What we know about the incoming Presidential position

On October 15th, Trump’s cryptocurrency token, World Liberty Financial, went live. This was a first for a US presidential candidate, and now a first for the incoming president. His cabinet picks include bitcoin bulls like Elon Musk, who is associated with tokens like Dogecoin.

Notably, such actions are a departure from prior comments around cryptocurrency. Back in 2019, he tweeted, “I am not a fan of Bitcoin and other Cryptocurrencies, which are not money, and whose value is highly volatile and based on thin air.” This skepticism continued after his presidency with similar statements on Fox News guest appearances.

However, his position has clearly evolved, and the market has listened. Bitcoin is now at an all-time high, and we continue to see more Web3 startups looking to navigate the changing regulatory landscape.

The digital asset vs. security divide

While innovation in the Web3 space has proven unstoppable, businesses face a critical challenge: determining whether their digital offerings qualify as securities or digital assets. This distinction carries major implications for tax obligations and compliance requirements.

A digital asset is something you can use, like a subscription, license, or collectible. Think of it like buying software or an e-book. Securities, on the other hand, are investments where you’re betting on future profits from someone else’s work, similar to buying stocks. Many Web3 products blur these lines and interestingly, your marketing and business model can influence classification.

With digital assets, one may need to consider indirect tax if the product is taxable in the purchase location. Several states like Pennsylvania and Washington provided guidance as early as 2022 to incorporate NFTs in their definition of taxable digital goods. With securities, one may need to consider capital gains tax or income tax depending on the specifics of the transaction.

So, how do you know which category your token falls into? Enter the Howey Test—the decades-old framework that’s now the key to unlocking Web3 tax compliance.

Meet the Howey Test

The Supreme Court’s 1946 case about Florida orange groves might seem far removed from cryptocurrency, but it created the four-question framework that determines if you’re dealing with a security today.

Here’s the Howey Test—answering yes to some or all of these questions indicates your asset is a security:

1. Is there an investment of money?

2. Is there a common enterprise?

3. Is there an expectation of profit?

4. Do profits come primarily from others’ efforts?

The more centralized your project’s development and decision-making, the more likely it is deemed a security.

How does this apply to Web3? Let’s see it in action. Gaming tokens that only function within a specific game environment and can’t be traded externally? Likely a digital asset. Tokens sold with promises of future platform development and value appreciation? You’re probably looking at a security.

This test has been central to the cryptocurrency regulatory debate. While the SEC has provided limited guidance, many startups have structured their tokens and business models specifically to function as true digital assets, avoiding the characteristics that would trigger securities registration requirements.

It’s important to note that while the Howey Test is informative particularly at the federal level, states can and have been taking independent regulatory actions against digital assets, even when those assets might not clearly meet the Howey Test criteria. This results in state security laws potentially being applied more broadly than federal, making compliance even more inconsistent and difficult.

Tax implications for the Web3 community

Let’s cut through the complexity and focus on what this means for your bottom line. If your offering qualifies as a digital asset, you’re looking at a different set of tax obligations than securities.

For digital assets, sales tax obligations kick in when your digital asset is deemed taxable in the customer’s jurisdiction. You’ll need to:

• Register for sales tax in states where you have nexus

• Collect and remit sales tax on applicable transactions

• Keep detailed records of each sale’s jurisdiction and tax status

For securities, your obligations shift to securities regulations, requiring:

• Form 1099-B reporting for traders

• Capital gains tracking for investors

• Documentation of basis and holding periods

Keep in mind that assets potentially classified as securities under the Howey Test are also subject to sales tax in some jurisdictions. So knowing what an asset qualifies as at the federal level is not determinative when you get down to state-level taxation.

Given how nascent the Web3 space is, it is prudent to call in the experts early. The key moments include initial product classification decisions prior to launching the product and entering into new jurisdictions.

At Anrok, we are the trusted platform with many Web3 companies, including Alchemy, CoinGecko, and Magic. Our platform and network of experts can help you get it right from day one. Reach out today if you’re building in Web3. ⊞

Today, Jeff brings his risk management expertise to multiple roles: as an operating partner at Bessemer Venture Partners, board member at companies like Okta and Twilio, and trusted advisor to countless CFOs. In a landscape where finance leaders must balance AI investment enthusiasm with market uncertainty, his frameworks for evaluating risk feel particularly relevant.

Jeff sat down with Anrok co-founder and CEO Michelle Valentine to share his perspective on how modern CFOs can lead through uncertainty, what metrics actually matter for scaling companies, and why the best finance leaders know when to take calculated risks—and how to bring others along. ⊞

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In this episode, I spoke with ShipHawk Controller Meena Izadpanah, who brings over a decade of experience in accounting, including roles at Inspirato and Invoca.

Meena shares several key insights from her experience in financial reporting, including:

• The importance of solid accounting practices in customer relations

• Her top tip for audit preparation (as an ex-auditor!)

