth marketing, and growth product teams.

Director of Growth

Rap to Beats

Developed iOS app Rap to Beats & scaled to 300K monthly active users (MAU). Led team.

We shipped several game changing features:
- In-app purchases for premier DJ beats
- A leaderboard to showcase your raps by country
- Social sharing to Instagram and Facebook

With premier DJs regularly using the app to sell their work, we were able to scale one of the first consumer apps for rap on the iPhone.

Co-Founder/ Head of Product

Scoutforce Werving & Selectie

ScoutForce was a SaaS for College Athletic Recruiting Teams. 

I joined as one of the first employees, and helped create the initial product: in the cloud recruiting software to enable recruiting teams. I did in depth discovery, wireframing, and iteration on the early features. 

This built my initial skillset in product management.

Product Manager

The Product Growth Guy 🚀 | Helping you land your next job + succeed in your career | Newsletter Writer and Podcast Host

TL;DR: I write a newsletter for product managers, product leaders, and aspiring product managers. I focus on actionable and deep tips to improve your career.

The Longer Version:

After a long career in PM - rising to VP of Product at a Unicorn - I have made the shift to writing about it.

In particular, I write about product growth. That is, the art of growing via your product. 

This means I also talk a lot about product-led growth (PLG), the SaaS B2B motion.

My newsletter doesn't just focus on growth though. 

I also break down technology, product management, product leadership, and how to get a PM job. 

It's all the things that make for a great use of your learning & development budget. 

Or even personal spend. 

The math is pretty great. 
↳ $150 for an annual subscription
↳ $211K average PM salary in the US
↳ 0-10% average range of raises offered

If the newsletter helps you get a 0.07% better raise, it pays itself. 
That's 1/142th of your raise range.

It's a pretty smart investment for every level of PM.

http://www.news.aakashg.com

Aakash Gupta

This is what a Senior AI PM at Google's Github looks like.

It's wild:

Take a look:  https://lnkd.in/e7Jb8kRe

He's made 484 contributions in the last year (>1/day). 

New-age AI PMs don't just write PRDs. They ship:

1. Prototypes
2. Frameworks
3. Perspectives on AI
4. Open source contributions

The bar for technical fluency for AI PMs continues to rise as PMs like Shubham Saboo raise the standard. 

AI PMs don't just have a theoretical understanding of concepts, but a practical knowledge of AI engineering.

Take a look at Shubham's top repos, for inspiration:

1. Collection of LLM Apps: https://lnkd.in/e-Zesb7u
2. MCP Playbook for AI agents: https://lnkd.in/eR5KHvww

These are practical resources that he's collected himself. They aren't necessarily at-scale production apps. They're PM friendly projects. 

He's also built a more-technical open source Python toolkit for building AI agents: https://lnkd.in/eCsAzPtR

When you put these types of work products together, jobs come to you. This is how you break into AI PM. 

If you look at his LinkedIn, most of his career was in dev rel. But a Github speaks louder than titles.

This is what all aspiring AI PMs should do:

1. Start with Prototyping: https://lnkd.in/eJujDhBV
2. Then pick up Cursor: https://lnkd.in/d2pcXD7R
3. Build with Claude Code: https://lnkd.in/eUyPEAma
4. Ship to Github: https://lnkd.in/eVFVT_Yc
5. Create a Portfolio: https://lnkd.in/eT3RvQiM
6. Build a Job Search System: https://lnkd.in/efbppEDj

Then you too can become an AI PM at Google like Shubham. He's created the roadmap. Now follow.

📌 Want my complete guide to the AI PM job search? Comment 'AI job search' + DM Me. Must do both.

➕ Follow Aakash Gupta for daily AI PM tips. 

Proof of work > titles

The AI PM Guy 🚀 | Helping you land your next job + succeed in your career

Linear hit a $1.25B valuation with just 2 PMs.

The way they build product is from the future:

They're contrarian.

They do almost no "growth product" work:

• No A/B tests
• No work on activation
• No growth dashboards
• Zero focus on retention tactics

They also don't have a design system. 

Instead, at the core of the way Linear builds product is "The Linear Method." Many founders I talk to have been religiously copying it. There's three keys, as I see it:

1. Directness
They don't do user stories, they speak clearly

2. No Busy Work
They don't do sprint planning, or OKRs for ICs

3. Momentum
They don't have cycles, scope aggressively, ship fast

Their founders, and CEO Karri Saarinen have operated this way from day 1. They set a very intentional culture. 

Beneath the method, there's tons more texture... 

One of the most unique processes they have is the roast. Before every launch, the entire team spends 30 mins breaking every feature with a massive doc. 

Another one is their zero bugs policy. I heard from multiple people of bugs being solved in hours, not days.

They also don't have a PM on every project. With 2 PMs, small little teams of designers and engineers really think like product people in the work that they do. 

The results speak for themselves.

$1.25B valuation with just 100 employees.
145% net revenue retention. 
No retention team needed.

They've put on a masterclass in building a unique product culture, with lots to learn for others out there.

Want to go deeper?

• My Deep Dive on Linear: https://lnkd.in/e_R9ruDG
• Head of Product Nan Yu: https://lnkd.in/e3WDcETz
• AI Agents Demo: https://lnkd.in/etXnfc2C

📌 Want my breakdown of their PLG iceberg? Comment 'Linear Iceberg' + DM Me and I'll reply to your DM.

