Manny Medina

Executive Chairman of Outreach, the leading Sales AI platform

Outreach is the leading sales execution platform that helps market-facing teams efficiently create and predictably close more pipeline. From prospecting to deal management to forecasting, our platform leverages automation and artificial intelligence to help revenue leaders increase efficiency and effectiveness of all go-to-market activities and personnel across the revenue cycle. Outreach is the only company to offer sales engagement, revenue intelligence, and revenue operations functionalities in a unified platform. More than 5,500 companies, including Zoom, Siemens, Okta, DocuSign, and McKesson depend on Outreach to power their revenue organizations. Outreach is a privately held company based in Seattle, Washington, with offices worldwide. To learn more, please visit www.outreach.io.

The Rule of 40 breaks with AI.

For those who don’t know, the rule of 40 states that your company is healthy if growth rate + profit margin ≥ 40.

Traditional SaaS has got used to 20% profits with costs breaking down at:
- 20% COGS
- 25% S&M
- 25% R&D
- 10% G&A

20% profit + 20% growth = healthy company. 
Easy. 

In the agent world, with all your LLM and tool calls, COGS often goes up to 60%.

BUT NONE OF YOUR OTHER COSTS GO DOWN. 

That’s why AI companies are often looking at -20% profits.  

Those companies must grow at at least 60% YOY durably to be healthy.

Some are seeing that level of growth, but it’s not sustainable with the current level of competition. 

The party has to end sometime.
Something will have to give. 

One area that will change is sales comp. 

I think we’ll see a lot more companies moving sales commissions from % of revenue to % of margins. 

It’s not a new concept. In many industries this is the norm.

Door to door sellers selling encyclopedias or cookware were doing it before. 
And if they gave a discount, it came out of their own pockets. 

Sellers were incentivized by the same metrics that mattered to the business - more deals, bigger deals, and bigger margins.

How are you compensating sellers in the AI era?

I’m hosting a roundtable on this topic soon. DM me to get on the guestlist.

Sign up at Paid.ai. We help AI Agents get paid!

How do you raise a seed or series A? 

This came up at Slush this week and I wanted to share my 2c.

There is a tactic for seed fundraising that makes it way faster. 

FIRST: Find all the different seed stage VCs that agree with your hypothesis. With your view of the world

There are more seed VCs than stars in the Milky Way. But there’s no magical database. You have to dig in. Trust me though, you’ll get bored before you run out.

A handful of these WILL be into your hypothesis. Probably more than 10.

So find them all.

Be diligent. Dedicate a day a week to finding these people, connecting with them and seeing if they agree with your hypothesis. Develop that relationship. Show them progress. 

This will take a couple months but it will be worth it. And it will save you time later. 

SECOND: Find out where they will take risks. 

There’s 2 types of risk VCs take:
- Those that take market risk - they find amazing technologies and assume that the market will be there. Like a16z. These are great fits to strong technical founders.
- Those that take technical risk - they identify markets that are huge and find founders/technologies that can deliver value for those markets. Like Sutter Hill.

Find the one that is right for you.

Those people will be willing to write you a small check to find out more.

It’s like a game of poker. Show your cards. If they’re interested, they add chips to the pot. 

Your job? 

All you have to do is convince them that you are the right founder for this time for that space. 

Land five believers and one will go all in. That’s how rounds start.

Thanks Slush for an amazing event. It was super energizing to spend time with so many founders of different types and backgrounds, all coming together to help each other and grow the industry.

Shout out to Alexander Schmitt for joining me on the founder’s stage.

The POC is dead. Long live the POV.

The biggest mistake companies make is using proofs of concept to prove their technology works.

Nobody cares if your tech works. Buyers and champions care what it means to THEM. Personally and professionally. Get them promoted? Boost their resume? Elevate their status?

What value will they get? So my advice is to stop proving your tech works. Start proving VALUE - in the shortest possible time

Sales leaders resist POVs because they slow down sales cycles. But done right, POVs can 8X your conversion rates. 

- A normal opportunity goes from S1 to Closed-won at 10-20% conversion rate 
- An opportunity that moves to a POV goes from S1 to Closed-won at 80+% conversion rate 
- 90% of POVs will convert to closed won
- That’s a 70-80% win rate for those who offer POVs vs 10-20% for those who don’t

Yes, your sales cycle lengthens. But you lock competitors out. Most companies won't run multiple POVs. You get in first and close the door behind you.

What if your POV fails?

Well, make sure it doesn’t. 

OBSESS over delivering value. Throw your forward deployed team at it. If you don't, someone else will.

