Today we sit down with Doug May, SVP of Productivity at Harness, to discuss one of the most critical yet overlooked aspects of a healthy organization: Sales Productivity. Doug has had an illustrious career at elite organizations including Datadog and Databricks, and he brings that expertise to Harness, where he has cut ramp time in half and increased per-rep contribution by 43%.

We explore the "F1 engineering team" analogy of GTM support, why productivity metrics are the ultimate indicator of a company’s health, and the specific questions every candidate should ask to de-risk their next career move.

🙌 Thanks To Our Sponsor! Aurasell AI: https://www.aurasell.ai

🏹 Key Topics Covered

00:00 - In this episode

02:44 – What is Sales Productivity Strategy?

04:00 – The F1 Analogy

05:15 – Measuring Every Step of the Process

07:45 – Cutting Ramp Time in Half at Harness

09:05 – Optimizing the Hiring Profile

12:02 – How Investors Use Productivity as a Health Signal

13:57 – Questions to Ask During Recruiting

18:23 – Benchmarks for Rep Attainment

20:00 – The Six Secrets of Excellence

25:34 – Productivity Leadership and the CRO

31:12 – Understanding CAC and Scalability

42:45 – The 5X Win Rate Improvement

💥 3 Biggest Lessons:

The F1 Engineering Team Approach: Doug views the productivity function as the engineering team behind an F1 driver. Elite sellers are the drivers, but they shouldn't be dropped into any car. A world-class productivity strategy provides the fuel (pipeline), the GPS (RevOps), and the tools to optimize every contributor's horsepower to ensure they reach "elite" status.

De-Risk Your Career with Data: Candidates should scrutinize an organization’s health through productivity metrics, not just OTE. Key indicators include ramp time (e.g., 7 months vs. 15 months), the percentage of ramped reps achieving quota, and average deal size. If a CEO does not list sales productivity as one of their top three success metrics, it is a sign that the organization may view sales as a cost center rather than an investment engine.

Decision Velocity is Critical for Growth: In a high-growth environment, waiting to make decisions is not an option. Doug highlights the concept of one-way vs. two-way doors: identifying which decisions are reversible so the team can maintain high velocity. This agility allows an organization to respond to data in real-time and win the competitive race.

💬 Notable Quotes

"We are basically there to optimize the horsepower of every single seller in the organization." "Any change is incremental until you pile them on top of each other."

"If sales productivity is not one of [the top three metrics], I should probably walk away." "Great talent... can make hay in any field if they’re supported properly."

"You have to be willing to do the hard, hard stuff."

🙌 Thanks for listening! This episode was hosted by Simon Kouttis and Ollie Kuehne, founders of Hunters & Unicorns, and post-produced by the team at videoforce.pro. If you enjoyed this episode, please drop a like/share and subscribe to our channel!

🦄 Connect with Hunters & Unicorns

Website: http://huntersandunicorns.com

Twitter: http://twitter.com/HuntersUn1corns

Instagram: http://instagram.com/huntersandunicorns

Blog: http://huntersandunicorns.com/blog

#softwaresales #huntersandunicorns #playbookuniverse #salesproductivity

In this episode, we sit down with Anish Agarwal, CEO and Co-founder of Traversal, for a deep dive into the substance behind the AI noise. Anish, a former Columbia professor and researcher in causal AI, shares his unique journey from academia to founding an organization disrupting the site reliability engineering (SRE) and observability space.

We explore the critical difference between "AI wrappers" and companies building genuine infrastructure, the emergence of the "Forward Deployed Engineer" in the sales pod, and how to identify technical moats in a world where models are rapidly evolving.

🙌 Thanks To Our Sponsor! Aurasell AI: https://www.aurasell.ai 🏹

Key Topics Covered

00:00 - Intro 01:53 – Traversal: the AI Site Reliability Engineer

03:14 – AI Wrappers vs. AI Native

04:58 – The Importance of a Technical Moat

07:27 – Human Error and Data Scarcity

08:56 – From Experimental Budgets to Production Reality

14:42 – The Rise of the "Forward Deployed Engineer"

22:50 – From PhD to Founder 34:50 – Rethinking the Observability Stack

💥 3 Biggest Lessons: A Technical Moat Lives in Infrastructure and Data: Anish argues that a sustainable AI company must be an infrastructure and data company at its core. If your value is purely in prompt engineering or simple workflows, model companies (like OpenAI) will eventually "eat" those features. A true moat is developed through context engineering—managing petabytes of data in a way that is consumable for LLMs—and functioning on systems elements haven't been trained on, such as private observability logs.