I can't help but think most teams will move this direction.

AI + Product Management 🚀 | Helping you land your next job + succeed in your career

Want to pass PM Interviews at OpenAI, xAI, Meta, Google, Airbnb, Dropbox, Palantir?👇 

1. Understand these important concepts:

- Business: P&L, metrics trees
- Technical: APIs, scalability, performance
- Roadmapping: now/next/later, alignment
- AI/ML: prompt eng, RAG, fine-tuning, evals
- Experimentation: when to A/B test, p-values
- Prioritization: RICE, value vs effort, opp cost
- Discovery: customer interviews, user research
- Metrics: north star, guardrail, leading v lagging
- Product strategy: future trends, big tech choices

2. Practice these case interview questions:

- Product sense: https://lnkd.in/eTqEFtPM
- AI product sense: https://lnkd.in/ekWty_bw
- System design: https://lnkd.in/etTmzNzz
- Product design: https://lnkd.in/encHB-k7
- Product execution: https://lnkd.in/e8t8zS9q
- Product estimation: https://lnkd.in/eZvjP4RR
- Product strategy: https://lnkd.in/eqzpahmW
- Success metrics: https://lnkd.in/efdj_QYT
- Vibe coding: https://lnkd.in/e66DrW-h

3. Practice these behavioral questions:

- Tell me about yourself: https://lnkd.in/eYmRYF2Y
- Why want to work here: https://lnkd.in/eeuRsR9M
- What's your current pay: https://lnkd.in/gS-CKH9P
- Improve app speed: https://lnkd.in/dG9EFhiR
- Greatest weakness: https://lnkd.in/ePQ7dBtr
- Were you laid off: https://lnkd.in/e2iTP3xs
- Leadership style: https://lnkd.in/eCPdQVp2
- What level targeting: https://lnkd.in/e2YQ8tK2

4. Study these company specific processes:

- Microsoft:  https://lnkd.in/eXWWYefH
- Amazon: https://lnkd.in/eA_WxXHQ
- Apple: https://lnkd.in/eXWWYefH
- Google: https://lnkd.in/ezAHYXhV
- OpenAI: https://lnkd.in/ebR_Auss
- Meta: https://lnkd.in/evHQ_w-E

Image: Yangshun Tay

———

♻ Repost to help others in your network
📕 Save the post so you can find it in future
📌 Comment 'Interview guide' + DM me for all the links
💡 Follow me Aakash Gupta and my company account Product Growth for 2x daily AI PM insights

🛠️🤖 How to build AI agents from scratch
(Even if you've never done it before.)
𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝟴 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝘁𝗮𝗸𝗲, 𝗳𝗿𝗼𝗺 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 𝘁𝗼 𝗨𝗜.

𝙁𝙞𝙧𝙨𝙩, 𝙬𝙝𝙖𝙩 𝙞𝙨 𝙖𝙣 𝘼𝙄 𝙖𝙜𝙚𝙣𝙩?

An AI Agent is an LLM that:

• Reasons on its own
• Act (via calling tools)
• Observes & improve itself

This is the core "ReAct" framework Google released all the way back in 2022. It's now a reality. 

---

𝙃𝙚𝙧𝙚'𝙨 𝙝𝙤𝙬 𝙩𝙝𝙞𝙨 𝙩𝙧𝙖𝙣𝙨𝙡𝙖𝙩𝙚𝙨 𝙞𝙣𝙩𝙤 𝟴 𝙠𝙚𝙮 𝙨𝙩𝙚𝙥𝙨:

1. Define purpose & scope

Over-scoping is the killer of agents. You want a single clear use case with success criteria, and constraints. 

2. Build system prompt

This is often the most important step. You want a really good prompt, or the whole thing falls over. 

3. Choose LLM

Don't always go for the best. Balance speed, multi-modality, and what each LLM is best for. 

4. Integrate tools

This is what makes your agent an agent, MCP, APIs, other agents as tools. Create your architecture.

5. Add memory

Don't add memory unless needed. But for support agents that need conversation history, or research agents that build on past findings? Game-changer.

6. Orchestrate things

You want a control flow over routes, workflows, message queues and your agent-to-agent (A2A) coordination.

7. Build a user interface

This is the flashy part we want to jump to. But only it do it now. Figure out how users will interact. 

8. Add testing & evals 

This is the most important step to create a continuous improvement loop. Systematically analyze errors.

Here's guides to help with each step:

1. Purpose ➝‬ https://lnkd.in/eekC6bwT
2. System prompt ➝‬ https://lnkd.in/d_qYCBT7
3. Choose LLM ➝‬ https://lnkd.in/eqRgsMZx
4. Integrate tools ➝‬ https://lnkd.in/ezNsTfbD
5. Add memory ➝‬ https://lnkd.in/eeey5Cxr
6. Orchestrate things ➝‬ https://lnkd.in/eKhmKYNp
7. Build a UI ➝‬ https://lnkd.in/epRC64ku
8. Evals ➝‬ https://lnkd.in/eGbzWMxf

---

𝙏𝙚𝙘𝙝 𝙨𝙩𝙖𝙘𝙠:

Now, what tools should you use? 

For general assistants: ChatGPT, Claude, Perplexity 
For coding projects: Cursor, Windsurf, Claude Code 
For business automation: Lindy, Relay app, n8n 
For complex workflows: LangGraph, CrewAI, LlamaIndex

The best stack is the one that gets you shipping fast.