Still no results? Find out WHY (usually team misalignment or tech issues). Communicate it. Reset the POV. Try again. No close-lost, just trying again.

Here’s my 5 steps to win with POVs:

1. Get there first - Speed wins. Close the door on competitors.

2. Study their POV history - What did they pay last time? What worked? What failed? 

3. Find their approval threshold - How much can they spend without CEO/CFO signoff? 
When in doubt charge double the last successful pilot price - everyone leaves money on the table

4. Win fast - All hands on deck. Embed your team. Prove value ASAP.

5. Get the testimonial right after winning the POV - Use it to win your next POV. Rinse and repeat.

Congratulations. You've got a repeatable process for winning customers that STICK.

Go win your first POV.

I'm running a roundtable around selling AI agents next week. DM me for an invite.

AI agents are unlocking new and BIGGER budget pools. 

A CEO I just interviewed for the Get Paid podcast told me that for every $1 a company spends on software, they will spend $9-12 on the labor around it - administration, training, implementation, management etc.

Most SaaS companies are used to getting their budgets from the $1 software and tools budget pool. And as they launch their AI agents they are still fishing in that pool - and splitting that $1 with 50 other vendors. 

The new guard of AI natives are playing differently. They are positioning themselves as labor replacement and fishing in the $10 labor pool - and landing deals that are 2-5x higher ACV than their SaaS predecessors. 

If you’re a SaaS sales team selling  in AI, you need to start fishing in the labor pool for much bigger wins.

And to do that, you need to rethink how you do discovery. 

MEDDPICC taught us to find pain, implicate that pain, and position our product as the solution. That worked when we were selling software.

But if you want to get to labor budgets, you need to uncover not just the pain, but how they are solving it right now! Or how they're planning to solve it. In most cases, the answer is - hire someone! 

Hiring or outsourcing is instinct for most buyers. 

Even I caught myself doing this recently. Discussing business gaps with my cofounder Raj, his instinct was to buy or build a tool. Mine? Hire someone to fix it.

"Why are you always hiring people?" he asked.

Good question. My cohort of SaaS founders has been conditioned to solve problems by finding smart people. I was ready to open a $200k role. He was thinking of a $10k AI tool that does the same thing. Same value, wildly different price tag.

So, the labor pool. Here's 3 things SaaS CROs and sales leaders need to do to unlock it:

- Change your discovery to find pain AND how they’re solving pain now, where are those budgets coming from and how large. 

- Implicate the pains and the potential solutions - If you open headcount, or outsource to a BPO, what’s the budget impact this year and next?

- Lean on the zeitgeist - Boards are asking CEOs for agentic strategies. Position yourself as the agentic solution and give your customers something to take back to the board. 

I'm hosting a roundtable discussion on selling AI agents on Thursday. We'll be talking about this among other things. DM me to join!

Your Best SaaS Sellers Are Leaving 80% of the Deal on the Table.

Not because they're bad. Because they're too good at what they've always done.

Hear me out. Here is a classic example:

🔥 Seller demos an AI agent that handles 80% of inquiries.
🔥 Buyer sees it could replace 8-10 FTEs. $800\year value.
🔥 Seller talks about the last purchase and where the budget came from.
🔥 It came from the tools budget, and there is $100K left in that budget.
🔥Closes $75K. Celebrates.

$725K just walked out the door.

🛑 The Problem? Muscle Memory.

Your President's Club winners spent years mastering tools-budget discovery:

🎯 Find the pain in current systems
🎯 Quantify inefficient software costs
🎯 Find similar past transactions
🎯 Close against IT budget

This playbook works. That's why it's killing you.

AI agents don't replace software. 

They replace work.

Stop asking: "What's your software spend?" Start asking: "What's your fully-loaded cost per employee?"

💰 Three Budgets You're Not Touching:

💲 Shadow headcount: Approved roles they can't fill, it lives in CFO's workforce model, not IT budget.

💲 BPO spend: $2M offshore service contracts (not categorized as "software").

💲 Redeployment value: What could 20 freed-up humans accomplish? (Lives nowhere on the P&L).

✅ Here's the Test:
Run your next discovery twice. Once with your tools lens. Once with a labor lens.

Compare the ACVs.
The difference will break your addiction to the tools budget.
The choice is simple:
Close $50K deals against Salesforce licenses.
Or close $500K deals against headcount.

Then think about what you will do with all that extra commission? 😄

A big shift doesn't just improve things - it renders the old way of doing business completely useless.

