Matt Hudson became CFO of Grammarly – the popular writing assistant tool – after the company acquired Coda (a productivity tool)- of which he was a founding member. Now as CFO at Grammarly he is overseeing rapid change at the company which has over 40 million daily active users and $700m in revenue (and a 50 person finance team). Finance is helping pioneer a company vision of AI redefining every business application and workflow, “reinventing productivity”. To this end, in May 2025, Grammarly raised $1 billion in non-dilutive financing from General Catalyst to fund sales and marketing costs and strategic acquisitions. Since then, Grammarly acquired email startup Superhuman (July 2025). You sense more is coming.
In this episode
- Grammarly $1billion non-diluted growth investment explained
- Being a founding member of Coda (acquired by Grammarly)
- How my product expertise plugs into finance
- Joining YouTube when it was around $140million in revenue to $3billion by the time I left ( $36 billion today)
- Will CFOs see an explosion of costs because of AI
- FP&A set up at Grammarly and biggest KPIs
- Bonding over a love of Chili’s
Connect with Matt: https://www.linkedin.com/in/matthudson/
Glenn Hopper:
If you would like to earn CPE credit for listening to the show, visit earmark cpe.com/fpe. Download the app, take a short quiz, and get your CPE certificate. Finally, if you enjoy listening to FPA today, please go to your podcast platform of choice. Click the subscribe button and leave a rating in review of the show. And now onto the show from Data Rails. This is fp NA today. Welcome to fp NA today, I’m your host Glenn Hopper, and I’m really excited about today’s guest. Matt Hudson is the new Chief Financial Officer at Grammarly, where he brings a rare combination of expertise in finance, product management, data science, and growth strategy. Before stepping into the CFO role, Matt spent more than a decade at Coda as an early team member, helping grow the company from beta into a million dollar business, a multimillion dollar business that is over his tenure. He led across product go to market and finance, ultimately serving as CFO and playing a central role in Coda’s acquisition by Grammarly. Earlier in his career, Matt held roles at Google and YouTube, including serving as chief of staff to YouTube’s head of product and engineering. He holds degrees in finance and data science from the University of Michigan and has built a reputation as a data native finance leader, someone who blends analytics, experimentation, and strategic thinking to scale both teams and systems. Matt, welcome to the show.
Matt Hudson:
Thank you, Glen. Looking forward to being here. This is fun.
Glenn Hopper:
Yeah, man. Uh, it’s been a while since you and I first talked and I’ve really been looking forward to this episode, and I know you are super busy right now, so I really appreciate you taking the time to come on.
Matt Hudson:
Of course, of course.
Glenn Hopper:
I am very excited about having you on the show because I think you and I are kindred spirits in the way we approach the office of the CFO. And I think that looking at your background, you know, it’s not your traditional come in through, uh, public, you know, big four accounting audit coming up through the, the CPA ranks, and it’s, it’s much more where you’re seeing, uh, a lot of new CFOs come in today. And you, so your career, you, uh, span product, data science and finance. And I’m wondering in all that, what led you down that CFO path and that transition? Like how did your early experience influence how you, uh, how you lead in finance today because it’s a, it’s a different approach.
Matt Hudson:
Yeah, it’s a great question. I certainly have a pretty uncommon background for folks that are in this role today. Um, I think for starters, this was never a preordained path for me. I think there’s a, there’s a question I often give to folks coming outta school or when I’m interviewing, which is, what do you wanna be when you grow up? And I always preface it with, I’ve never known that answer, so it’s okay not to have one. I bounced around my whole career. I think the, the most common theme is I like being an operator. I like building businesses. Even back in school, I initially thought I’d go into astrophysics or engineering and just didn’t find something that was that exciting. And finance coming outta business school and undergrad felt like the most technical thing you did in business, um, which is kind of why I gravitated there.
Uh, but I didn’t even go the, the traditional finance, finance route coming out of undergrad business, not going into banking, not going into consulting, and instead buying a business, a furnishing manufacturing company. And doing that for a few years. Um, and then picking, uh, Google had started a rotation program, uh, that was kind of modeled after the General Electric, uh, financial management program. And I got to bounce around finance there and try out accounting and controls treasury, fp and a. Um, and it was there where I really kind of cut my cloth and found a team that was very quantitative in nature. Uh, today you would call ’em a data science team. Back then we just called it a quant team, and it was all mathematicians, PhDs, um, who were responsible for doing predictive modeling for revenue across all the Google product lines. And I had a lot of fun on that group, and that’s kinda what led me down a product management path and then the startup path. And then once you’re in a startup, you can wear whatever hat you want. And I did product management. I did go to market, uh, before kind of going back to my analytical roots at the end, uh, before the, the Grammarly transaction
Glenn Hopper:
With you at Google. My, my first finance role, I actually started in marketing. I was in telecom, and I ended up moving over in first job out after business school in, in marketing. I ended up moving over to finance after a couple of years in telecom. I’ve never worked with a team that had that many nerds, that many smart people that were thinking, and I really, I was in finance, but I kept gravitating more to the engineer. And, you know, in telecom it was a lot of, um, network, uh, routing and, and all the, everything that went through that. But these guys were also really good at Excel. So I learned early on, you know, just all these excel tricks and we would sort of compete with each other and everything. And it really influenced my, my approach to finance. And it sounds like at Google you had a, a similar experience and being around that, that level of, uh, math nerds and, and data folks can really, uh, settle in early. Yeah,
Matt Hudson:
And I, I’d say one other thing that’s very unique about finance. So I’ve spent years in product management in running, go to market teams, et cetera. And the, the really unique part about finance is within a company, it’s the only one that has the vantage point into everything, and you kind of have a reason to stick your nose anywhere. Um, so when it comes to building businesses and having a data orientation to it, it’s the one function where it’s always appropriate to go look into marketing, go look into sales, go look into engineering. Like you can kind of go anywhere with it, which is nice.
Glenn Hopper:
Yeah. And you had that mindset. You leave Google, was it, did you go straight in Dakota after Google?
Matt Hudson:
Yeah, so it, I was within the Google Finance team for a while. Uh, spent three and a half years at YouTube, which was owned by Google, but was, um, somewhat run as like a, not fully independent subsidiary, but a different team, different location, different headquarters. So it was separated enough and then went straight to, we didn’t call it Coda, then, we called it Krypton. Um, but yeah, we had, we had raised a series A and were in stealth for the first four years or so, but I was there on day one with, uh, with the founding team.
Glenn Hopper:
Wow. So you there, day one, scale it up into a widely adopted platform. So now you’ve got big company, huge team experience around all the, the math PhDs, and then you’re in this startup where you’re getting to wear a lot of hats. What lessons from that rise at Coda growing with the company? Are there lessons that you picked up then that you carry today and that you’re even now seeing you’re applying them at Grammarly?