---

Agents are one of the most powerful skills for PMs and engineers to learn right now. 

I have been studying them deeply.
 These are my core learnings.

📌 Want my 8-layer agent architecture? Comment 'agent architecture' + DM me.

What agents have you been building?

This $4B CPO runs her entire day out of Claude Code + Cursor. And she shared exactly how she does it:

It was a delight to sit down with Rachel Wolan.

She is one of the most AI-forward CPOs I have met, and she dropped all the sauce on her AI workflows.

🎬 Watch Now: https://lnkd.in/es6aQigP

Available Everywhere
Spotify: https://lnkd.in/eyt7agKj
Apple: https://lnkd.in/eAEVwr3u

🏆 Thanks to our sponsor:

Linear: Plan and build product like the best

Use my link! https://lnkd.in/e8rtjVRZ 

✍️ Here were my favorite takeaways:

1. There are two sides to AI product leadership: being a productive AI product leader yourself, and shipping AI native features for your customers. Rachel showed us both in this masterclass.

2. Webflow went from 0% of designers using Cursor to 30% using it weekly after one builder day. That's the power of setting up your org correctly. Champions teach, incentives align, adoption follows.

3. Rachel built an "agentic chief of staff" that analyzes her calendar for delegation opportunities, triages her entire inbox, and queries Snowflake in natural language. She gets answers in seconds without waiting for data scientists.

4. Her calendar agent spots double-bookings, context switching issues, and meetings she should delegate. It recommends what to cut next week. She runs it weekly & shares outputs with her EA.

5. Her email agent removes junk automatically, pins what matters, and drafts replies based on her inbox behavior patterns. It learns what she cares about by watching how she responds.

6. She queries their data warehouse in plain English through Claude Code. "How many sites does this workspace have?" answered instantly. Set up MCP servers for Snowflake & Tableau to enable this.

7. Building custom agents takes minutes. Create a markdown file in your /agents folder, describe what you want, Claude generates it. She built a LinkedIn post generator live on the pod.

8. To enable your team: give them tool access (Cursor, Claude Code), run builder days where everyone demos something new, update career ladders to expect AI usage, and let champions on your team teach others.

9. For shipping AI features, invest heavily in evals. Webflow's code generator broke when they switched models because eval coverage was insufficient. 

10. Choose AI features that leverage your core strengths. Webflow built a code generator using their CMS, design system, and production hosting. That's their differentiation versus every other coding agent.

11. Think distribution first, not last. Answer engine optimization is the new SEO. Feed FAQs to your website, build ChatGPT apps, and make sure product changes get recognized in answer engines from day one.

What's even cooler is she is a paid subscriber to the newsletter! 

Join her: https://lnkd.in/gp2cCv8K

And don't miss the ep for her live demos!

Tag who you think I should have on next 👇

What does a PM building AI features do?

I've been interviewing 2+ weekly for 14 months:

Here's the latest tools, skills, responsibilities, knowledge

𝟭. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀

1a. Discovery + Research
Finding where AI actually adds value takes research

1b. Execution Delivery
Getting AI into production is harder than it looks

1c. Strategy Planning
You're planning the future of AI at your company

1d. Leadership Impact
The best AI PMs shape company strategy

𝟮. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲

2a. AI Fundamentals
Understanding what AI can and can't do is critical

2b. Product Patterns
Designing for AI requires different UX patterns

2c. Market GTM
Positioning AI products requires market knowledge

2d. Testing Taxonomy
Testing AI is completely different from testing software

𝟯. 𝗦𝗸𝗶𝗹𝗹𝘀

3a. Context Engineering
Getting AI to understand what you need is half the job

3b. Evaluation Testing
You can't ship without knowing if it actually works

3c. AI Architecture
Choosing the right model & system design matters

3d. Cross-Team Communication
You're the bridge between tech teams & business

𝟰. 𝗧𝗼𝗼𝗹𝘀

4a. Core AI Platforms
Claude, GPT-5, Gemini & NotebookLM are the top tools of the trade

4b. No-Code AI Builders
Zapier, n8n, Lindy, Linear & Replit are the most used

4c. Prototyping/Vibe Coding
Bolt, Cursor, Claude Code & v0 ship prototypes fast

4d. Analytics Monitoring
DataDog, Amplitude & Arize track AI performance

With so many things to do and learn...

PM is really a "do everything" role. It's hard to have work-life balance. Here's resources to stay sane:

𝟭. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀

1a. Discovery → https://lnkd.in/e9QrMEDw
1b. Execution → https://lnkd.in/eajEfG5R
1c. Strategy → https://lnkd.in/egemMhMF
1d. Leadership Impact → https://lnkd.in/eymXeBmE

𝟮. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲

2a. Fundamentals → https://lnkd.in/e6zyYugs
2b. Product Patterns → https://lnkd.in/eq8xvCAK
2c. Market GTM → https://lnkd.in/eJj4bx_i
2d. Testing Taxonomy → https://lnkd.in/eKhmKYNp

𝟯. 𝗦𝗸𝗶𝗹𝗹𝘀

3a. Context eng → https://lnkd.in/ebfnDUmi
3b. Prompt eng → https://lnkd.in/d_qYCBT7
3c. Architecture → https://lnkd.in/eeey5Cxr
3d. Communication → https://lnkd.in/eg8anRXa

𝟰. 𝗧𝗼𝗼𝗹𝘀

4a. Claude → https://lnkd.in/eZ6UBbAQ
4b. Prototyping → https://lnkd.in/eJujDhBV
4c. Experimentation → https://lnkd.in/e86mpjGR
4d. No-Code Builders → https://lnkd.in/e_fjvHHN

📌 Want my free 5 day course? Comment EEC + DM me.

