Must-Ask Questions When Joining a Startup in 2026

Episode Transcript

[00:00:00] So one time I joined a startup, and it became glaringly clear that they weren't going to be successful. When it came time to make budget cuts, their product was the first thing people cut. It was brutal. Now, if I had dug more into that question around why customers churn when they do, I would have had better context to make a decision around joining and wouldn't have found myself looking for a new job three months later, which is what happened.

[00:00:27] Hey, I'm Alicia, and you're listening to Non-Founder Crew, your insider guide to surviving and succeeding in tech startups.

[00:00:37] So, do you have any questions for me? You take a breath, you pause, and you either land with, "Yeah, what's the culture like?" Or worse, "No, I think you've answered everything, uh, I was thinking about." That is the moment when you have so much opportunity to ask the questions that really will help you better understand if the startup you're considering joining is worth betting on and has a chance of making it.

[00:01:02] Or if you're about to sign up for a startup that's going to leave you needing or wanting to find a new gig again soon. So, whether it's your first time on the inside or you're an OG who feels like maybe you should know more, or maybe you're trying to land your first startup gig, I've got you covered with the questions you need to be asking when you're assessing a tech startup, especially if the company has the word AI anywhere in its pitch.

[00:01:28] Because the questions you need to ask have completely changed, and most people are walking into these interviews are still using a playbook from two thousand and nineteen. Or worse, no playbook at all. So sit with this for a second. When you join a startup, you're not just taking a job, you're making an investment.

[00:01:46] You're investing your time, your opportunity costs, meaning the other things you could have been doing at that time. And very often, real cash money in the form of strike prices you'll pay later to actually own your equity. Founders on hiring teams have the upper hand here. They get to do due diligence on you for weeks.

[00:02:05] So they get reference calls, take-home projects, panel interviews, the works. You get, what? An hour, maybe two. And most of the questions in those interviews are being asked by them, not by you It doesn't have to be that way. And in this episode, I'm going to show you exactly what to ask and how, so you walk out of that interview knowing what you're actually about to sign up for, more than the benefits package and what they stock in the fridge.

[00:02:33] And here's the other big part of this. The questions you should be asking have changed. The framework that worked for evaluating a traditional SaaS company five years ago will give you misleading answers at an AI company today. So we're covering both, the timeless ones every startup employee should ask and the new ones you need if the company touches AI in any way, which in two thousand twenty-six is basically all of them.

[00:02:58] One quick housekeeping note here. So every single question I'm covering today is in a Google sheet I put together for you. SaaS questions, AI questions, equity questions, all of it. I'll link to it all in the show notes. Please steal it. Use it. Take it to your next interview. No need to scribble down while you listen today.

[00:03:15] And a quick disclaimer before we go further. I am not your lawyer or your financial advisor. When we get into the equity stuff, treat what I'm saying as a starting point, not as legal advice. All right. So we got three buckets: business health questions every startup should answer, AI-specific follow-ups, and the equity questions I want you to ask the moment an offer hits your inbox.

[00:03:37] So before we get into the questions themselves, I wanna talk about why people don't ask them. Because the questions themselves are not complicated. You could find most of them on a blog post somewhere. The hard part isn't knowing what to ask. The hard part is actually asking, and if necessary, digging in a bit.

[00:03:56] When I think back to the interview process at a few different startups, before I really knew what I was asking or doing, I didn't have a clue how to determine if the company was successful. It was largely outward perception like, oh, they raised a ton of money from a hot VC, which is a terrible indicator, by the way.

[00:04:11] The influence or opinions of others, so hearing things like, "Oh, they're doing really interesting things," or maybe they had a few execs or founders who had been successful in the past, and the assumption was that because they had done it before, that they would be successful again. I now have known a number of founders who that track record does not hold up.

[00:04:31] And then there were times that I didn't ask every single one of these questions or that I didn't dig in deeper to really understand the answers that were vague or unclear. So one time I joined a startup, and it became glaringly clear that they weren't going to be successful in large part because while customers really liked the team and had great things to say about them, the product itself was a nice-to-have, not a necessity for customers.

[00:04:53] And when it came time to make budget cuts, their product was the first thing people cut. It was brutal. Now, if I had dug more into that question around why customers churn when they do, I would have had better context to make a decision around joining and wouldn't have found myself looking for a new job three months later, which is what happened.

[00:05:13] Now, this is the pattern I see over and over. Smart people skip asking these questions because asking feels rude or because the energy in the room makes it feel like you're voting against the company before you've even joined. There's this implicit pressure that says, "If you really believed in the mission, you wouldn't need to ask about churn."