Matt Hudson:
Yeah, I mean, this is probably an hour’s worth of conversation. Uh, I’ll, I’ll try to keep it brief. <laugh>. I’d say it’s probably worth commenting a little bit on context for both, uh, Coda and Grammarly, uh, and just the acquisition itself. So Coda, for folks that are unfamiliar is a freemium SaaS business used by individuals, startups, large enterprises to streamline their workflows. Uh, it’s an all-in-one document platform. Uh, so think of it as a modern take on Microsoft office, the whole productivity suite blending together doc spreadsheets, databases, apps into one flexible surface. You end up seeing teams start from meeting notes going all the way up to custom tools without writing any code, but using a common set of building blocks, very spreadsheet like in terms of the formula language and how you can build pretty amazing things in it. Grammarly probably is better known, but for folks that don’t know, Grammarly’s, also a freemium SaaS business.
It’s claim to fame is being one of the early NLP or today you’d call it ai, uh, writing assistance. That helps a lot of people communicate clearly and effectively across all sorts of different apps. Uh, so you install a client on your device like a Chrome extension or a Mac app or a Windows client, and you get help anywhere you write, Grammarly is used by a lot of individuals and it’s built a really impressive business over seven, 700 million in revenue. So in terms of scale between Coda, Grammarly, YouTube, Grammarly for me, re reminds me a lot of the YouTube days. Um, so I was there when it was around. I think I joined it when it was around 140 million, uh, in revenue. And it scaled up to about 3 billion by the time I left. It was kind of through that really big growth phase. To your question on what learnings, learnings come across it, I’d say Coda, one of the biggest pieces was just thinking about SaaS businesses generally, YouTube and Google were fully ads businesses, and there’s a lot that you learn around product telemetry and measuring a subscription business from first principles like cohorting subscriptions or thinking about a RR and NRR.
And I’d say that Grammarly has this nice intersection of, it’s a lot of those same principles and mechanics, but at YouTube level web scale, uh, ’cause of Grammarly’s business just being so large. Um, so I think very early on, some of the first things we’re doing are trying to stand up a lot of the forums and the stats meetings or metrics meetings that we had patterns of at YouTube, uh, but doing it on the Grammarly side
Glenn Hopper:
While we’re on the business. So Grammarly just secured a a billion dollar non-dilutive growth investment. I think for, first off, I guess for our listeners who may not be familiar with with what this means, can you explain what this non-dilutive investment is, who the investor was and, and actually, you know, seeing that deal, I’m wondering how common is that and is it becoming more common now? Just kind of a little bit of background on, on that investment and, and the, and the type of investment that
Matt Hudson:
It’s Yeah, certainly. So, uh, as most SaaS businesses go, you end up having the very, a very similar cash flow pattern where there’s a big investment upfront and then it pays back a little bit over time. Um, so the, the firm that led this was General Catalyst. Uh, the fund is called CVF for the customer value fund, and it’s a financing vehicle that co-invest in a company’s sales and marketing spend upfront up to a certain threshold. And it gets paid back over time as a profit share of each respective cohort that they fund the value add for it. And what makes it super appealing for us is really simple in the sense that it improves the cash flow dynamics of a SaaS business. It maybe to give a, a very silly, uh, comparison, um, that that lofting business that I ran when I was in college, um, had a negative cash conversion conversion cycle, which was lovely.
We, we’d get ca cash from customers before we had to pay suppliers. SaaS businesses don’t really have that dynamic. You end up spending a bunch upfront and then earning it back over time. This flips that a little bit. This allows you to have a lot less capital intensity upfront and maybe to pick a, a very common way that finance folks will understand. If you think of this whole equation as a net present value or just kind of cash flow equation and think about IRR, this dramatically improves our IRR for every investment in new cohorts. ’cause where you don’t have to put as much cash up front and we still get most of the return over time.
Glenn Hopper:
And you keep the equity
Matt Hudson:
That’s right. You keep the equity and like compared to most traditional places, you’re gonna go acquire, uh, capital from like a banker or whatnot. There’s no anchor in the business, there’s no lein and you’re utilizing an asset that traditional banks don’t look at. They don’t look at customer cohorts with predictable attributes as an asset that you can underwrite against. Um, CVF allows you to do that.
Glenn Hopper:
Yeah.
Matt Hudson:
To your question on how common it is, I think it’s a somewhat newer product in market. I wanna say that they’re to the tunes of of 50 or so, uh, deployments. I might even be one of the few CFOs that’s used it twice because, ’cause we used it at, we used it at Coda two.
Glenn Hopper:
Did you consider, was it even on the table to do a traditional round where there an equity round instead of that? Or was this, you were gonna do this or nothing or
Matt Hudson:
I mean,
Glenn Hopper:
Alternative financing or,
Matt Hudson:
I got advice from someone, uh, many years back when we were first looking at this at Coda where equity is really useful to fund your r and d. Like it’s your high, it’s your most expensive cost of capital. It’s something that you want a lot of leverage on. You wanna see big returns on your sales and marketing expense, spending equity dollars to go fund that. It’s, it’s kind of like an expensive way to go do it. So CVFI feel like offers a very good sales and marketing option to do it. It’s not a huge commit upfront. It gives us lots of flexibility in how much to draw and when and the timing of it. So it’s, it’s very helpful that way. Uh, and it’s much, much cheaper than using equity.
Glenn Hopper:
And you mentioned the, the cost structures for a, for a SaaS business, but I think also now, well Grammarly is AI driven, um, and AI driven products have their own cost structures, model inference costs and GP usage and all that. How are you adjusting your margin models and planning frameworks to reflect these kind of changes?
Matt Hudson:
I was, I was at a, uh, a conference the other month and it was a common question that came up of people being worried, worried about the cogs and worried about like an explosion in r and d because of all this stuff. Maybe two different perspectives. I think one is I have a philosophy or a, a perspective that this is all gonna come down over time. It’s not gonna quite, it’s not gonna be as big of a crunch as we’re playing it out to be right now. I think we’ll find optimizations in ways that you offload certain aspects of the job to something that’s much cheaper. I don’t think I’ve seen, you know, huge, huge explosions yet in cost. We just haven’t, we haven’t incurred that or, or seen something like that yet. The second is from like a ROI perspective. Um, my guess is this is all gonna end up just getting fold in, folded into the way that we traditionally measure and monitor ROI. Um,
Glenn Hopper:
Yeah,
Matt Hudson:
There, there was one CFI was talking to a few months ago, and there’s a question of where is this gonna get categorized, like in the world where you’ve got, you know, agents that are running around and you’ve replaced key workflows within the team with something that’s agentic or whatnot, and like, how do you, how are we gonna capture it? And the analogy I gave was, you’ll end up treating agents as headcount. It’ll be, you’ll allocate them to different lines of business and you’ll have agents in r and d agents in s and m agents in GNA and you’ll just track the cost the same way. So you’ll end up looking at ROI exactly the same way. How much value am I getting from my normal measures against the cost that I’m investing in them? And they’ll all fold into the rest of the common parts of the p and l.