Follow Aakash Gupta for daily AI PM tips.

Repost to help others ♻️

Google JUST dropped 70 pgs on context eng and it's 🔥

I summarized it for you:

Stateful, personal AI starts with Context Engineering: assembling the right information at the right time.

There's 7 keys:

—

𝗢𝗡𝗘 - 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 = 𝗦𝗺𝗮𝗿𝘁 𝗔𝘀𝘀𝗲𝗺𝗯𝗹𝘆

Your LLM context window is prime real estate. Context Engineering optimizes what goes in:

→ User intent + conversation history
→ Retrieved facts (RAG for general knowledge)
→ Long-term memory (user preferences, expertise)
→ Tool outputs and grounding data

The right information, assembled at the right moment.

—

𝗧𝗪𝗢 - 𝗦𝗲𝘀𝘀𝗶𝗼𝗻𝘀 = 𝗬𝗼𝘂𝗿 𝗪𝗼𝗿𝗸𝗯𝗲𝗻𝗰𝗵

A session is a single conversation with:

1. Events (user messages, tool calls, observations)
2. State (conversation history, accumulated context)
3. Lifecycle (create, interact, close)

Think: one task, one session. When you close it, the session ends but memories can persist.

—

𝗧𝗛𝗥𝗘𝗘 - 𝗠𝗲𝗺𝗼𝗿𝘆 = 𝗬𝗼𝘂𝗿 𝗙𝗶𝗹𝗶𝗻𝗴 𝗖𝗮𝗯𝗶𝗻𝗲𝘁

Memory persists across sessions for true continuity:

- Declarative (facts, preferences: "I'm vegan")
- Procedural (how you work: "I debug like Sarah does")

RAG retrieves general facts from the internet. Memory captures YOUR specific expertise and preferences.

Big difference.

—

𝗙𝗢𝗨𝗥 - 𝗠𝗲𝗺𝗼𝗿𝘆 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 = 𝗘𝗧𝗟 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲

LLMs themselves drive memory creation:

1. Extract relevant info from session conversations
2. Consolidate with existing memories (dedupe, merge)
3. Load into vector stores for retrieval

An LLM-powered ETL pipeline. Automated intelligence extraction from your interactions.

—

𝗙𝗜𝗩𝗘 - 𝗣𝗿𝗼𝘃𝗲𝗻𝗮𝗻𝗰𝗲 = 𝗧𝗿𝘂𝘀𝘁

Every memory needs metadata for production use:

→ Source (which session created this?)
→ Timestamp (how fresh is this?)
→ Confidence (how certain are we?)

Provenance is critical for debugging, trust, and reliability in production systems.

—

𝗦𝗜𝗫 - 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀

Two approaches to memory retrieval:

Proactive: Always include in context (user name, critical preferences)
Reactive: Retrieve on-demand based on query (semantic search, vector similarity)

Smart agents know when to pull vs push. Not everything belongs in every context.

—

𝗦𝗘𝗩𝗘𝗡 - 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗹𝗶𝘁𝘆

Building this at scale requires:

• Privacy: User data isolation, encryption, compliance
• Performance: Caching strategies, batch operations
• Scale: Vector stores for semantic search, not SQL

Google runs this across millions of users. The infrastructure matters.

—

This took me 2 hours.

To go further, you need to learn:

1. RAG vs Finetuning: https://lnkd.in/ebfnDUmi
2. Prompt engineering: https://lnkd.in/d_qYCBT7
3. AI agents: https://lnkd.in/eeey5Cxr

📌 Want me to send you the whole PDF? Comment 'context PDF' + DM me. Repost to cut the line.

Follow Aakash Gupta for daily AI tips.

Context Engineering Masterclass by Google

I literally mapped out the whole agents market. 

These are the top players:

SECTION 1: AI AGENT INFRASTRUCTURE

Development Platforms & Frameworks
• Anthropic, OpenAI, cohere, Stack, Beam, ADEPT, BREV/AN, DeepOpinion, Distyl, E2B, KESTREL AI, Moveworks, Sema4, greptile, lyzr, /dev/agents, itwin, wordware
  
Workflow Automation & Orchestration
• Enterprise automation: Zapier, Airtable, n8n, Relay, Lindy
• Multi-agent systems: fetch, crewAI, Martian, aaru, emergence
  
Agent Infrastructure & Tools
• Web & tool use: composio, Browserbase, exa, tavily
• Data & knowledge: LlamaIndex, vectara, Letta, Across AI, zep
• Authentication: ANON, Keet
• Payments: Skyfire, stripe, paymon, nevermined
  
Evaluation, Observability & Voice
• Eval & monitoring: arize, Langfuse, Patronus AI, COVAL, Agency, Haize Labs, vijil, Vocera, Weights & Biases
• Voice AI: ElevenLabs, Cartesia, Deepgram, Retell AI, VAPI, Bland, Synthflow, Hamming, FIXIE, sarvam, WaveForms

