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

Apollo.io

• 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

"The best part about our AI SDR is that the agents are never sick, never hungover, never take PTO, never leave for another job. 

They're always onli......"

Currently writing about the future of gtm tech, at The Signal. Subscribe: www.thesignal.club (it's free!). 

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. 🤘

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

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 🫡

AI CRM drama over on X 🍿 

Follow me as I make the shift from B2B SaaS founder to newsletter writer to gossip columnist 😂

Writing The Signal (Exploring AI + the future GTM playbook) | Tinkering | Playing long-term games with long-term people 🫡

The smartest GTM teams are shifting away from stagnant databases and antiquated sequencers to tools that give reps *intelligence* through signals and AI.

But you shouldn’t have to hire a consultant/agency to figure out the pros and cons of this new category of “signal-based sales” vendors. So, I vibe-coded a free tool to show which signals each vendor supports today.

Let’s dive into this new trend and how you can be an early adopter to gain an edge over your competitors. 👇

First, a quick refresher: 

Why signal-based sales?

~3% of your ICP is in-market for your product at any given point in time.

So, if you have 5,000 Target Accounts, there are ~150 companies currently in-market for your product.
Do you have that list in your hands right now? This is what a signal-based selling platform can tell you. 

Imagine having 10 reps, and each of them now has a list of the 15 companies in-market for your product (within their respective book of business) that they should reach out to this week.

This level of intelligence is an edge over your competitors. 
Knowing this information gives you a legit reason to reach out with "why you, why now" and get positive responses. 

Another ~7% of your market has a problem that your product can solve. They aren’t actively looking for a solution. But, if you reach out to them, they’re very likely to have a sales conversation (or try your product) to see if it can solve their problem.

Signals+AI can help identify this list of companies too. That’d be another ~350 companies that are highly likely to respond to a message if you reached out to them today.

Tracking signals (intent) is MUCH better than cold outreach.

I created a list of the top signals most GTM teams should track. 

And then overlaid the top "signal-based sales" vendors so you can compare.

- You can select/deselect certain vendors you're evaluating to compare them head-to-head. 
- You can also select/desect the signals that you care about (maybe some signals aren’t relevant for your GTM motion, or maybe you already have a tool for particular signals). 
- I only included the top* signal-based selling platforms (*based on traction/revenue) → ZoomInfo, Apollo, Clay, Common Room, Unify, UserGems 💎, Pocus, Sumble, Warmly,. 
- I only included vendors that have 3+ signals (eg: RB2B/Vector/etc. only have 1 signal - de-anonymizing website visitors; Trigify only supports 1 signal - LinkedIn engagers, etc.). 

This resource works best on desktop (mobile works, but isn’t great right now). 

Hope you find it useful: https://lnkd.in/gSH_fRdp

Let me know what I should add!

Writing The Signal (Exploring AI + the future GTM playbook) | Playing long-term games with long-term people 🫡

A new "category" of GTM tooling has emerged, called "signal-based selling." New vendors (and incumbents) are getting into this space and expanding the signals they support, which makes it hard to keep up! 

So, I vibe-coded a "signal-based selling → vendor comparison matrix" to show which signals each vendor supports today. 

- You can select certain vendors you're evaluating to compare a few head-to-head. 

- You can also select certain signals that you don't care to evaluate (maybe they're not relevant for your GTM motion, or maybe you already have a tool for that particular signal). 

I only included the top signal-based selling platforms. 

Let me know what I should add to it. 

Hope you find it useful! https://lnkd.in/gi7cbB4u 

Explainer video below 👇

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)

HubSpot is quietly—but steadily—improving.

Don't bet against Dharmesh Shah to lead them to the promised land of Flexible/Powerful *plus* Easy to Use. 

In 2021, I would have said you need to "graduate" from HubSpot at ~5-10 reps →  onto Salesforce + Outreach/SalesLoft + Gong. 

But today, in 2025, I think you can get away with staying on HubSpot a lot longer, maybe 25+ reps (for CRM + sequencing + and now some lightweight intent/signal stuff). We'll see where it goes from here. 

More on this topic in this post: https://lnkd.in/eYsY-a4f

Navan (TripActions) went Public a week ago. 9 years ago, I had the opportunity to be one of the first AEs and passed (leaving millions on the table). I did some consulting work for them (if I had taken that $10K check in equity instead of cash, it'd be worth half a million today — oops). This missed opportunity is an example of the crapshoot, that is tech. But there are lessons I can take away from it, by retroactively pattern-matching against it. Here are 6 lessons:

1. Vortex of Talent

High-quality talent joining a company is the best leading indicator that I've found for a company's success. This was the case in observing TA in the early days. Interacting with Lauren McGuire, Rich Liu, Michael Sindicich, and many others made it clear that this company was creating a vortex of talent.