[00:05:34] Or maybe you don't feel totally confident in even understanding the questions at all. It happens. But you need to reframe that completely. The founders worth working for want you to ask these questions. They take it as a sign that you think like an owner. The ones that get defensive or vague or condescending when you ask, that's a data point. That tells you exactly what working there is going to feel like when something goes wrong, and something's gonna go wrong.

[00:06:02] It's a startup. So with that in mind, let's get into it. These are the questions you ask during the interview process, ideally with the hiring manager or the founder, not the recruiter. Recruiters often don't have the numbers, and that's totally fine, but you wanna hear these answers from someone with that level of context.

[00:06:20] I'll walk you through each one of them, what it means, what a good answer sounds like, what a red flag sounds like. And if you start your interview process directly with the founder, be confident that you should ask these questions up front. Oftentimes, I found that when I came out of the gate with these questions before we really dug into a role or the business, it set the tone and gave a strong impression about the way I was thinking about the business, how serious I was taking the conversation, and that at a wider level, I understood how the business should run.

[00:06:49] All right, first question: What's your ARR today, and what's the target for the end of the year? So ARR means the annual recurring revenue, the predictable subscription-based revenue annualized out. So it's the gap between today and the end-of-the-year target that tells you how aggressive the plan is. So a company at four million ARR looking to get to six seems ambitious, but it's probably possible.

[00:07:14] A company at four million that's looking to be twenty million by the end of the year is a fantasy. You're being recruited to fulfill someone else's fantasy. All right, next question: Walk me through year-over-year growth inception. So this is a trajectory question, not the snapshot. Early-stage startups should be growing fast, often doubling or tripling.

[00:07:34] Later stage, slower but consistent, so watch for flat years buried inside the story. If they say, "We grew one million to five million in four years," that's an average. So it could mean one million, one million, one million, five, which is a very different company than one, two, three, five. Third question, and this is the most important one: What's your burn rate, your runway, and how much money is in the bank right now?

[00:07:58] So burn rate is how much money they lose per month on things like operational costs, advertising, paying staff, rent. Runway is how many months until the money runs out. So if they have six million in the bank and they burn half a million dollars a month, that's twelve months of runway. Under twelve months without a clear plan to raise more money is a yellow flag.

[00:08:18] If they have under six months and no plan, that is a red one. So I've interviewed with a few companies that were in the midst of raising their next round. They were quite transparent, which they should be, and I was also pretty clear I'd wanna wait until the funding was in, in the bank before jumping ship just to de-risk the situation.

[00:08:35] While folks have every intention of going out and raising the next round, actually securing it is a different thing. This is a moment where you can go, "So how's the fundraising going so far?" It's a fair ask. Next question. What's your churn rate, your ACV, and your CAC? So churn is the percentage of customers leaving per month or per year.

[00:08:53] ACV is the average contract value, so what a typical customer pays, and CAC is the customer acquisition cost, so what it costs to get a customer to join. The relationship between these three numbers tells you whether the business actually works. So high CAC plus low ACV plus high churn equals a leaky bucket being filled with expensive water.

[00:09:14] That company is going to struggle no matter how good the product is. All right, so one of my favorites: Why do customers churn? This question is great because it's a test of whether they actually talk to their customers at all. So a vague answer like, "Oh, you know, sometimes it's just not a fit," means they really haven't done the work to connect with customers to understand what's going on.

[00:09:32] If they come back and they're like, "There's three reasons why customers churn, and these two we're attacking with this initiative over here," that shows you that they're already talking to customers, they're thinking about how they're gonna solve these issues, and they're facing them head-on. What's the biggest challenge your business is facing today?

[00:09:48] This is an honesty test. Every business has a biggest challenge. If a founder can't or won't name theirs, one of two things is true: either they don't have enough self-awareness to see it, or they're performing for you. Both are bad things. The best founders I've worked with answer this question immediately with specificity because they think about it all day, every day.

[00:10:06] Okay, those are the six questions. Quick reminder, all of these are in a sheet with the definitions and what to listen for. Don't try to memorize. Now, here's the part nobody is talking about. Those six questions are timeless. They apply to almost every startup. But if the company is anywhere in AI, you need three more, because the answers to those traditional questions can be deeply misleading in the category.

[00:10:29] Here's why. The traditional SaaS playbook was built on a specific kind of business. So software has a near zero marginal cost. Once you build it, every additional customer costs almost nothing to serve. That's why classic SaaS gross margins are seventy to ninety percent. It's why the metrics work the way they do and why people really like to get into the SaaS game.

