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Episode 52
with Mohit Aron, Founder of Cohesity
06.15.2024 | By: Ashu Garg
Mohit and I explore his hard-won lessons from starting Cohesity and Nutanix, and what he wishes he knew at the outset.
Of all the founder-CEOs I know, Mohit Aron stands out as a true expert in building enterprise software businesses.
His track record speaks for itself. In 2013, he founded Cohesity, an AI-powered data management and security platform that has become an industry leader. Prior to Cohesity, Mohit co-founded Nutanix, a pioneering enterprise cloud computing, storage, and networking company that went public in 2016.
Part of what sets Mohit apart is his commitment to codifying the lessons he’s learned throughout his entrepreneurial journey. In his second appearance on the B2BaCEO podcast, he offered his frameworks for validating startup ideas, finding product-market fit, and building and managing high-performance teams. He also spoke candidly about the challenges and rewards of growing from a technical founder into a business leader.
Here’s what Mohit shared.
Before writing their first line of code, Mohit advises founders to pressure-test their ideas by articulating, in writing, exactly why their startup will succeed. This “hypothesis document” should include four sections:
By forcing founders to distill their thinking, expose assumptions, and confront hard questions upfront, this exercise can preempt common modes of startup failure. Done right, it can meaningfully tilt the odds of startup success in the founder’s favor.
As Mohit puts it: “If you build this hypothesis document and you are intellectually honest, I can nearly guarantee that your company will be a success.”
Patents and IP offer limited protection in enterprise tech. The key to sustainable differentiation, Mohit explains, is having an ambitious vision that goes well beyond your MVP.
“You start with the MVP, and then you iterate toward a bigger vision. Even if people try to copy you, you’ll be further ahead by iterating on that vision.”
Mohit illustrates this principle with examples from Nutanix and Cohesity. Nutanix’s MVP targeted virtual desktop infrastructure (VDI) workloads, but the vision was to be a broad platform for compute, storage, and networking. Cohesity’s MVP focused on backing up VMware environments for large enterprises, but the longer-term plan was to become a comprehensive data management and security platform. In both cases, would-be competitors soon found themselves chasing a rapidly expanding platform rather than a narrow point solution.
A compelling MVP solves a specific pain point better than anyone else, but a compelling vision is multi-step and acts as a roadmap for turning that MVP into a platform. It forces you to architect your product for the future and avoid over-indexing on the first use case at the expense of the second, third, or tenth.
When you’re attacking a large market, copycats are inevitable. But with the right vision, repeatedly out-innovating them becomes possible. As Mohit notes: “If any naysayer says, well, what if a competitor copies you in a year? Your rebuttal is that, well, I have a bigger vision that I’ll iterate on and I’ll get ahead of the game by the time they copy me.”
Sizing a market opportunity is a critical exercise for any founder. Done well, it provides a north star to guide your company’s growth. Done poorly, it risks pouring years of effort into a business that can’t support your aspirations.
Mohit has seen many founders make a basic but all-too-common error: conflating a startup’s TAM with the sum of all current players’ revenues. But unless a startup plans to put every incumbent out of business, this math simply doesn’t add up.
To more accurately size your TAM, Mohit recommends a bottom-up approach based on multiplying two factors: the price you can charge per unit (whether that’s per user, per workload, or another relevant metric), and the total number of units you believe the market can support over a 5-10 year time frame.
To pressure-test these assumptions, look to comparable companies and products for reference points on pricing and adoption. The goal is to base your projections on observable data, while still allowing room for your product’s unique innovations and value prop.
Once you have a realistic TAM estimate, work backward to set market share goals. For enterprise software, capturing 5-10% of a large TAM often equates to market leadership.
PMF is often defined in hand-wavy terms, along the lines of “you know it when you see it.” Mohit offers a clear, concrete way to identify it: “Product market fit happens when an average sales guy is able to sell to an average customer without involving people in the headquarters.”
In other words, PMF is about sales repeatability. If closing deals requires your “elite” sales reps or heavy involvement from the executive team, you haven’t yet figured out scalable customer acquisition.
“You need to be able to hire somewhat average salespeople and be able to sell. And on the other side, your customers also can’t be too elite. Elite customers might understand your product, but is an average customer able to understand it?”
Reaching this level of repeatability takes time. Mohit pegs the threshold at somewhere between 100 and 500 customers for a typical enterprise startup. The exact revenue milestone will vary based on price point, but the core idea is that you need enough customer learnings and at-bats to develop a playbook that works consistently.
As a founder, recognizing PMF is critical because it fundamentally changes how your startup operates. Before PMF, the focus is on conserving cash and refining your product. But after PMF, the imperative is to scale quickly to capture the demand you’ve identified before competitors do.
The risk of moving too slowly is that rivals will seize the opportunity. But the risk of moving too quickly is just as real. Hiring a bunch of sales reps before you’ve found PMF is a sure-fire way to burn cash and demoralize your team.
To avoid these traps, track leading metrics (sales cycle length, win rate, etc.) and listen to anecdotal signals from your sales team and customers. Above all, be honest about where you really are.
Trying to win on small, marginal improvements is a tough road for enterprise startups. As Mohit cautions: “If the pain you’re solving isn’t significantly bigger than the pain the customer is going to go through to change, the customer is not going to do it.”
Deal cycles get longer as buyers nitpick feature differences. Sales teams struggle to establish urgency. Moreover, any success you eke out will likely be short lived. Enterprise incumbents are slow to disrupt themselves but quick to co-opt and crush external threats. If you manage to win a few deals based on an incremental edge, expect a swift response from your competition.
While deep-pocketed competitors can rapidly close small capability gaps, what they can’t do is match a step-function change in customer value.