SECTION 2: CORE ENTERPRISE USE CASES

AI Assistants
• OpenAI, Anthropic, Glean, Lindy, HyperWrite, Convergence, DeepSky (Airtable), perplexity, Personal AI, Please
  
Software Development
• Cursor, Cognition, replit, Autify, Magic, lovable, FACTORY, imbue, poolside, Tusk, qodo, Magic Patterns, All Hands, Resolve
  
Customer Service
• Decagon, SIERRA, Voiceflow, COGNIGY, Maven AGI, crescendo, DevRev, parloa, Gradient Labs, yampa
  
Sales & Rev Ops
• 11x, clay, AISDR, ARTISAN, bounti, Agent, DocketAI, flowirks, QUALIFIED, mindy, IOX, tektonic ai
  
Data Analysis
• Talonic AI, Embry, ConnectlyAI, Paradigm
  
People Ops
• Mercor, MERCOR, apriora, Borderless AI, otto, moonhub, Tezi, CONVIN, ToothFairyAI
  
Accounting
• Basis, Hypatos
  
Cybersecurity
• AIRMDR, Bricklayer, Dropzone AI, nullify, prophet, RadiantSecurity, Simbian, torq
  
Marketing 
• ability, firsthand
  
Web Research
• Lutra, Reworkd

SECTION 3: INDUSTRY-SPECIFIC AGENTS

Financial Services
• Concourse, FINNY, Boosted, Brightwave, Greenlite, altiius, Hebbla, Indemn, Powder
  
Legal
• Harvey, &AI, eve, NormAI, Parcha, Spellbook
  
Healthcare
• EliseAI, Anterior, Hippocratic AI, Assort Health, health, Synthpop
  
Supply Chain & Logistics
• DIDERO, FleetWorks, HappyRobot, Naratix, Pixel Robotics
  
E-commerce 
• Naratix, CommerceOS
  
Gaming 
• ALTERA, innovacer, thoughtful
  
Industrials & Infrastructure
• Composable, juno, phaidra, Pixel Robotics

Shoutout to Crunchbase. Used it heavily. 

Want to go further on agents?

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5. Become an AI Agents PM: https://lnkd.in/eekC6bwT
6. Relay app CEO: https://lnkd.in/ed_ji7-W
7. Lindy CEO: https://lnkd.in/eqRgsMZx

Follow Aakash Gupta for more AI

If you want traffic your site in 2025.

Don't use Old SEO.

Millions of queries daily are going to LLMs. 

Weekly users for ChatGPT alone hit 800M+. 

You need to:

1. Get cited by ChatGPT/Claude → GEO
2. Rank in Google AI overviews → AEO
3. Be used by AI agent tools → AIO
4. Convert users upon land → SXO

It's time for companies to embrace the AI search wave.

I'm not saying SEO is dead. In fact, being good at SEO makes you good at all of these things. 

But you need a specific playbook. 

Here's your starting point 👇

🔗 OLD SEO 
↳ We were obsessed with ranking number 1.

S - Search
E - Engine
O - Optimization

The bread and butter here was keywords, programmatic, backlinks, and technical optimization.

The core question behind all work was: "Can we rank higher than competitors?"

It was great for driving high-intent traffic. 

The problems were:

• more traffic without intent
• informational clicks
• algorithm updates

As a result, the funnel looked like:

100 visitors
4% convert
4 signups

💬 NEW SEO
↳ You have to get cited and used by AI platforms. 

You build 4 things:

1. Generative Engine Optimization (GEO)
2. Answer Engine Optimization (AEO)
3. AI Integration Optimization (AIO)
4. Search Experience Optim (SXO)

It's great for durable authority that compounds.

And it can drive:

• Built in trust
• Pre-qualified intent
• Visibility where decisions happen

As a result, the funnel looks like:

50 visitors
24% convert
12 signups

📌 WANT TO MASTER NEW SEO?
↳ I've written a full guide with Josh Grant.

His team at Webflow drive >10% of their signups...
Which convert at 6x the rate via AEO + GEO

Here's the guide: https://lnkd.in/e8X-Qt_C

We cover:

• How to build your team
• How to measure these efforts
• And 3 s-tier tactics you can steal from Webflow

👉 Tag your favorite growth person or SEO agency so they get the goods.

Brendan Short

The Signal (.club)

• Separating the signal from the noise — a weekly newsletter on the future of GTM/tech (including AI, obvi)
• Subscribe now (it's free!): www.thesignal.club

Founder

• I was booking ~1,800 meetings/quarter, fully automated (Apollo didn’t have SDRs)
• Read more here: https://www.apollo.io/magazine/how-apollo-tripled-meetings-booked-using-its-own-platform

Product-led sales ("AutoSDR")

Groundswell

• Here is what we learned building Groundswell for 2.5 years: https://www.thesignal.club/p/what-we-learned-building-groundswell
• Started off building in "product-led sales" and then expanded to, what I call, "signal-based selling" (using gen ai to find signals and generate next-best actions)
• Raised $1.6M from Village Global, GTMFund, Henry Schuck, Lars Nilsson, Shaan Puri, and other founders and executives from the world's leading PLG companies including Slack, DocuSign, Rippling, Snowflake, TripActions, Workato, and others
• Had the best co-founders I could ask for, Hari and Nick