Glenn Hopper:
I feel like this is a, a sci-fi episode of <laugh> of fp and a today. I mean, it’s amazing that that’s where we are, where we’re really having this kind of conversations about agents. And I know I have this whole soapbox I get on about people calling things agents now that aren’t, but I know they are true agents are coming and in the interim there’s agentic workflows and all, all sorts of tools that can, you know, to the end user, they might as well be an agent. They look like one. So it, uh, but it’s the, the, the productivity and efficiency and the idea that we’re going to have, uh, ENT robot partners alongside, I was talking to someone months ago about, used to be hired as an fp and a analyst. You would kind of come with, what you would bring would be essentially all of your models, all of your Excel skills. And so you’re getting hired, but also all your experience and the models you’ve built and talking about a future where when you’re hired somewhere, it’s you and all the agents that you’ve built sort of the way that we’re gonna look at it. So it’s, it’s pretty, pretty amazing times.
Matt Hudson:
And in that world, the idea is that they’re portable, which is an interesting one. I was listening to someone talk, we’re on the topic of, uh, shapes of teams going forward and how do you, ima especially if agents and a lot of the workflows that get replaced end up being the ones that folks that were entry level were doing before. Like how does that end up shaping what teams look like broader instead of a pyramid, it looks like a diamond or it’s, you know, big in the middle versus, uh, big on the bottom. And one of the analogies going back to the point of like, how do you measure ROI and think about cost for agents and how do they, how do you think of ’em? If you think of ’em all as headcount and just count the number of them, you’ll still have a pyramid.
It’s just that the, at the bottom it’ll be a bunch more of these agents that you’re using to deploy on certain tasks or workflows and coming outta school, your job is not gonna be doing those tasks yourself. It’s gonna be managing a set of 15 agents that are doing those tasks and you like immediately move in like, what is prompt engineering other than giving people clear instructions and you know, the context necessary to a complete a job. It sounds a lot like management and it’s gonna be managing agents, not managing, you know, people to start your career.
Glenn Hopper:
Yeah. And that is one of the, that’s one of the conversations that happens over and over is, well, if agents are taking all the entry level jobs, then what are the entry level people are gonna, what are they gonna do? But I think you just nailed it right there. It is. And it, it’s, it’s funny because when we think about prompt engineering, it sounds like something that you would have to have more of a, a CS background, but really it’s almost the liberal arts or, or psychology background even, or just, you know, industrial organizational psychology or, or just basic management. It’s breaking down a task into its component parts and managing those parts of the task. And I think it’s, uh, I don’t know there, I don’t wanna be overly optimistic, but I think there is, um, a a lot more doom and gloom around, uh, AI maybe than is warranted. If you look at every other tech revolution we’ve been through, you know, what, 120 years ago we were all 80% of the US economy was in agriculture and <laugh>. Yeah. And, you know, look where we are now and there’s jobs we couldn’t have imagined. But I think you’re, I think you and I are aligned on that with what, what we kind of see the, uh, future being as agentic powers get stronger and and more capable.
Matt Hudson:
This, this will be the same product or a similar productivity shift of what you saw with, I mean, for finance people in particular, the spreadsheet you went from calculating it all by hand manually to now having it programmatic computers, um, that also be all used to be done by hand, not, um, which was a lot of work. And it, it allows you to go to the higher level strategic stuff.
Glenn Hopper:
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As a data guy, I really want to hear your, your thoughts on this and at the size companies where you are. And we’ve got listeners that are everywhere from SMB all the way up to, you know, the, the biggest enter enterprise companies out there and all at different levels of data maturity. You know, we’ve been talking about digital transformation for 30 something years now, and the idea that, uh, we, you know, I I don’t even know what digital transformation means anymore, but to me, to me it’s, it’s evolving to keep up with the technology that’s out there and, and at the foundation of that is data, but I still talk to businesses of all sizes that don’t have their data house in, in order. And I’m wondering what you’re seeing and what you’re thinking and, and for these agents to work, we have to have that sort of foundation layer of data. And I’m wondering what tips or ad advice or or insights you might have looking at the different companies where you have the amount of data that’s there and for people who maybe aren’t caught up yet, how you would guide them to think about data and, and what they need to do ahead of this sort of, uh, agentic future that we’re headed towards.
Matt Hudson:
Yeah, I mean, I think maybe two or three reactions I’d give. I think one of them is, uh, I would distinguish stuff from, like, when we talk about data, I’m often referring to things that are like heavy analytical workflows or about deriving insights from stuff. Um, versus when I talk about agentic workflows and using agents to do stuff, I think I’m, I’m more relying on a perspective of the world that’s about like human workflows within software. It’s not as much like magic answers to complicated questions. Um, I, I feel like that is part of the equation that’s probably the furthest off being able to, uh, ask a question and get a full faith and fidelity answer back, I think will actually be the long pole in this environment. And so I, I’d separate those a little bit. I think on the data side, you know, the best practices for how teams get set up, uh, I think are no different today than they were 10 years ago when things like DBT and um, thinking about warehouses versus lake houses, thinking about how do you structure things in a star schema using highly performant column io oriented data stores separating storage from processing.
That that all I think is still true and as true today as it was before. Um, I do think in the data realm there has been a shortcoming in, folks end up thinking about a lot about the middle of the stack, like the warehouse, the lake house, where you store stuff, how much door, how much data you can store, et cetera. And then the last mile often stops short. It’s like we get to a dashboard or a report. And what I think that there really needs to be is a lot more innovation in what I’ll call data apps, which are, uh, consumer grade applications for data products. Like things that feel like, maybe to pick a really clear example, every single company has a single pane of glass problem for their customer usage of their product. Like, you wanna look in one spot, everything about this customer, and you want that to be an experience for like a frontline salesperson that is working with them.