Other examples where I observed this to be true: Okta, Figma, Stripe, Ramp.
Examples today: Rippling, OpenAI, and a couple others. 

2. Weird Business Models = Good

Raaid Hossain first introduced me to TripActions in 2016. When he described the business model, it made no sense to me. Some of the best companies have business models that are quirky or hard to understand.

3. Don't Overlook Serendipity 

I found it "too good to be true" that some random company (TripActions) shared an investor (Oren Zeev) with my then-current company (Wiser). Same reason I passed on investing in Outreach at a $5M valuation (Manny rented a desk from us at Wiser, before he pivoted into Outreach — we were one of his first customers). I thought a great opportunity couldn't come across my desk by chance.

Turns out, being around smart people and 'hanging around the net' can increase your odds of serendipity.

4. Networking is the Game

Like I mentioned before, I got introduced to TripActions and did some contract work for them. That happened because of my 'network'. Many of the opportunities throughout my career have only been possible because of who I know (not what I know). Even writing The Signal (.club) is an example of that, but at scale. I try my best to pass that 'karma' along (with no expectation of receiving anything).

5. Compounding is a Long Game

In business. Relationships. Investing. Careers.

It's easy for me to pontificate about my experience with Navan. But, I know a bunch of people (like Michael, in particular) who have grinded there for close to a decade. And they've had to overcome plenty of challenges over the years (imagine being a corporate travel business in 2020...). No such thing as an overnight success. 

6. VC is a Game of Outliers

For every 1 "Navan," I could name 9 other companies that I thought were winners, but ended up flopping. This is the high-stakes game we play in VC/tech world. But, with high risk comes high reward. 

•••

I bought some shares of $NAVN on the public market (nfa ofc). 
I am long Navan. Today is Day 1.

Congrats to Navan and everyone there!

Huge win for tech. Let's go 🚀

The Signal (.club) has grown 270% over the last year (subscribers and views). But, over the previous 6 months, I've only averaged 2 posts per month. I am changing that. Starting today, I'll be posting 2 posts per week. 

The Signal is a hand-rolled, 100% organic (anti-AI-slop), farm-to-table newsletter that explores the future GTM playbook. 

There are 5,575 of the smartest GTM founders and operators already reading. 

~20% are founders
~80% are GTM leaders (split ~evenly across Sales, Marketing, and RevOps) 
+ a handful of ICs (mostly GTMEs)

It's free. Forever. (I believe 'information should be free')
You can subscribe here: https://lnkd.in/dv3BduGr 

To existing subscribers: thank you. 
I appreciate your trust and attention, and do not take it for granted.

PS - Whether you’re new to The Signal or looking to catch up, here’s a list of the top posts 👇 https://lnkd.in/ge4T2wP5 

See you at 6,000 🫡

GTM founders and operators: What's your most contrarian belief about GTM in 2025?

A motion that's actually working, a role you're bullish or bearish on, a tool everyone loves/hates, a comp structure—anything.

Goal: Crowdsource the spiciest GTM takes.

👇

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 been wild to watch the "de-anonymised website visitor" space evolve over the last 12 months.  

Most companies were trepidatious about entering the space — for legal reasons (eg: GDPR). But, bootstrapped companies (like RB2B) just yolo'd into the space. 

Big companies like ZoomInfo and Apollo avoided the space for years — only doing "company-level" (which is, obviously, far less interesting for sales teams). 

Then, earlier this year, ZoomInfo released person-level. 

And Apollo is supposedly releasing their version soon too (presumably taking the cue from its public-company rival, ZI). 

Interestingly, Clay launched their version of natively de-anonymising website visitors, but it (currently) only supports company-level (not person-level). (But, they have such a crazy/good ecosystem that makes it easy to plug in RB2B, Vector, etc. and let it rip.) 

Many companies (like Common Room) partner with providers via white-labeling.

As I understand it, a lot of these companies have "their own version (in-house)" but it's actually just 5x5 data in the background. (I don't exactly know how this works technically, so please someone chime in with a better explanation.) 

I'll be watching this space closely to see how it evolves in the AI-era, as "agents" — both sales agents and buyer agents — are inevitable. And that will drastically change the 'website de-anon space' :) (you can't "de-anonymise a buyer-agent that is on your website doing research). 