Co-founder & CEO

GTMfund

• Limited Partner alongside great GTM leaders in the GTMFund

Limited Partner (LP)

Zoom Video Communications

• Consulted for Zoom starting in Dec 2019
• Joined full-time in 2020 as things got busy due to the pandemic (it was a wild time to be there)
• Led Operations and Enablement for the Global SDR Org (150-person team)

Operations & Enablement (Global SDR Team)

The Revenue Engine (Consultancy)

• While figuring out my next move, I took on some consulting and advising gigs to help B2B SaaS companies build scalable, predictable revenue engines
• I've been lucky to work with some incredible companies and people, including: TripActions, Zoom, Snapwire, WellHealth, LoanBase, Edvo, among others

Owner

OutboundWorks - ACQUIRED

• I joined OutboundWorks as VP Sales as part of their acquisition of my company, Hexa.ai
• We were building “Automated Sales Development”
• I led Sales Development, Sales, Marketing & Customer Success 
• The company was acquired in October 2019

VP Sales

Hexa (acquired by OutboundWorks)

• SaaS Product to help automate XDR work ("AI to replace SDRs" was a horrible tagline... but man, wish LLMs existed back in 2016 when we were working on this problem...)
• Acquired the technology of Brisk.io and merged it with Proleads.io
• Class 17 of The Alchemist Accelerator (6 month program; #1 Enterprise B2B Accelerator in the World)
• Raised $800k in funding
• Acquired for 7-figures in Feb 2018, by OutboundWorks (backed by True Ventures)

Wiser Solutions, Inc.

• 1st full-time employee (started as SDR then AE then Manager)
• Was there from ~$15k MRR to $10M ARR 
• Personally closed $1M+ in first full year selling - online retailers, brands & manufacturers (mid-market and enterprise)
• Built and managed the Sales Team (SDRs, AEs, Sales Engineering, and Sales Ops)
• Wiser was acquired for 8-figures in September 2016

Head Of Sales

Avista Solutions, Inc. - now part of D+H

• Cold-calling from a cubicle from 9-5
• Avista Solutions was acquired by D+H, for $40M (45 employees, in business for 4.5 years)
• I left after Avista was acquired by D+H (cubicle life wasn’t for me)

SDR (before the title existed)

Playing long-term games w/ long-term people ✨ Building Groundswell

Currently writing about the future of gtm/tech, at The Signal. Subscribe now (it’s free): www.TheSignal.club 

I’ve worked at a couple of startups ($0-$10M), a couple of big companies (Zoom Video, Apollo.io), and I’ve started a couple of venture-backed SaaS companies (Hexa.ai, Groundswell).

I’ve been an SDR, AE, Head of Sales, SDR/Ops Leader, and CEO. Jack of all trades, master of none, perhaps. 

I like learning and building. 🤘

Writer

• Separating the signal from the noise—a weekly newsletter on all things GTM tech

Brendan Short 🌊

Sumble is the most interesting GTM technology I’ve come across this year (no joke). Their bread and butter is providing the best tech stack data in the world (via job descriptions, which is clever). But they also can help GTM teams find “rich data” and signals that help present a *unique insight* which is how the best teams/reps in the world are breaking through the noise right now. Here’s what I find incredibly compelling about Sumble:

1/ Tech stack data

This is their sweet spot. If you’re looking to see what companies use Snowflake,  Rippling, whatever - they have it. They pull it from job descriptions. You can do creative things once you have this information (reliably). You can click in and see exactly where Sumble found {technology you care about}, and it’ll show you the actual job post/source.

2/ Identifying internal projects

They’re applying ML to their dataset to generate something they call “Projects.” Things like: Cloud Cost Optimization, Cloud/Data Infrastructure Migration, Design System Migration, Frontend Migration, GenAI, Microservices Migration, Security Infrastructure Modernization.

If you want to know which companies are currently working on one of these projects, it's a no-brainer.

3/ You’ll see the value in under 3 minutes

I’ve shown Sumble to dozens of people this year, and there’s always an aha moment very quickly (even non-technical folks get it immediately). It’s magical to see a product that is so powerful and simple.

It provides immediate context to the reps on who to contact and why.

4/ People data

Once you find a company that’s a fit (based off tech stack, project, or other firmographic data), you can drill into teams and people. Again, they’re a legit ML team (the founders previously sold their company to Google), so they’re doing a lot of technical work to pull this off.

5/ Signals

I’m a fan of signals. :) And they have signals that can notify reps (via CRM or Slack) based on their dataset. Things like: new tech mentioned in JDs, job changers, etc.

6/ Keyword search on JDs

You can search for keywords within job descriptions. So if you’re looking for “Sales Engagement Platform” or whatever, you can find it. The bottleneck is creativity, not technical.

7/ Examples of how I’ve used Sumble this year:

- Companies using Pytorch/Tensorflow w/ 1+ ML/AI Eng
- 5-25 data engineers actively hiring for data engineers + using Snowflake/Databricks/Redshift/BigQuery
- Companies actively hiring for specific titles based on *keyword* (not captured in other tools)
- B2C companies w/ 1+ {specific title} in the US hiring in the last 6 months
- People search for certain teams using SalesLoft/Gong/Outreach/Clay
+ I'll put a few more in the comments

PS: they just added an API!

I convinced Anthony and Abhishek to partner with me/The Signal, because I like the product so much. Thanks for trusting me to help spread the word!