I had a great conversation with Nicolas Sharp, CEO of Attio, on the Get Paid podcast recently. He used a brilliant analogy.

"Imagine you're building a wooden navy with sail ships. And then suddenly, people are building metal ships that have steam power. It doesn't matter how big your navy was before, it's just completely useless.”

This seismic event is happening right now in the CRM space with the rise of AI.

Listen to the full story on the Get Paid podcast to hear:
- Why the old playbooks are being thrown out
- How Attio built a platform to be "possible and easy", not "possible but difficult”
- The moment they realized their original niche CRM was a trap.

Search “Get Paid with Manny Medina” on YouTube or wherever you get your podcasts!

I'm hosting a roundtable on Thursday. We'll be talking about how sales changes for AI companies vs traditional SaaS, and what tactics your peers are using to win more deals and get back to growth. 

Want to join? DM me for the link!

Throw back to this gem from Pat Grady earlier this year:

A lot of the companies that are ripping right now have negative gross margins.
That's okay.

If you have negative gross margins because you're chronically underpricing your product, that’s not good.

But you have negative gross margins because you are sub scale and you can see a path toward getting to 60-80% gross margins over time, that's okay.

People are getting a little more reasonable on data privacy. Instead of needing local instances in every European country, for example, you can have a single instance that spans European country.

For enterprise customers, instead of needing dedicated instances per customer because they're worried about their data leaking into somebody else's results, people are now accepting multi-tenancy.

There are lots of different things allowing capacity utilization to go up and COGS per unit to go down.

So, if your gross margins stink today, you might actually be okay as long as you can chart the path to having healthy gross margins over time, which obviously Paid can help you with 😉

Full episode on Youtube or wherever you get your podcasts. Search 'Agent Talk #7: Pat Grady (Sequoia) - What actually works in AI startups'

The SaaS playbook just broke.

- SaaS companies added just 30% net new ARR in 2024-25 (Jamin Ball)
- Growth crashed from 36% to 12% (Aventis Advisors)

Billions of dollars are stuck in SaaS no man’s land.

The only way out is to get back to growth.

The good news is that you're already sitting on the solution.

You have a treasure trove of workflow data and operating procedures. 

The companies winning right now are already turning that knowledge - procedures, data, and policies - into AI agents. 

But most are still figuring out how to sell them. And most are getting it wrong.

Here’s 3 ways I’m seeing SaaS companies package their AI products, ranked from worst to best:

🚩OPTION 1: The "mega seat"
Bundle AI into existing seats at a premium.

It sounds safe, but it’s not.

Procurement is already cutting 10-20% of low-usage seats. Layoffs mean fewer buyers. You're fighting seat compression with... more seats? Bigger seats? 

Procurement will tell you to give them the more premium seat for the same price as the last seat if you want to maintain the contract value. 

Congratulations you got yourself a flat renewal! Yay! 

👍🏽 OPTION 2: Seat + credits
Attach AI credits to seats.

Simple to understand. Allows you to start demonstrating value without rocking the boat.

But not so simple to adopt.
How do you retrain customers to use new AI features? 
You have to put work here - otherwise a pure agentic company will convince your champion that you were built 15 years ago and no longer relevant.

This is a great way to get started down the path to … 

🚀OPTION 3: Launch a new product
Package your AI into a brand new product.

Look at Intercom and Fin. Matillion and Maia. Salesforce and Agentforce.

This approach wins because:
- You own the new category before competitors wake up - this category is for you to lose
- Customers pay for results, not seats
- Sales cycles collapse - no one argues with ROI
- You escape the compression death spiral 

The companies already making this shift are pulling ahead. 

The rest are still debating seat pricing while their ARR bleeds out.

I'm hosting a closed-door roundtable with SaaS leaders navigating this exact transition. DM me for an invite.

“You don't buy enterprise software, you inject it into your corporate veins.”

Quote of the week courtesy of Dave Kellogg on the latest episode of Get Paid.

In markets with high switching costs, like a lot of enterprise software, land grab strategies make sense.

But in markets where customers can switch providers with a click, giving away your product for free doesn't build a moat, it just burns cash.

“If you’re in a market with high switching costs, grab share, because you’re going to get something for it downstream.”

Listen to the full episode for more great insights into AI pricing, FDEs, sales commission structures, hiring AI natives vs SaaS veterans and tons more.

Search “Get Paid with Manny Medina” on Youtube or wherever you get your podcasts.

We are here to get all AI agent builders to grow - whether you are Saas company looking to re-ignite growth 

or a startup or solopreneur just building your AI Agent 

 Paid will help you grow. Fast!