And it’s a combination of what’s in the CRM, all my notes about them, how they’re using the product. And as a sales person bouncing around between like a dashboard that’s kind of like in a language I don’t totally understand ’cause it’s a bunch of charts that I’m not like super used to. And then going to my CRM to try to do stuff. None of that is a particularly good product experience. Like if you put a product manager hat on, what you really want is a data product that feels like a way to explore the organization and like click on people, not like refresh the filters and change the select list on a dashboard and hit run. You’d wanna interact with these data products that feel like a first class application. I think there’s a huge gap right now on that side of the stuff on the ag agentic workflow world.
I think where that really is gonna come together is today there’s like, um, I wouldn’t say an a hard fork, but there are at least a couple different camps emerging in the AI space. I think there’s one camp which is very much, I’ll call like the black box world, where it’s like you submit something and get something back. The other side of the spectrum I think is, and this is, I think the philosophy that we’ve got at Grammarly is one where we expect a lot of these AI agents to work where people work and feel like a supplement rather than a replacement. So it’s about kind of similar to like the, what the spreadsheet did, it’s about giving someone a lot more power to go execute on a lot of stuff rather than re removing the human in the loop entirely. Those I think are gonna be totally different, uh, worlds or constructs and we’ll see over the next couple of years how things really play out.
Glenn Hopper:
Yeah, and when I talk to a lot of companies, if they happen to be large enough and they’ve real, are committed to data and they have a, a data science team and they have machine learning engineers, you know, they’re gonna, they’re gonna build tools that they’re gonna use within their company that are, uh, agentic or at least agentic workflows and, and they’re gonna be able to operate with that. But when o other companies, you apparently have fp and a in a lot of cases, <laugh> and, and it’s, it’s a stretch to think about them on their own, building out some big AI plan. And I wonder, I, what I end up telling them is, really focus on your data right now because the tools that you’re already using, uh, your CRM, your ERP whatever, whatever tools are in your finance tech stack are gonna start. They’re gonna, they’re gonna have generative AI worked into it and the the foundation of these tools is gonna be the, the same, there’s still gonna be a thousand tables in your NetSuite instance or whatever, but how you interact with those tables and thinking about going through and, and building your customer reports, it’s, instead it’s gonna be a, a conversational layer.
And we’re seeing in the fp and a space, uh, data rails among them are already starting to integrate the, um, AI genie kind of tools where you can talk to your data and, and all that. And Grammarly, I don’t, are you guys focused right now? Are you building any sort of new interfaces using AI or are you seeing it any of the, any of the tools that you’re using as far as applying AI beyond, beyond your basic, and, and we’ll talk a little bit about machine learning too before I let you go, but any generative AI use cases at, at Grammarly right now?
Matt Hudson:
And in particular, like how we’re using it within the finance team?
Glenn Hopper:
Yeah, yeah.
Matt Hudson:
There, there are certainly a handful of of cases. One, one philosophy I have a strong one on, uh, just in terms of like how do you engage the finance team to the broader company is being heavy dog footers of your own product, uh, meaning that you, you use a lot of what you build, going back to like the dichotomy I was just describing a moment ago of like the data world versus AI and like the agentic flows and the agentic flows often being about workflow rather than being about analytical insights or answers. I think there’s several areas where you end up seeing a lot of, uh, broad-based workflow tools. Um, I think a couple somewhat silly ones that come to mind for me. The first one being anytime you have to go do deep research on a topic, like I think in a traditional fp and a team, you’re getting ready for the board meeting and you wanna look at comps, public market comps.
All right, well that normally would’ve been like two days for someone to go and like copy and paste stuff from a source somewhere. And instead it’s like, oh, let me queue up a de deep research topic to go pull in this stuff with citations. I can go check it and spot check it. It’s not the end of the world if there’s something wrong in it, but it saves me a lot of time to get that first baseline black pen done. The other one that I’ve found really useful in my role in particular, uh, is getting feedback on presentations. Like if you’re gonna do a board deck, I, I heard this from someone a couple of months ago, and what they’ll do is they’ll walk through their board script, they’ll have the enterprise version of chat gpt or whatnot, they’ll load their board deck in it and say, Hey, pretend you’re a board member and ask me questions like, take a look through this and grill me.
And then it’ll, it might find like parts you’re missing or things that you’re doing and ask it questions that you can now get ahead of and do, do dry runs. I think for the finance team particular in particular, I still feel like there’s, there is a gap in terms of a lot of core finance flows are still really hard and a lot of the AI tools or services or solutions today don’t address them particularly well. Uh, may maybe to give a a really quick, uh, anecdote, uh, both because of my background on that team back in the Google days, I still am very hands on keyboard, write a lot of code, do a lot of stuff in r and Python and was an early adopter of GitHub co-pilot. Uh, so I’ve seen like the, from the early, early days of what this collaborative code editing looked like, uh, before Cursor really took off and all this stuff and it was magical.
It was a, a huge benefit, a huge plus. And I think that they did two or three things really right, that are independent of the quality of the model. I think one of them is the user interface was done in a way that it allowed me as someone that knew what I was doing to edit and correct things that were wrong. So it would write code and I can go change the code or it would suggest code and auto complete and I could easily dismiss it by keep typing. That was very useful. Or I could like select a block of code or ask a question about that block of code and say, what is this doing? Or like, why is this working this way? Those are three okay to be wrong. I can still go change the output and have it be useful to translate that to finance for a moment.
Now, what I want out of an AI agent that I’m using in my workflow is not someone to ask answer the question for me. It’s someone to like, uh, maybe to pick an example. We talk about vibe coding is a coolest thing right now. Why is there not vibe modeling? Like take this spreadsheet I’ve already built, I want it now for this company or these new set of inputs and what you’re gonna go bill are not static values. I want to change the formulas and I’m gonna go change the formulas in the spreadsheet. Now I’m gonna go do it there, there’s not a lot of that today. Or I’ll pick another one that seems like a, a prime use case for something that should be really easy to do. And that’s like flux analysis or BVA prep. So close the month, tell me what’s different, what moved, why is it, why give me like the summary of it all. That seems like something that should very be very easy of like prepping versus reviewing, you know, month close entries or something like that.
Glenn Hopper:
Yeah. And to get that flux analysis to have it useful. ’cause you can, you could dump, you know, just convert, convert your financial statement month over month into CSV and upload it into, uh, into chat two PT or whatever and, and do some great analysis, but without tied directly into your gl. It’s like, yeah, I can see there’s a variance of, you know, 8% over last month, but I have to then go back to my system and and find out what it is and I could do the same thing in Excel. So getting that value and I it’s interest like I know why Microsoft, we all know why Microsoft put $15 billion into open AI and has continued to invest in ai. I mean, you know, they want bring back clippy. They want <laugh> clippy revenge. They want it to actually work. And it’s, on one hand, I wanna say I’m surprised that Microsoft using co-pilot, I mean it, they’re just, they’re not there yet with, uh, their, you know, chat GPT and the others are are kind of eating co-pilot’s lunch, but you know, where they want to get is that true integration into Excel where, um, you’re <laugh> you don’t have to be able to write the, the 10 line log nested if statement and, and do all the complex stuff that we, that you and I came up being proud of.