Cohesity, for example, didn’t set out to merely improve backup and recovery. It sought to radically simplify data management by consolidating all secondary data on one platform. As Mohit explains: “Your backup SLAs fail all the time. Backups today don’t scale. They’re a pain to manage. We’ll give you a simple distributed platform. As it scales, it cuts down your cost tremendously. And your SLAs are going to be 99%+.”
By rethinking data management from the ground up, Cohesity delivered 10x value, not 10% savings.
Of course, even the most innovative products rarely start as end-to-end solutions. The key is to have a bold vision and a concrete roadmap to deliver it over time. Nutanix started by solving for VDI, then expanded to cover databases, unstructured data, and more. Cohesity launched with backup, then added archives, files, objects, and dev/test. The MVPs were focused, but the visions were expansive.
This expansiveness is critical for long-term defensibility. Incumbents may be able to claw back one use case, but they’ll struggle to keep up with a disruptive platform that’s rapidly extending to new ones. As Mohit puts it: “By the time they catch up, you have more on the truck, and eventually they just give up.”
Building a high-performing team is an ever-evolving puzzle. What works for a scrappy seed-stage startup often breaks at Series B scale. To navigate these transitions, founders need a structured, disciplined approach to recruiting, developing, and managing talent.
It starts with hiring. Many founders rely on loose interviews and gut feelings: a recipe for inconsistency and bias. Mohit advocates a competency-based approach with clear scorecards for each stage: resume screening, interviews, and reference checks.
This systematic approach brings rigor and discipline to an otherwise subjective process. By defining explicit criteria upfront and assigning focus areas to each interviewer, you get a more complete, objective assessment of each candidate.
This scorecard mentality extends to reference checks too. Generic questions like “is this person good?” are extremely low signal. Instead, Mohit suggests specific, probing questions like:
“On a scale of one to ten, would you hire this person again? If they say eight, I would say, okay, why is it not a nine and why is it not a seven? Give me some examples of why you think it should not be a nine. That brings out the problems. Another question could be, ‘Did high performers at your organization respect this person? Would great people follow this person?’”
Calibrating references on a numeric scale surfaces far more insight than broad qualitative questions. It also allows references to more easily offer negative feedback by masking it as faint praise. Remember, the 1-10 scale is effectively a 6-10 scale: anything lower than 6 is a failing grade.
Backchannel and blind references are also essential for getting the full picture.
Mohit has helped drive multiple paradigm shifts in enterprise computing, from the early days of Google to the rise of hyperconverged infrastructure. But even for someone with his track record, the current moment feels unique:
“Given the kind and magnitude of disruption that generative AI can bring, pretty much any area can be disrupted. Take any big company that is doing quite well, but that’s not using generative AI—it can possibly be disrupted.”
Too many enterprise software companies, Mohit warns, are still treating AI as a novelty or a nice-to-have. They sprinkle it onto existing products as a marketing gimmick without fundamentally rethinking their core value prop. In Mohit’s view, this is a recipe for disruption:
“If someone finds a problem but they try to solve it without thinking about AI or generative AI, they’re really missing out. Because once you’ve made the world aware of this problem, and you allow someone else to solve it in a way that’s more next-gen, somebody’s going to do it and your solution will be toast.”
Rather than try to beat incumbents at their own game, AI-native startups can harness this technology to rewrite the rules—creating new categories, serving new markets, and delivering new kinds of value that were previously not possible.
It’s easy for technical founders to focus solely on product innovation. For many, creating cutting-edge technology is what draws them to entrepreneurship in the first place. But, as Mohit argues, GTM innovation is just as, if not more, important.
He credits this insight to a Cohesity board member:
“He said something very illuminating once. He’s like, you can talk about all sorts of innovation, but there’s one innovation that trumps every other kind of innovation, and that’s GTM innovation. So unless you understand GTM, and most technical founders don’t, somebody will build an inferior product but a superior go-to-market, and they’ll trounce you.”
Mohit’s point is that in the enterprise world, sales and distribution are often the biggest hurdles to success. You can have the best product in the world, but your business will flounder unless you can get it in front of the right customers and convince them to buy.
Just as product innovation is about finding novel solutions to technical challenges, GTM innovation is about finding novel ways to identify, engage, and close customers. It means developing a repeatable playbook, then constantly iterating on and optimizing it.
Mohit advises technical founders to approach GTM with the same rigor and discipline that they bring to product development. Learn from experts (he recommends fellow B2BaCEO guest Frank Slootman’s Amp It Up, along with The Qualified Sales Leader, Working Backwards, and Leadership and Self-Deception), seek out mentors, and get hands-on experience selling to real customers. Over time, you’ll start to develop the instincts and pattern recognition that allow you to distinguish exceptional GTM leaders from merely good ones.
In closing, I asked Mohit what advice he would impart to his younger self at the start of his enterpreneurial journey.
His response was twofold. First, be humble and recognize that mastery of technology does not make you a master of the many other skills needed to build a thriving startup. Second, proactively seek out knowledge and mentorship in areas like hiring, performance management, and sales.
In his own words: “The first advice I would give to myself is to be humble. You actually don’t know a lot beyond technology. You have to learn a lot, and you need to learn how to hire business people. You need to learn how to build high-performance processes in the company. […] The second thing I would say to myself is, I need to start learning about sales, especially the science behind sales. How do you inspect salespeople? How do you inspect GTM and whether it’s going well or not?”
The importance of continuous learning and growth is a recurring theme on the B2BaCEO podcast, and it’s a trait I observe in the most successful technical founders I work with. These founders are polymaths: not just 10x engineers, but high-slope students of every aspect of company-building.
Listen in on our full conversation here.
Published on June 14, 2024
Written By Ashu Garg