Investor - Sourcery - LA/SF/NYC

Manny Medina, Co-Founder & CEO of Paid, & former CEO of $4.4B, Outreach, joins me in London to discuss the $19.9T AI agent opportunity.

Roughly $300 billion in SaaS revenue is “trapped” inside legacy pricing & delivery models

After scaling Outreach from scratch to 6,000 customers, 220,000 active users, & $250M in ARR.. 

Manny Medina  saw firsthand how traditional SaaS monetization models break down when software shifts from user-driven actions to agentic systems performing complete end-to-end workflows.

He founded Paid  to close this gap.

Early customers have already reported 20–40% revenue growth within six months of adopting the platform.

With $33.3M in total funding, Paid is backed by Sequoia Capital , Lightspeed , EQT Ventures & Growth , GTMfund , & FUSE.

(thank you to Max Altschuler at GTM Fund for the intro!)

Love when a legend like Nick Mehta drops tactical gold

Board Member: Gainsight, F5 (NASDAQ: FFIV), Pubmatic (NASDAQ: PUBM)

How do you run client meals that people actually look forward to?

I've led hundreds of customer lunches and dinners over the years. As Erica Mansfield Robin Merritt Emily Wilkes Kat Leong Lauren Olerich know well, I have a very particular (or as some might say, "high maintenance") approach to making sure guests have a special experience.

As a note to myself, this is a partial list of my event eccentricities:

1. Topical Theme: Something attendees are already thinking about.
2. Goldilocks Zone: For one discussion, 20 is too big. But 8 RSVPs mean 5 might show. I've found 14 signed up is perfect.
3. Reply to RSVP: For intimate settings, having to click on a site and fill out a form reduces attendance.
4. Reminder: A second email with who's coming so far has a big effect on reg (social proof).
5. Day-Before Email: Get folks in the right state of mind with an agenda and prep info.
6. Real Private Room: There are so many fake private rooms out there. I've seen ones where a curtain divides the room from the restaurant. Make sure it's truly isolated.
7. Cozy Size: Nothing kills a vibe more than a 40 person room with 10 in it.
8. Turn It Down: Tell the server to cut the music in the room when dinner begins.
9. Mingle: Allocate 30 min before dinner for folks to meet each other.
10. Assigned Seats: Not always required but good way to make sure people meet each other.
11. One Convo: Jeffersonian dinners are special.
12. Active Moderation: Make sure everyone gets a chance to speak.
13. End On Time: Nothing worse than customers feeling like they can't leave.
14. Checkout: Final question - e.g., "one word to describe what you learned today but you can't repeat someone else's word?"
15. Thank You Email: Right after with a summary and contact info.
16. Team: None of it is possible without an epic team like the folks I listed above.

What have you learned about running small customer meals?

it's the Friday after Thanksgiving 

possibly the least productive non-holiday day in the year 

enjoy

"How do I set up a team internally to ship AI Agents faster?"

I get this question from a lot of CEOs about moving at start-up speed and capture the AI opportunity in front of them -before a YC startup gets it 

I love my friend Matthew Scullion's approach 

Set up an A Team. And no - this doesnt mean that the rest of your company is the B team 

This is a Series A Team - ie if you were to be a series A funded company, this is the team you would build 

fast, empowered, funded, and out to capture market share! 

Super excited for the Matillion team and their Maia agentic data engineering team

Matthew Scullion shares how Matillion set up the “Maia A Team” — a small, cross-functional unit designed to operate like a Series A company inside their growth-stage organisation.

The goal: move fast, test assumptions, and avoid pulling the entire business into early uncertainty.

Full episode now live.

Why do most AI Agent pilots fail?

B/c the agent outright doesn't work!!!!

Overpromising and underdelivering does not help your business 

in Saas, we lived way too long with the "Fake 'til you make it" ethos

In the world of AI agents, that breaks. Your agent either does the work within a small error tolerance 

or if doesn't

that's why most AI agent revenue is pilot revenue 

ARR ❗ 🟰 ERR (experiment RR)

you need to land the pilot by delivering work - Ariel Harmoko show us how he does it at Artifact AI

A common reason AI agents break down in production:
they don’t know when they’re wrong.

Ariel Harmoko explains how Artifact AI agents operate with confidence thresholds.

If an action falls below a certain level of confidence, it doesn’t guess.
It flags the task back to a human.

A document might match a transaction total, but if the vendor doesn’t line up, the agent pauses instead of pushing bad data downstream.

This is what real agent systems look like in production.
Not blind autonomy.
Not “set it and forget it.”
But agents that escalate uncertainty instead of hiding it.