Target Workflows Where Humans Are "Bad": When assessing the longevity of an AI solution, look at the tasks it automates. If a human is already good at the task, data for that workflow is easily collected and fed into public models, making the solution easy to replicate. The most defensible AI companies solve superhuman problems—tasks humans are inherently bad at or data types (like time-series logs) that are not publicly available for training.

The Chasm Between Pilot and Production: We are entering a phase where the "experimental AI budget" is drying up. Winners in the next two years will be companies that can connect their year-long pilots to hard labor or software spend. This requires tight technical scoping and a "pre-sales mindset" that lasts long after the initial check is signed to ensure the product survives the first renewal cycle.

💬 Notable Quotes

"As much as we’ve been an AI company, we’ve been an infrastructure and data company."

"If you are creating prompts or simple workflows, the models will just keep eating those up."

"The un-sexy part of AI—how you actually deploy this and comply with regulation—that is what is now maturing."

"Sales is a first-class citizen in the company... a less good product can win if you have the right team to bring it to market."

"I’ve become a student of sales... it eventually becomes a systems engineering problem."

🙌 Thanks for listening!

This episode was hosted by Simon Kouttis and Oli Kuehne, founders of Hunters & Unicorns, and post-produced by the team at videoforce.pro.

If you enjoyed this episode, please drop a like/share and subscribe to our channel!

🦄 Connect with Hunters & Unicorns Website: http://huntersandunicorns.com Twitter: http://twitter.com/HuntersUn1corns Instagram: http://instagram.com/huntersandunicorns Blog: http://huntersandunicorns.com/blog #softwaresales #huntersandunicorns #playbookuniverse

Today we tackle the noise surrounding the AI movement with Paul Klein, CEO and Founder of Browserbase. With a career spanning early-stage Twilio to raising $70 million in under two years for his own infrastructure startup, Paul brings much-needed critical thinking to the "AI bubble" debate.

We explore the bridge between old-world sales principles and modern, developer-first GTM strategies. Paul breaks down why Product-Led Growth (PLG) should be viewed as a pipeline engine rather than just a revenue machine and explains the power of the "Logo Flywheel" in creating executive FOMO.

🙌 Thanks To Our Sponsor! Aurasell AI: https://www.aurasell.ai

🏹 Key Topics Covered

00:00 - Intro

02:20 - AI Bubble: Hype vs. Genuine Technology Value

05:19 - Building the "Logo Flywheel"

08:42 - Raising $70M in 2 Years

10:15 - Infrastructure Power

11:13 - PLG as a Lead Engine

19:15 - Selling Inspiration, Not Just Problems

32:14 - From Twilio to Browserbase

36:00 - Building a Generational Company

💥 3 Biggest Lessons:

Bring Critical Thinking to the AI Wave: While technology is creating real value, Paul warns against blindly buying into the financing hype. He emphasizes that both sellers and buyers must look for sustainable business models and long-term viability. Success in the AI era requires a "critical eye" to separate genuine innovation from experimental revenue that may not renew.

The "Logo Flywheel" is the Ultimate De-Risk: For infrastructure companies, social proof is invaluable. Executives often make decisions based on FOMO (fear of missing out), looking for established logos to validate their choice. By landing high-profile customers early, you create a flywheel effect that makes subsequent sales easier and de-risks the purchase for the next executive buyer.

PLG and Sales are Not a False Dichotomy: Paul challenges the idea that you must choose between being a PLG company or a sales-led company. Instead, use PLG as a lead engine. Letting developers self-serve allows them to "try on" the software like a jacket, while the sales team focuses on high-value "graduation" opportunities—converting active users into enterprise-level champions.

💬 Notable Quotes

"We need to bring critical thinking to the AI bubble and the AI wave."

"If you can get these great logos that inspire other companies, it's going to be much easier to sell."

"Developers... want to try it on. You don’t just buy a jacket without trying it on."

"You have to think a lot more like an angel investor or a VC than like a salesperson."

"First move is often lose... someone can copy everything... but they won’t get the 10,000 hours of customer understanding."

🙌 Thanks for listening! This episode was hosted by Simon Kouttis and Ollie Kuehne, founders of Hunters & Unicorns, and post-produced by the team at videoforce.pro. If you enjoyed this episode, please drop a like/share and subscribe to our channel!

🦄 Connect with Hunters & Unicorns

Website: http://huntersandunicorns.com

Twitter: http://twitter.com/HuntersUn1corns

Instagram: http://instagram.com/huntersandunicorns

Blog: http://huntersandunicorns.com/blog

#softwaresales #huntersandunicorns #playbookuniverse