Curious if a market map of this space would be helpful for anyone...? (to add to this growing inventory of market maps: https://lnkd.in/gSH_fRdp

12 examples of ways I've used AI to find "rich" data points to build a company fit/tiering model to enrich your CRM: 

1. Privacy policy page updated in the last 30 days 
2. Large number of SKUs (products) on a retail website 
3. Using open-source AI tools (like Pytorch, tensorflow, etc.) 
4. High-growth indicator (raised $100M+, founded < 5 years ago, employee count < 50)
5. 40%+ of employee count are engineers 
6. "B2SMB" meaning a prospect has a price on their website under $50/month
7. Negative review on review site 
8. Currently hiring 3+ 
9. Presence of a very specific title (eg: UX Researcher, Search & Recs, RevOps) 
10. Usage of certain technologies being used at a company (scraped from job descriptions)
11. Mentions of AI initiatives in the last 30 days (on social, or other public data) 
12. The prospects' prospects sell to an ICP with a strong "digital presence"

These are "rich data points"
(Indicators of Company-Fit)

These are not "signals" 
(Signals show timing/intent to buy)

Use these to determine whether the company is a High Fit, Medium Fit, or Low Fit account to target. 

Then, layer on (intent) signals to determine the timing of when to reach out to each company on this list. 

•••

AI doesn't *replace* smart GTM strategy. 
But, it can help *scale it* in a way that wasn't possible three years ago. 

PS - Here's an example I built, showing a few "rich" data points plus "basic" data points, for a hypothetical example of an AI CodeGen Platform (think: a company like Cursor) 👇

Two days ago, someone asked me which sequencer (sales engagement platform) I recommend. I told them, "it depends." 

Then, I pulled up superwhisper and sent a 3-minute recording to my intern, Claudia (aka Claude AI), and had her create a graphic for me. 

What would you add or change? 

Check it out: https://lnkd.in/ge8H4E4M

People often ask me whether they should use Common Room or Clay (or another signal-based selling platform). My answer is evolving because the GTM AI landscape is shifting so quickly (new signals, workflows, features). 

Every few months, the category looks different than it did before. Which makes it hard for teams to feel confident in their tooling decisions, especially when many buyers still treat Common Room and Clay as if they're interchangeable. 

Here's my 2c on the differences and how you should evaluate using one, versus the other (or both!).👇 

1. I think both are great :) Really, I do. 

2. They are apples and oranges, but have some overlapping use cases (maybe 20%). 

3. Clay takes the approach of centralizing GTM data/strategy/orchestration (via a GTM Engineer building Clay tables) to run plays. While Common Room has built a product that RevOps, SDR Leaders, or even reps can use. 

4. Common Room has a UI for reps to work out of; Clay does not. So if you're looking for a place for reps to work out of, CR is the right choice, not Clay. 

5. Common Room is incredibly easy to set up and use (takes 10 minutes to learn). Clay is difficult to use (I say it takes 50+ hours to become an expert at Clay — which is why Claygencies exist). 

6. Clay is more flexible/powerful (horizontal), while Common Room is more opinionated (off-the-shelf Plays are super easy to spin up). 

7. If you don't have the resources (employees in-house, or money to outsource) to use Clay, Common Room is a great product. 

8. But again, they are apples and oranges. And many companies should — and do! — use both Common Room and Clay at the same time. 

I'll be presenting on the differences (and quickly evolving changes) between Common Room and Clay on a webinar where we'll discuss: 

- How the GTM AI category is evolving
- Where Clay fits vs where Common Room fits
- When the “use both” answer actually makes sense
- How to evaluate tools in a market that changes quickly

Come hang! https://lnkd.in/dYPEB3v6

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.

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The founder of a $20B company is staying up until 3am to learn AI and ship experiments using it. 

What's your excuse?

Founder and CTO at HubSpot. Helping millions grow better.

It's almost 3am, but I'm on a roll.

UPDATE: It *was* 3am last night when I started writing this, but I (wisely) decided *not* to post this last night. 

Day 3 of the CompanyResearch .ai launch.

Just released an update:

1) Now uses the latest GPT 5.1 LLM making results better and runs a bit faster (though the agent is still slow because it does so much)
2) People section more accurate
3) Inline search for specific people at the company
4) New section that shows Tech Used by the company
5) Bunch of bug fixes and such.

Keep the feedback coming as to issues you find or what you'd like to see next. Next update tomorrow. I believe in quick iterations.

Thanks for your support.

taplio

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