This is the difference between demos and systems you can actually trust.

Full episode is live. Link in comments

Gold from Jake Saper 

Try outcome based pricing for at least a portion of your arr. 

it will instill discipline with  delivering results and holding yourself accountable for what matters to your customer 

Not just shipping products

General Partner @ Emergence Capital

Founders: you should experiment with outcomes-based pricing, even if it adds negligible ARR in the near term.
 
Most teams dismiss it because “we/the customers aren't ready” or “it's too risky.” But the payoff isn’t in short-term revenue – it’s in the discipline you build.
 
When you force yourself to tie even a small piece of pricing to customer outcomes, you and your product team have to answer harder questions:
 
→ What outcomes are we actually driving?
→ How do customers measure those today?
→ How do we prove we’re the ones driving them?
 
You don’t need to flip your whole model overnight.
 
Keep 90% of revenue on your current model (seat-based / usage-based / platform-based) and add a 10% kicker tied to clearly defined outcomes.
 
That 10% won’t move your top line. It will force you to build the muscles of value creation and value attribution – muscles you’ll need later when deals get more competitive and CFOs scrutinize every line item.
 
Prosper AI has been feathering in outcomes-based economics on a portion of their contracts and recently talked with the Wall Street Journal about the impact (article in comments). It’s still early, but the discipline is already shaping how they build and sell.
 
The same idea applies to PLG.
 
Even if you never fully ship a self-serve motion, asking “What would it take for someone to get value in 5 minutes with zero hand-holding?” pushes you to simplify onboarding, clarify value props, and reduce friction. That’s useful whether sales is human-led, product-led, or both.
 
Both moves – outcomes-based pricing and PLG – are worth building towards even if they never become your primary GTM strategy.
 
Because the real win isn’t the new line on your pricing page.
 
It’s the compounding discipline of designing around customer value and being able to prove it – which, over time, is how you earn the right to build a more valuable company.

Incredible summary of what’s about to happen to the world of data 

William is a prophet!

Data Mercenary

If you haven't already heard the starter pistol go off, then listen up: You are not alone, but ... the race has begun. As we are all in the midst of annual planning at year end, thinking about a future that holds both utopian and dystopian possibilities, yesterday was the time to act. Beyond hyperbole, there is a clear and true message that the hope, anxiety and fear we are all experiencing serve a useful purpose.

Embodied in the symbol for Matillion, the peaks of two mountains, there is a hidden truth. While most only see the peaks, the pioneers and explorers see the gap. You don't trek from mountaintop to mountaintop to the future. You look for the space that allows you to pass beyond the obstacle to achieve your outcome. Now is the time to abandon the old goals of standing on the highest mountain peak on a ridge littered with the corpses of those who inherited tired aspirations.

If there is one podcast you listen to this year, make it this one. Within contains a sweeping history of the technology landscape and the single most important first-principle that has ever existed: Data and one’s capacity for synthesizing its meaning is the foundation of all innovation.  The great productivity leap of Maia will not only solve yesterday’s engineering problems, but enable the endless, continuous, constant, ouroborotic paradigm-shifting skill of always watching the horizon, looking for the gap, achieving your outcome and seeking what is next.

Look around, if you think your peers aren’t racing because you don’t see them on the ridge or on the peak planting a flag, it’s because they started the race months ago and they aren’t aiming for the tallest mountain, they’re aiming for the closest star.

Thank you for the inspiration Matthew Scullion and thank you Manny Medina for bringing the message to us.

https://lnkd.in/ecUu9vW3

Matthew Scullion drops a masterclass on how to capture value from the headcount budget - which is 10X larger than the tools budget! 

His buyer shares that next year they will be hiring 20 data engineers to work through the backlog of projects

20!!!! data engineers. 

at $200K a piece

let that sink in

he just walk into a $4M budget that was not available to him UNTIL he had a headcount conversation, and position his AI data engineering team agent, Maia, as a new headcount saver

Use the headcount you already have. Give them all access to Maia. Clear the backlog. 

Split the $4M budget! 

Results? ACV up 10x    <- this can be you!

Every data leader knows this moment:

The backlog is growing.
The team is overloaded.
And the first instinct is: “Let’s hire 20 more engineers.”

A ~$4M decision: Matthew shares a conversation with a new CDO who was ready to make that call.

His response was simple:
“Before you hire 20 people, let me show you what agents can do.”

A familiar scenario — and one more teams are rethinking today.

Full episode now live: https://lnkd.in/e3FuTa8M

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