Go try them for free and see the value for yourself → https://lnkd.in/gZBzxdu5

brendan short

Exploring the future GTM playbook → Writing The Signal | Playing long-term games with long-term people 🫡

Who are the best GTM x AI practitioners (with a focus on Pipe Gen)? 

No vendors please :) 

I want to talk with people who have deployed AI into production. Real use cases. Can be building their own tooling, or using third-party tools. 

Tag someone who has shipped something dope this year.

Doing a bit of research for a little event for The Signal... more on that soon (subscribe to get updated first: www.TheSignal.club)

Generic "signals" will become commoditized. "Niche/specific" signals will become the alpha and have staying power. These signals should indicate the right *timing* to reach out to a prospect. Here are 20 examples:

1. Warehouse robotics / 3PL automation
Signal: New fulfillment center announced (Workflow design phase → ideal timing for automation decisions)
Source: Press releases, careers page, job descriptions

2. API security
Signal: Public API docs show a major version bump (New surface area → expanding security debt immediately)
Source: API docs, GitHub commits

3. Sales compensation & planning
Signal: Job posts mention “new FY comp/territories” (Comp redesign underway → high organizational pain)
Source: Job descriptions

4. AI QA for contact centers
Signal: Support page adds phone or chat channel (Volume spike + multi-channel complexity → QA automation need)
Source: Support page crawl

5. Accessibility compliance
Signal: New accessibility footer appears, boilerplate content (Legal pressure surfaced → remediation required)
Source: Website footer change detection

6. Usage-based billing
Signal: Pricing page adds “usage-based pricing (beta)” or similar (Metering/billing complexity rising → need for usage infrastructure)
Source: Pricing page diff

7. Privacy / DSAR automation
Signal: Privacy policy page’s last updated date is within 30 days (New privacy workflow or regulatory pressure emerging)
Source: Website change detection

8. ESG / sustainability reporting
Signal: ESG/CSR report updated recently (Entering ongoing reporting cadence → tools needed to scale)
Source: Investor relations page

9. Expense management
Signal: Employee handbook updated with new stipends/budget sections (New spend categories → need for controls/visibility)
Source: Public handbook PDFs

10. Consent & preference centers
Signal: Email footer switches to “Manage Preferences” (Growing privacy + marketing pressure → need for consent management)
Source: Marketing email footers

11. CDP / data routing
Signal: Multiple new martech tags appear recently (Stack expanding → rising data identity and plumbing issues)
Source: BuiltWith, tag managers

12. AI enablement / sales enablement
Signal: Product changelog shows new AI Assistant/Copilot (Sales team needs updated messaging + enablement immediately)
Source: Product changelogs

13. FinOps / cloud cost optimization
Signal: Company adds 2nd region or cloud provider (Cost/governance complexity spikes → FinOps needed now)
Source: Engineering blog, technologies detected in job descriptions

14. Travel Company
Signal: HQ move or office relocation announced (Operational changes → need for new systems and workflows)
Source: LinkedIn posts, press releases

15. Incident management
Signal: Status page shows spike in incidents (Reliability regression → push for incident tooling)
Source: Status page logs

...I ran out of room. The last 5 are in the comments. 

PS: Much more on this topic coming to The Signal readers' inboxes soon 👀

Generic Signals are getting commoditized. Niche Signals are the new alpha. Here's what it means if you're building an AI-native GTM org today:

First, signal-based selling remains the most effective approach for GTM teams today. Much better than cold outbound (which doesn't work like it used to). 

Signal-based selling helps teams/reps find the prospects who are MOST LIKELY to take a sales meeting if you reach out this week.

Generic signals—like funding raised, hiring roles, etc.—are quickly becoming commoditized because everyone has access to the same data from the same providers. And prospects can smell the automation from a mile away.

(Operationalizing these signals is still hard to do, btw. So, I think there's at least another year for companies to use generic signals to get ahead of their competitors.)

But, just like back in 2016, it was novel to have Outreach or SalesLoft use “personalization tokens” in an email to inject {first_name}, {title}, {company}, or {industry}. Today, it’s obvious to a reader that a machine, not a human, wrote those.

The same thing will happen with generic signals. In 2026, I suspect that when a prospect sees a cold email that is based on {their new job}, {a website visit}, or {some other generic signal}, they’ll know that a machine (AI), not a human, found this signal and wrote this message.

But, not all signals are created equal.

Enter: "The great signal divide."

The next wave of alpha will be niche, specific signals that indicate the right timing to reach out have real staying power. And that’s exactly where the best tools are differentiating themselves (Sumble is one example).

→ Alpha
 
The alpha is in the creative, thoughtful experiments that only you can set up. And in choosing vendors who can surface signals that aren’t available everywhere else.

→ The more niche/specific, the better

I’ve noticed that the absolute best signals are ones so good that AI—or myself, as an external advisor/consultant—cannot come up with them. They have to come from within the company (almost always from either the founder directly or the top sales rep).

→ 1st party signals

It’s also worth noting that some of these signals will be 1st party data. These signals, by definition, cannot become commoditized because they are your own company’s IP.

→ AI / agentic workflows

Yes, many of these niche/specific signals require AI workflows or “agent runs” to identify at scale, autonomously. You should be asking what works manually, then scaling it with an automated or agentic workflow (or combination).

Conclusion

The signal-based selling market is splitting into two tiers: commodity signals that everyone has access to, and niche, timing-focused signals that create real competitive advantage.