Look, look what I can do in Excel and all that. Like I see that, you know, they wanna get it to that conversational interaction or iterating like it’s, I I get to ask that question all the time or people will upload, you know, try to upload a massive spreadsheet with all the formulas and the formatting and everything up into chat GPT and it just completely craps out because it’s, you know, it, it blows out the context window and it, it’s just too much for it to handle. I expect it to be there. And I know there’s a lot of platforms out there that are, are trying to, and, and Google has tried with, uh, putting building AI into Google Sheets as well, but it’s, it’s a hard, apparently it’s a harder engineering problem than, than you might imagine
Matt Hudson:
<laugh>. I mean, I think, I’m sure that there are is a lot on the, uh, backend technical side that make it difficult. I don’t think it’s intractable. I think it’s possible. And ironically, I’d say the hardest part about this in my mind is gonna be on the UX side. Like how do you do it? Like I think the, the key insight that I’ve had is
Glenn Hopper:
Clippy clippy <laugh>.
Matt Hudson:
Yeah, clippy. But that was like a great example of imagine in GitHub copilot instead of like auto completing code. It like was prompting me. Yeah. Like it was like a modal, like that clearly won’t work. I do think there’s something though about what is the right UX to provide suggestions and to provide these kind of like interact, decorate, work with users in the surfaces. Users are already in with this kind of like extra intelligence, which I think is a hard problem. I, I, I’ll caveat part of the reason we’re so bullish about what we’re doing with Grammarly, we, we view it as a very good platform to go do this. But I still think back to the point on finance teams and adopting ai, I just don’t think we’ve seen ’em yet. Uh, I think one of the analogies I heard from, uh, someone a couple months back was if you think of modern computing, there is a period of time where everything was done on the command line and not a lot of people could use it, and it wasn’t really that useful.
And then it switched to the graphical user interface and everything changed and now it a whole new category of people could use it and do stuff with computers. I think it’s the same thing for this, right now we’re in the command line era of ai. We’re talking about chat interfaces and like the pinging back and forth with stuff, painstakingly copying, pasting your GL into, you know, chat GPT and having it regurgitate stuff back out. Like that is a horrible ux. Like no one wants to do that, but we just haven’t gotten to the next UX paradigm yet. Like, we’re still waiting
Glenn Hopper:
For that. Yeah. Alright. I, it’s easy for me to get sidetracked and go down a tangent, tangent with, uh, generative ai, but I think it’s important. There’s a lot of people who never e even in, in, in finance who never thought of or, or uh, really considered AI at all. But you’re, you’re an OG with ai, you’re a machine learning guy and you’re, you know, you’re a data native CFO and I’m, I’m wondering for, and, and this, I mean, a lot of people are, obviously it’s, it’s been the trend for a long time, but going back to your background and history and everything being a data native CEO, what does that actually mean for you now in day-to-day practice? And how does it shape kind of how you approach planning, forecasting decision making and all that?
Matt Hudson:
Yeah, I don’t know if I’ve used that exact phrase <laugh> before, but I certainly prefer getting a SQL statement than getting a CSV. Um, I think that that is much more descriptive to me of like, what’s going on. Um, certainly I’m, I’m non-traditional in, in the, in the CFO molds, but the, I think the most, uh, salient historical pattern I’ve got from the last 20 years is that I spent a large time outside of finance, um, very practically. I think that ends up meaning that I show up differently in three distinct ways. I think one of ’em is, um, and this is less about having the data background, it’s more, I’d say I learned a lot more having the, uh, operator experience and product or in go to market, uh, because it allows you to sit down with some of these other executive partners and solution based on firsthand experience versus just judging it based on reporting your data.
One quality we used to emphasize both in the YouTube days when I was, uh, responsible for the data and data science team there, and in the coded days where we ended up merging our user research and data science team, so we had qual and quant together, was that data itself has a way to lie to you all the time. And sometimes you have to like look at the data you’re trying to get to truth, and you have to supplement it with qualitative input too. So back to like, how do you partner within a finance as a, with a finance hat on, with all of these other functions? It goes back to, you know, starting with empathy, understanding what the data is saying, but trying to see what their real goals are. Is now the right time to be looking at these numbers versus should we be looking at a different one? And maybe the last, the very last point I’d give is that it’s meant that my background has had much more, uh, every couple of years I end up on these learning curves where I’m ramping up very quickly on what it means to run a function like sales or marketing. Uh, and that means that I end up approaching a lot of these problems with first principles thinking rather than a hardened playbook.
Glenn Hopper:
Thought here, as you were talking about that in the teams that you’ve led, I <laugh> I frequently preach and I get pushback from this, especially from kind of the, uh, og uh, FP and a professionals. I really believe that if you’re coming in to fp and a today, and that’s your aspiration to take that path in finance, that you really, it’s not just the traditional good at Excel, good at modeling. Like I think you need to lean heavily into, you don’t need to be a full blown machine learning engineer, but data science chops and not just, you know, most people I think in fp a I don’t, maybe that’s an overstatement, but many people in fp a can write can get around in SQL and they can write, write queries and all that. Now that’s kind of, that’s pretty much become table stakes. But, and, and when I get pushback, it is, well, you know, and, and at large enough companies it’s, well, there’s a data science team, I’m a customer of theirs, I get it. But to me it’s like, I’d rather not be a customer I’d, I’d rather get to that route. I mean, if somebody were coming into fp and a right now, would you advise them, especially kind of knowing where we’re going with AI and, and maybe increased automation? Is it required or recommended that they become pseudo data scientists? I
Matt Hudson:
Mean, I, I think it’s a very personal question. Uh, it’s probably an overstatement to say that every single person that will ever work in fp and a needs to know that stuff. I, I think that’s, that’s certainly not true in a lot of parts of the process and what we end up doing day to day. I, I think you have to look at the whole, uh, shape of the problem and pick which parts of this you’ll end up being an expert on doing machine learning or becoming an expert in statistics. And a lot of the underlying mathematics that go into, uh, predictive modeling or ML or anything like that, uh, is certainly one category. It might be the last category I would pick. Um, maybe, maybe to pick with the first one that I’d, I’d start with, uh, when I was running the data science team at YouTube, we used to have a statement, which was the hardest problem we ever faced was asking the right question.