[00:10:52] Those are great margins. AI companies are different. They break that model. Every time a user makes a request, the company is paying for compute, either to a model provider like Anthropic or OpenAI, or for their own infrastructure if they're running open source models. That is a real cost that scales with usage and it's variable, which means that metrics you'd use to evaluate a traditional SaaS company will give you answers that sound great, but they mean something completely different.

[00:11:22] So here are the three questions to close that gap. The first is: What are your gross margins, and what's inference cost as a percentage of revenue? Before I dive in, if you're listening to this and you're like, "Alicia, what's inference?" Inference cost is the ongoing competing expense of running a trained AI model to make answers, predictions, or outputs.

[00:11:45] So now that we have that covered, hear me out because I have numbers for you. I love a little data. Iconic Capital's State of AI report, which I'll link to in the show notes from this past January, found that inference averages 23% of revenue at scaling stage AI B2B companies Bessemer Venture Partners benchmarks AI gross margins at fifty to sixty percent against seventy to ninety percent for mature SaaS.

[00:12:11] And the fastest-growing AI startups, the ones rocketing to hundreds of millions of dollars in ARR, some of them are averaging only twenty-five percent gross margins. That is a fundamentally different business than what you signed up for if you thought you were joining a SaaS company. A good answer sounds like this: "We're at fifty-five percent margins today, but we have a clear path to seventy.

[00:12:32] Plus, as we migrate workloads to cheaper models, optimize prompts, and move some inference in-house, things get better." That is an answer from someone who has thought about it. A red flag answer sounds like, "Oh, compute is basically a rounding error." It's never a rounding error. Anyone who tells you that either doesn't understand their own cost structure or is hoping that you don't and you won't dig in.

[00:12:55] The next question you really should be asking is, what's your gross retention? Not your net retention, your gross. So this one's important. Net revenue retention is the headline number here companies love to share. It includes expansion revenue from existing customers, so it can look great even when you're losing a lot of accounts.

[00:13:14] Gross retention strips that out, so it just tells you of the customers you had a year ago, how many are still there. So here's what's happening right now. AI native companies have a median gross revenue retention of around forty percent. For comparison, healthy B2B SaaS is in the eighties or nineties. The picture gets worse at the low end.

[00:13:32] Budget AI tools, the ones priced under fifty dollars a month, retain just twenty-three percent of users. Premium tools, so those over two fifty a month, do better, around seventy percent. But the forty percent median is kind of the headline here. And there's a name for it, the AI tourist effect. So people sign up because they heard about it on a podcast, they try it once or twice, and then they never come back.

[00:13:54] One VC, her name is Casey Young at Young Venture Partners, is calling it the gross retention apocalypse, and she's probably not wrong. So when you ask about retention at an AI company, you specifically want gross, not net. If they only wanna talk about net or if they pivot to ARR growth, that tells you that they're hiding something.

[00:14:13] So the third question you wanna ask is, what's your moat and how do you think about model commoditization? So this is an existential one. If the company's entire value proposition is we built a nice interface on top of someone else's model, that is a really hard business to defend. The model providers will catch up.

[00:14:31] New models come out every quarter that do what your product does, sometimes better, sometimes cheaper. Real AI moats look like proprietary data the company has accumulated. Distribution where they've embedded in a workflow customers can't easily leave. Things like vertical expertise where they know healthcare or legal or construction in a way a generalist tool can't replicate or genuine product depth that took years to build and would take years to copy.

[00:14:56] A good answer is specific and defensible. A red flag answer is, "Our model is better." Every AI company says their model is better. These three questions matter because they tell you whether the company you are about to invest the next four years of your life in will still exist when your equity vests.

[00:15:16] Which is the perfect segue. Let's talk about that equity. So equity is the part of the compensation that almost everyone gets wrong, including me for years. Let's slow down for a moment, because the difference between understanding your equity and not understanding it can be hundreds of thousands of dollars, sometimes more.

[00:15:34] So for this episode, I wanna give you the short version on equity. We can do a deep dive later down the road. The questions that you ask the moment an offer hits your inbox to truly understand what the offer represents and to understand your full package. So first you'll wanna know what the strike price is.

[00:15:53] So that is the price per share you'll pay to exercise your options. You'll wanna know what the current fair market value of a share is. The spread between the strike price and the FMV is your paper value today, so what it would be worth. You wanna know what percentage of the company does the offer represent.

[00:16:09] So founders or recruiters will pitch you a big-sounding number. "We're gonna offer you fifty thousand options." Cool. Fifty thousand of what? Always convert to a percentage. You'll wanna know are these ISOs or NSOs, something else? They get taxed totally differently. What are the vesting terms? Standard is four years with a one-year cliff.