Thoughtful, creative, taste-driven go-to-market is the future. Teams that proactively deliver real value to their prospects.

Read more in my latest blog post on The Signal:

Open article: Generic Signals Are Getting Commoditized. Niche Signals Are the New Alpha. by thesignal.club

Generic Signals Are Getting Commoditized. Niche Signals Are the New Alpha.

It's unrealistic to "give an old school team new school systems." These tech stack infographics are misleading. 

This tech stack is for high-volume, fully automated outbound. This is the stack a centralized team/member (eg: RevOps, Growth, or a GTM Engineer) would run *on behalf of* reps. 

Enterprise companies are not downgrading from Salesforce to HubSpot (or Attio, etc.). They're not swapping Outreach/SalesLoft for Instantly/Smartlead (there's literally not a UI/way to multi-thread in them). They're not plugging in HeyReach and automating a ton of actions (at the risk of getting their LinkedIn accounts shut down). Replacing ZoomInfo for Ocean? These are completely apples and oranges.  

Just because new tools are available does not mean you should buy all of them. 

It's much more nuanced than that. 

Some of these new tools are REALLY valuable, but only in the right context. Think about using them for a new/different motion (not replacing your existing motion with them, in most cases). 

PS - I'm slowly distilling my (agnostic) opinions about the evolving stack on a new website — check it out at ProspectingStack.com.

Co-Founder @ Workflows.io | Growth playbooks using AI

Sales team setups 2020 vs 2025

The truth is that the job of BDRs & AEs never changed.

Their objective is still the same.

But tech changed one thing:

→ Volume 

An old school BDR could do:

• 50 dials/day manually researched
• 10 personalized emails/day
• 2-3 demos booked/week

A new school BDR in 2025:
• 200 calls/day
• 50 hyper-personalized emails
• 7-8 demos booked/week

And this was made possible ONLY with proper GTM engineering.


Here's the tech stack changes I'm seeing:

CRM: Salesforce → HubSpot
(no need for a CRM admin to make changes)

TAM Sourcing: ZoomInfo → Ocean.io
(find any company on the web)

Data Handling: Excel → Clay
(AI spreadsheet with APIs)

Global Email Coverage: Apollo → Findymail
(better coverage & validation & price)

Global Phone Coverage: Cognism → BetterContact
(best-in-class waterfall enrichment provider)

Email Campaigns: Mail Merge → Instantly.ai
(no comments here)

DM Campaigns: Sales Nav → HeyReach.io
(automating signal-based plays)

Call Recording: Manual Notes → Sybill
(AI notetakers are a must-have in 2025)

Give an old school team new school systems.

That's where magic happens.

I’ve been playing around with Gemini 3 and it’s insane. 

Would be ironic if OpenAI become the Yahoo of AI era, crushed by Google — again.

Google just woke up.

And OpenAI is feeling the heat.

A leaked internal memo from Sam Altman just surfaced.

The OpenAI CEO admits Google's AI progress could "create some temporary economic headwinds."

Here's what's happening:

→ OpenAI's revenue growth could slow to single digits by 2026
→ They're projecting a $74 billion operating loss by 2028
→ Gemini 3 Pro now leads GPT-5.1 in reasoning and coding tasks

Altman's own words: "Google has been doing excellent work recently in every aspect."

This is a massive shift.

For 3 years, OpenAI had peacetime dominance.

Now it’s a wartime scenario.

OpenAI is scrambling with social-infused features like Sora 2 App and group chats in ChatGPT in an effort to push top-line revenue and seek virality.

Meanwhile, Google's been quietly building:

→ Gemini 3 for reasoning
→ Nano Banana Pro for images
→ Veo 3.1 for video
→ TPUs powering everything

The real insight here:

• Google wasn't the first search engine
• Many came and went before it
• What matters is being the last mover

Owning the "stack" from chip to chatbot creates the only real enduring moat.

My takeaway:

Frontier LLMs are rapidly commoditising.

OpenAI pioneered the technology, but it can't defend margins when competitors match its capability.

I recently switched my new default model from GPT-5.1 to Gemini 3 “Fast” mode for general Q&A tasks because of its speed.

The sleeping giant has woken up.

And the AI race just got a whole lot more interesting.

Follow me Alex Banks for daily AI highlights and insights.

I cover the most important AI developments each week in my newsletter.

Subscribe here: https://lnkd.in/ePSZP6KF

The Narrative Company | Executive Narrative & Influence Strategy

Something big is shifting on X. And almost nobody is talking about it.

Companies aren’t just hiring engineers or PMs anymore.

They’re hiring distribution power.

X brought in Nikita Bier
Cursor brought in Ben Lang
Google brought in Logan Kilpatrick
Cognition brought in swyx ​
Coinbase brought in Alex A.
Anthropic offered $250k+ for this exact skill set

And look at Sam Altman and Satya Nadella. The precision of their posts tells you someone deeply understands this platform.

Understanding the X algorithm is becoming a real skill. And companies know it.

Why are companies doing this?

Because distribution on X now determines:

• narrative
• talent flow
• demand
• investor attention
• product adoption
• cultural relevance

It’s happening on LinkedIn too, but X is still the center of gravity for AI.

The companies that win distribution don’t just grow faster.
They shape the whole conversation.

If you understand X, you understand leverage.

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