It wasn’t implementation, it wasn’t getting the data, it wasn’t cleaning the data. It was starting off on the right path and knowing what to ask. Um, that’s true in data, data science and finance. Like asking the right question is hard. It does not require you to be an expert in machine learning and know all the different ways that you can do clustering. Probably useful to know what solutions you have once you’ve got a question and know what’s possible, but you don’t necessarily have to have to be on the ground for to implement it. The second thing I was gonna say, if the first one is asking the right question, the second one I’d say is, uh, I, I often describe this as the data gene, and it’s about knowing in your mind, like the shape of data that answers certain questions. Like what do you need this thing to look like to be able to visualize it the way that you want to be able to measure it the way you want.
And again, that’s not a question of like how statistics work, uh, works or understanding like the details of how neural nets work, but you kind of need to know what shape it needs to be in upfront so that you can go answer the question off of it. And then the last thing I’d say, which is the spot that, uh, has the biggest variance in terms of peep people being able to do well is inference. It’s once you’ve, once you’ve asked the right question, you’ve got the data stood up in the right way, and you’re now looking at something on the other side of it, are you able to interpret it in a way that is meaningful, that is like pointed in the direction of truth and you’ll be able to go change the direction the business is headed using it. All three of those things I think are way more important than, you know, actually writing code or knowing how to use, uh, very specific ML models.
Maybe one other comment I’d give you, were making a statement a moment ago of, if you’re on a big team, you know, there’s a data science team. My business partner with them, I asked them for stuff early on in my career. Someone gave me advice that, um, there’s kind of two ways you end up scaling yourself. Uh, you either scale yourself with people and you lead teams and that’s how you get leverage. Or you scale yourself with technical skills and you learn how to code, you learn how to get access to stuff. And for the first 10 years of my career, I did everything in the f in the first camp, or sorry, in the second camp. Uh, it was all about technical skills, learning as much as I could. If knowledge is power, then access are the keys. And if you can’t get the data, if you can’t pull it, well guess what?
Like, you don’t necessarily have a seat at the table everywhere. Um, so I spent a lot of time focused on how do I make sure I can get the stuff I want, know how to use it, be able to, I’ll know how to ask the right question ’cause I have a vantage point to see what’s important to the business, but now I need to be able to go get it all and do something with it. That is probably a convoluted answer to your question of what advice would I give to folks? Yeah. <laugh>, I I think it’s a little bit personal, like it’s different and not everyone is like well suited to have the data gene or whatever, and like, you should know, I, I have a proclivity that way, or I don’t, and I wouldn’t beat myself up over not having it.
Glenn Hopper:
Yeah, that’s a great point. And I, I actually really like what you said about scaling, and I think for me, the reason it necessity is the mother of invention. And, and maybe because I was wired to go in in that direction a little bit early, but I was in smaller companies, usually turnaround situations where I was, was coming in without a lot of resources, private equity groups wanting detailed reporting modeling and not being able to do it and not willing to work, you know, the extra 20 hours a week on top of what I was already working to do that it was, I had to lean into figuring this stuff out myself because it was my, my way to go in. But, you know, the way you broke it down, it’s almost like a left brain, right brain. I, I don’t know, but there’s an orchestrator that is knowing these are the questions to ask and then having the team that can go answer them in different ways.
And then whoever, you know, the, your <laugh> your data engineer is not typically gonna be your best, uh, storyteller or presenter or whatever. So having those different lanes where the, if you’ve got the resources that you can have different people doing that, that does make sense because people are, are wired for, you know, things they’re going to excel at. And, um, it’s, it’s funny, I, uh, working with engineers all the time, you know, they’re always, when they try to put together like a UX for their, their prototype website or something, I’m thinking that now, I can see why you’re better in front of the blinking cursor than the, uh, <laugh> designing the webpage and everything. But that yeah, that makes total sense.
Matt Hudson:
And like maybe the, maybe the last point I’ll make on it too is that, um, in many cases there’s lots of, I think we’ve, we’ve focused on fp and a largely from like a product analytics, business analytics perspective. There’s many parts of it that, uh, using advanced tools would just be the wrong approach or the wrong tool for like, some of the problems, as an example, me trying to understand, um, uh, or, or modeling out headcount growth, uh, across functions on the team. I’m not gonna, I’m not gonna move to R and like write out a big model that those scenario, like, you’re not gonna do that. Uh, that’s kind of a silly, silly use of time. Uh, on the flip side, if you’re in a company that’s got large scale marketing programs and you can get into the click data, um, that’s much better to like switch to some of these more advanced tools or tools that are built for those bigger jobs.
Even something at the like transaction level, like if you’re able to plumb transaction level stuff into the GL and you’re looking at stuff that way now it’s like, all right, I can see perhaps using one of these tools, um, to go do some first pass analysis. Um, I, I know a lot of like the, the big four, um, where they’ve got technical teams that do accounting or do audits, like they’ll, they’ll staff some folks to look for, you know, signs of fraud or forensic accounting and, and those kind of tools. That might be an interesting use case, but it’s still a far cry from, uh, many of the problems that, uh, fp and a teams face like day-to-day in terms of like just planning the business and helping think about scenarios. It’s a different toolkit.
Glenn Hopper:
Can we talk about your, your team fp and a and sort of the broader finance function at Grammarly, and maybe how that’s, how that’s changed since you came on and, and it’s not just you coming on, it’s the shift in, in expanded focus, bringing Coda in as well. And I imagine that there’s a lot of shuffling and moving around. So what’s the basic structure of your team now?
Matt Hudson:
Yeah, I mean, it’s somewhat uncreative. Maybe I’ll just start with, start with that <laugh> and I think many finance teams end up having a very similar pattern. So as context coda coming in was a, a pretty thin team. We, we ran very, very lean. Uh, we had a, had a controller, had someone that was leading all of our fp and a work, and that was it. We were roughly a 200 person team at the acquisition time across all company. And most of the finance team, uh, or the functions within IT were outsourced. We used A PEO, we used, uh, a third party to do a lot of our bookkeeping. So when it came time for integration, there wasn’t a ton that we had to do for people. It was more on the system side to, to plug things together. The structure of the team today is kind of what you’d find in a lot of, we’re, we’re roughly a 50 person finance team, and the structure is very common for what you’d see in a lot of places.
Uh, I’d say two, two or three broad groups. There’s the accounting and con controls group that includes everything from, you know, revenue accounting, traditional accounting, um, tax, treasury, payroll, ap, um, ar, all, all of that. Um, a lot of our, I often think of it in terms of it’s our looking backwards team. It’s like accounting for history, making sure that we’ve got everything in the right spot. And then we’ve got our fp and a group, uh, which is roughly mapped into to three, three big groups. One of them is our corporate finance team, a lot, all of our consolidation, giving us the full p and l view, and then two business partner teams, uh, that are aligned to the sales motion. So a self-serve group and a managed group. And then the last group is kind of our corp dev and IR team. And there, if the fp and a team is very focused in the present, you know, this year, uh, this quarter, the corp dev and IR team is kind of thinking like longer term, thinking about our capital structure, thinking about m and a, those kind of questions.