[00:16:27] Anything weirder deserves a follow-up question and a lot of digging. Ask about acceleration. What happens to your invested options if the company gets acquired? And the question that exposes everything: How does the company value this equity offer? This is the one that separates the founders who understand their cap table from the ones who don't.

[00:16:46] So if they quote you a number based on the last preferred round valuation, they're either misleading you or they don't know better. Your common stock is not worth what preferred is. So preferred has liquidation preferences. They get paid first in an exit. Common gets paid out whatever is left. Sometimes it's a lot, sometimes it's nothing.

[00:17:05] And here's where the SaaS versus AI angle comes back. A lot of AI companies right now are raising at aggressive valuations, so assume aggressive growth. If they hit those numbers, the equity is incredible. If they don't, your common stock could end up underwater, meaning your strike price is higher than what the shares are actually worth at exit.

[00:17:23] So that how do you value the offer question is even more important at AI companies, not less. I wanna close out this episode with something that has been implicit in everything I've said, but let me make it explicit. You are not just gathering information when you ask these questions. You are auditing the relationship and the business.

[00:17:44] How a founder or a leader responds to these questions matters more than the answers themselves sometimes, and here's why. You can get good signals. They pull up the numbers. They walk you through the math. They say, "I don't know off the top of my head, but I can get back to you by the end of the week with that."

[00:17:59] They treat your questions like the questions of a future colleague who is about to help them run this thing and someone who is taking the conversation seriously You can also get bad signals. Defensiveness, vagueness. "We don't really share that internally." Making you feel small for asking, implying you're not committed enough.

[00:18:18] Pay attention to that response because if this is how they treat you when you have leverage, which you do, when they are trying to recruit you, imagine how they will treat you when you don't have that leverage. All right, so we're closing out today's episode with the Survive and Thrive toolkit, the segment where I share a resource, product, or habit that I think will help you navigate life at a tech startup.

[00:18:39] Today's toolkit is a habit, 30 minutes of pre-interview due diligence. That's it, 30 minutes. Before any startup interview where you are seriously considering the role, here's what I want you to do. Pull up the company on Crunchbase or PitchBook. Look at every round they've raised. Note the lead investor.

[00:18:58] Look at the time between rounds. If their last round was 18 months ago and they're not profitable, do the runway math before you walk in the door. Search the founder's name plus podcast or interview. Listen to what they say when they're not selling. Often, you will learn more about whether you wanna work for someone in 20 minutes of listening to a podcast than in two rounds of interviews. And one more thing.

[00:19:19] Search the company name plus Glassdoor and read the one-star reviews. Not the five-star ones, the one stars. The pattern in the bad review tells you what is actually broken inside the company. The five stars are often written by people in HR or new hires who have been asked to post before they really get into the weeds, and they want job security.

[00:19:40] That habit, those 30 minutes before any interview, has saved me from more bad decisions than any single question I have asked in the room while I was interviewing. So here's the truth about joining a startup. The hardest part is not the work. The work is hard. You can handle hard work. The hardest part is making a high-stakes decision with incomplete information on a compressed timeline while everyone in the room is smiling at you and telling you you're going to be incredible there.

[00:20:06] These questions don't guarantee a good outcome. Startups fail even when the numbers look great. Markets shift. Founders make mistakes. AI commoditizes faster than anyone would expect. There is no version of this where you ask the right questions and the risk goes to zero. But what these questions do is shift the odds, and more importantly, they shift the dynamic.

[00:20:28] You walk in as someone who is evaluating them, not just someone hoping to be chosen, and that changes everything about how the next one, two, or even four years might feel. One last time, the sheet with every question we covered today, the SaaS ones, the AI ones, the equity ones, all of it with what each question means, what to listen for, it is in the show notes.

[00:20:51] Copy it, save it, send it to a friend who is interviewing right now. That is how this stuff gets better for everyone. And hey, listen, if you've got a story about an interview that went sideways or an offer that turned out differently than you expected, I wanna hear it. DMs are open. If this episode helped you, the most helpful thing you can do is share it with one person.

[00:21:10] That is how the show grows. Oh, and be sure to subscribe. I'm Alicia. This is Non-Founder Crew. See you next time. Thank you for listening to Non-Founder Crew. If you want more insights, learnings, and stories from the trenches, sign up for my newsletter by going to www.nonfoundercrew.com. And hey, listen, if you know a friend who could stand to hear this advice, send it to them.

[00:21:32] See you next time.