Glenn Hopper:
Are any of the members on your team, uh, hardcore data science folks or
Matt Hudson:
Not? So, Grammarly had a, so at Coda I ran the data science team as well, and coming over to Grammarly, uh, they had a very large data org and a, a very large and established data science team, so that, that currently is all within engineering. Um, uh, however, one comment I’d make is that Grammarly as a product is very data intensive. So a very big part of that group is about the foundations of the product itself. Um, so whereas at Coda, I think our data science team was much more on the, I’ll call it like the insights and value add side versus core product underpinnings. Um, at Grammarly, a lot of them are staffed towards core product work. Um, at YouTube we had a very similar dynamic where the data science team that we had carved out under that brand, um, actually I think we might have called it the quant team while I was there too. That was, again, more oriented on in a supporting capacity of, of driving insights, partnering with all those feature teams. Um, uh, and we had a separate group that did all of, like the core, the hardcore ML for like recommended videos on YouTube and stuff that was, that was fully staffed in engineering. And
Glenn Hopper:
I’m wondering, uh, our listeners always like to hear from folks what, uh, what KPIs are important to them. And I think it’s an interesting time for Grammarly as the business broadens into the, this more of a collaboration tool. But can you tell us, without giving any, uh, secret sauce or anything, but what KPIs are important to you? Like what are you looking at most frequently and what’s maybe changed from how Grammarly, what used to be important to them now as the shift happens? What kind of things you’re looking at? Yeah,
Matt Hudson:
Maybe I’ll start with the boring answer and then maybe I’ll, I’ll switch to something that’s a little more creative or interesting. Uh, the boring answer is I feel like a lot of the core things that you would’ve measured 10 years ago are the same things that are still important today. You’re looking at usage, whether it attracts new users, you’re looking at acquisition, um, you’re trying to understand what, what’s gonna lead to conversion, paid conversion after someone’s been using it for a while, how to think about spread. If someone starts using a product, starts paying for it, do they bring in new users? Is there a K factor that you care about? Uh, and then lastly, retention. You look at, you know, the most powerful common denominator is your net dollar retention. You look at, you know, whatever cohort looks like, whether they’re, uh, trending in the right direction over time, and understand just how sticky these revenue lines are.
I don’t think ai, other than it’s a new product experience and you wanna think, think hard about users, seeing it as differentiated value against other, other things in the marketplace has really changed that. Like, I don’t, I think maybe there are small things like when you think about usage, is it something that they’re actively using? So the equivalent of a long click in the search result versus something that was like generated but not used. There’s something interesting there. But again, I’d say those, those feel to me very, very much like traditional product metrics may maybe stepping back to the less boring or more creative aspect of the question, um, are you familiar with Richard Feinman? Richard Feynman, he was a, the theoretical physicist from that project. So he, it is funny, I was telling someone this the other day, YouTube and all of its, um, Supreme Intelligence recommended one of his like 1950s, 1960s lectures for me, and I’m a nerd.
So I watched it and one of the students was asking him a question, so he’s known for, and I’m gonna get this part wrong, so no one should fact check me, uh, elect electrodynamics and quantum theory, dunno what any of that means. Uh, but someone was asking about why we needed new theories versus Einstein’s theory of general relativity or whatnot. And the answer he gave was that if you’ve got two different models, two different ways of measuring the universe, or in our case measuring a business or a product, and they both produce exactly the same result, the question might be, why do you have to have different models? Can’t you just pick one of them and use that all the time? And his response was that it’s not because the prediction is right, it’s not because they produce the same result. That’s important. What’s important is that when you look at the different models, it leads to different questions.
It leads to different ways of thinking about how does the universe work or how does this business or product work. I actually think we’re at a very interesting inflection point as a company right now where we’re going from, you know, the coda world, the Grammarly world, um, into this new space where all these things combine and it is an important moment to rethink what does this model look like? What is the right way to think about how the product works? What are the key drivers? What are different ways you could go develop, um, a revenue model or a growth model that represents all the moving parts and all the important mechanics. I think right now we’re kind of in exploration mode thinking about what are the different ways we might wanna model this, but it is something we’re actively going through and is fun, fun, fertile ground.
Glenn Hopper:
Yeah, and it’s, it’s gotta be an interesting time for you just thinking about, so, you know, Microsoft has had doing air quotes here, collaboration tools for forever, uh, that are, you know, definitely in a lot of ways lead a lot to be desired. OpenAI just announced yesterday, whatever they’re thinking. Uh, and that’s gonna drive a wedge probably more so between them and Microsoft if they roll it out. But OpenAI is talking about doing collaboration and I know Grammarly has a, a dedicated, uh, committed user base of which, uh, everybody in my family is <laugh> is one of them. Um, but you know, going into the collaboration space, knowing that you’re going up against these big heavy hitters there and then trying to think about where this goes in the future. It’s gotta be, it’s gotta be an exciting time to be A CFO right now.
Matt Hudson:
<laugh>, I mean, I think it’s just an exciting time to be in the space to be in software. Yeah. Again, I think the CFO role gives you a vantage point in all parts of a business, which is fun, but it is certainly a fun time to be part of this, part of this space or part of this environment.
Glenn Hopper:
As I was walking through and thinking about this, I was wondering if, if any of that product background is sneaking into your, your CFO thought and, and actually sneaking in is not the right way. I mean, I think that’s a great way to, uh, to sort of round out the CFO office, but are you finding yourself with your, your pro defaulting to having your product hat on sometimes?
Matt Hudson:
Oh, certainly. And I think, um, especially when you start talking about how do you measure the business, um, I, when I was a product manager, there are a lot of different ways you can think about what a PM looks like, like what their strengths are. I wasn’t the design pm, I wasn’t, you know, necessarily the processor into pm I was always the data pm I always had kind of more of an instinct for it, somewhat natural coming from my background. So I think that in looking at all these problems, this goes back to the modeling thing we were just talking about. Um, a lot of my instinct slash perspective starts from what are the ways that you’d actually model out the mechanics of how this new product experience is gonna work? Like, what are the ways it’s gonna happen, um, et cetera. Uh, I mean, to your, to your macro prompt and OpenAI getting involved, um, I think that there is a gut check happening across the industry and trying to figure out where in this chain value is gonna accrue and then value get captured if it happens at the foundation layer, at the app layer.
And we’re gonna see all sorts of interesting investments and, and movement. Uh, but it’s a fun time.
Glenn Hopper:
Yeah. Gosh. And I can’t believe how we did. We’re we’re, we’re almost coming up on time and I’ve got a bunch of other questions I wanted to ask. I’m gonna try to guide us in for a landing here so that, uh, I don’t know if, if our listeners are sitting in their car or waiting to go into the office, I dunno if people still go to the office, but if they’re <laugh>, we’re gonna try not to keep ’em in the car much longer. So I had a couple questions. I guess maybe I’ll just throw, throw out. Um, I’m gonna wrap these together and you can ad address them, uh, as, as sort of one, but I, I always like to ask, especially you really have your finger on the pulse of, of AI and tech and, and where we are right now. So two questions I’ll put together. One, if you’re looking forward five years out, knowing where the technology’s heading, how you might see the finance function evolving, and I’ll piggyback on that, <laugh>, and maybe this is unfair, but piggyback on that.
For fp and a people who are in the space right now and maybe they’re looking to build out stronger data and AI skills, as you look into your crystal ball and, and see where we’re headed, what would you advise them to focus on right now?
Matt Hudson:
It’s a great prompt. I mean, I think, uh, in terms of how does the finance function or the fp a function evolve, uh, my guess is it’ll be a very similar pattern to what we saw in prior generations with the advent of computers or spreadsheets. And ultimately what it ends up producing is more time to focus on, you know, the interesting strategic quick choices or questions. I certainly am hopeful that we’d end up seeing a bunch of the more mundane or, um, regular work becoming less and less, uh, uh, something that consumes the days. There was someone that I was talking to that was describing one of the measures that you have of your broader finance org is how much of the org is dedicated within the month to month close in reporting. And like, if you can minimize and squish the amount that comes from month close reporting and like just turning over, you maximize more time left over for strategic thinking, interesting problems.
Um, I imagine all of these tools are gonna end up helping with that distribution and shifting towards more time to spend really driving inference and understanding rather than just turning the crank on making sure all the books close and that we’re reporting out budget and stuff like that. It is a night and day difference talking with folks that are curious versus not. Um, finding it, finding individuals that are interested in adopting new tools, trying to find or experiment with new ways of doing things. Boy, I would certainly be on the, at the front of the line of try out new tools, see how you can learn them. I, I might even encourage towards, instead of using, uh, packaged tools that might have like somewhat limited capability or perspective and try to go towards more of the raw tools, um, to see how you can string them together, things that are composable, um, maybe is a different way to say it. You want, you wanna try to use things that are adaptable and then you can see applying the same skillset that you develop using it across many domains versus just being locked in or stuck in the one spot where you can use it.
Glenn Hopper:
Yeah. Love it. Love it. Great guidance there. Um, okay, so lightning round two questions we ask everybody. So I’m gonna dive in here. <laugh> the first one. What is something that not many people know about you outside of your work interests?
Matt Hudson:
For folks that have worked with me, they probably know it at this point, but one of my favorite parts about working at YouTube was that it was within walking distance to Chili’s, <laugh> Chili’s. If Chili’s was a franchise model, I’d probably be in it. Um, but I, I started, uh, that was probably my first quote unquote real job, was being a busboy at Chili’s.
Glenn Hopper:
Dude, I was at Chili’s. I started as a busboy. I did prep Cook, I was a waiter, and by the time I left, I was a bartender. I’d made it to the, that’s top of the chain at Chili’s. I was there for quite a few years, but
Matt Hudson:
If you wanna find hard, but I love that, have that in common. Wanna find hardworking people, start by closing chilies at the end of the day.
Glenn Hopper:
Oh yeah. And you would go home smelling like fajitas. It would just <laugh>. Let’s, Mitch Hedberg had a line of, uh, he wished there was a fajita cologne. I was like, go work at chilies, you’ll wear some fajita cologne <laugh>.
Matt Hudson:
Uh, for, for the longest time I made my wife take me to Chili’s on my birthday. Uh,
Glenn Hopper:
<laugh>, no, never
Matt Hudson:
Been my celebration, but
Glenn Hopper:
That’s great. That’s great. Okay. This, I’m, I’m curious to hear your answer on this one, and I need to start logging these. I say it every time. We don’t, we don’t log them, but it would be very interesting to see what is your favorite Excel function and why?
Matt Hudson:
I have a confession. I have been a hardcore Google Sheets user for at least 15 to 17 years. Um,
Glenn Hopper:
Wow. Well because you were, yeah, because you were there makes,
Matt Hudson:
You were at Google when they were, they called it tricks internally when they were first building it, but I was like one of the first users of it and have been using it since. Uh, and ironically the, for the formula that I use the most within sheets, Google Sheets is not available in Excel and it’s called Filter. And it’s basically, I mean now they might have equivalent versions of it, but I find it to be the equip using filter along with named ranges allows you to write very, very readable formulas that can be quite complicated. The equivalent of doing like a huge block of some ifs or count ifs or whatever. So anyways, Filter would probably my top list. If I have to stick to Excel, it would probably be offset or indirect. Um,
Glenn Hopper:
Yep, yep. So, no, no, we’re, we’re, uh, we’re equal opportunity here. So I just, I, it’s, it’s funny how few people, uh, actually I talked to someone at Google a couple months ago that they, that he doesn’t use Google Sheets. He’s in finance at Google. Yeah, yeah.
Matt Hudson:
<laugh>, I mean, for a long time we could, I didn’t even switch from it until I had moved out of finance. ’cause we would have to use Aspace or Hyperion, which didn’t have a Google connector or Google Keith connector. So we’d have to do it in Excel.
Glenn Hopper:
Well, Matt, this has been an absolute blast. I really appreciate you coming on and thanks so much and, and best of luck to you and Grammarly. I’m, I’m super excited to see this, this new collaboration direction.
Matt Hudson:
Yeah, that sounds great. Hey, can I give one final plug?
Glenn Hopper:
Absolutely.
Matt Hudson:
We are hiring on the finance team at Grammarly and in particular are actively looking for folks that have this finance, analytics, uh, background. So engaged very much in this space or this domain.
Glenn Hopper:
I actually love that you threw that out there and I bet a lot of our listeners are immediately opening their web browser and finding where did they go? Where did just go to the Grammarly website and, and jobs. Is that the best way to find
Matt Hudson:
That’s, or you can find me LinkedIn too. Uh, happy to route.
Glenn Hopper:
Okay, great. Great. Well, Matt, thank you very much. All right, thanks Glenn.
Matt Hudson:
Talk soon.