• Advice for companies preparing for their first audit without a controller

Watch the full conversation to hear Meena's expert perspective.

Transcript:

What's your biggest worry or challenge heading into the future? 

Currently, we have a competitor who's coming after our customer base and offering a competing product for free. I think in this economic market, it would be very hard for any CEO or CFO not to take that call. So I think that's our biggest challenge is proving our value. And that, just cause it's free, doesn't mean it's good or better.

How are you thinking about supporting that on the accounting and finance side of things? 

With accounting, sure it's back office, but we're talking to vendors, we're talking to customers every day, multiple every day. Having your written communication, having a solid invoicing process, making sure you're charging when you should charge, and just the basic accounts receivable processes is important. You are a representation of your company, and a customer could lose faith in the company if you're kind of a mess in the back.

What's your top tip for preparing for an audit? 

Find what you missed before they do. It's a big statement, but I fortunately was an auditor for several years and are familiar with the processes and the tests that they'll do. You can Google that if you don't know, or maybe call a friend, call another controller, see what their tests are and perform the test in your own company. Let's say something related to the audit year came in two months later, just create a list of it. Then, when they say, “Hey, I found this,” you're like, “No, I found it first.” That protects your management letter, which is a letter that goes to the board and management about the quality of your financials. So you want that to be perfect.

What would you recommend to companies heading into their first audit without a controller? 

More often than not, your audit firm wants to help you. They would rather be consulted than surprised. So create a relationship with them. Don't be worried that they're coming in and checking everything. Think of them as a partner. When you're unsure about something or how to state something or record something, just reach out. I reach out to my auditor all the time when I have a new product in service, and I don't know what the rev rec is going to be. I just check with her. By the time she arrives for the audit, they already know all the big stuff. I'd lean on your audit firm, you pay them enough! ⊞

I've witnessed firsthand how the compliance landscape for tech is shifting, culminating in our decision to start Anrok. Recently, I had the opportunity to discuss these seismic changes on Turpentine Finance with Sasha Orloff. In our conversation, we unpacked:

• Why sales tax is becoming more pervasive for software CFOs and teams

• Post-Wayfair tax landscape: How the rise of remote work is triggering unexpected tax obligations

• Global expansion 101: Debunking the B2B tax exemption myth that could cost companies millions

The question isn't if you'll address these challenges, but when. Liabilities continue to accrue even if you ignore it. And in today's fast-paced environment, timing is everything. This discussion offers insights that could save your company millions and position you for sustainable growth.

Watch the full conversation to learn more about these critical issues and how they might impact your business.

You can also listen on Spotify and Apple Podcasts. ⊞

So what happens when something comes up mid-way through the year that throws your carefully crafted budget into question? Even a positive event, like raising a new funding round, could prompt your team to reconsider how you should allocate spend in the next few quarters.

If you ask Caitlin Haberberger, a five-time CFO with decades of experience at fast-growing startups, revising your budget at mid-year isn’t just normal—it can be the best practice for early-stage companies that are in a constant evolution.

Haberberger sat down with Anrok co-founder and CEO Michelle Valentine to share her perspective on setting successful budgets, leveraging forecasts, and aligning around off-cycle budget updates with your board.

Transcript

Michelle Valentine: I’m excited to be here with Caitlin Haberberger, a veteran CFO at companies like Amplitude and Mezbo, and I’m excited to share her wisdom around executing and setting successful annual budgets. 

Caitlin Haberberger: Hi, I’m super excited to join you, Michelle, and look forward to our conversation today.

MV: Fantastic. Caitlin, can you share a little bit about your background before we jump in?

CH: Sure. I’ve had the opportunity to work at a lot of fast-growing venture-backed startups, primarily enterprise SaaS companies. And what I love about working at these types of companies is being able to jump in and help solve so many different business problems.

So I’ve certainly seen a lot from being at companies that have thrived and gone on to go public, companies that have been acquired—there’s lots of different paths. And so I bring those experiences to bear every time I approach a new problem, a new company, et cetera.

MV: So we’re talking about annual planning today, and this often happens at year end. 

Before we even talk about why you might need to revise it, can you share a little bit about your philosophy on setting an annual budget?

CH: I have a lot of strong feelings about this. I think that the budget should be the culmination of a much broader planning process. And I’ve been in situations where I’ve had someone ask me: Can you set the budget for us for the next year?

Okay, I have a lot of questions.

What are our goals? What’s the market opportunity? What problems are we trying to solve? Where do we need investment? What metrics do we care most about?

And so, I hate being that person who just comes with more questions, but I think the planning process starts way upstream of a budget being produced, and I think that’s actually where companies get it wrong.

They wait. They think they’ve got to deliver the board a financial plan and budget, but they’ve missed a lot of the essential prep work that happens from an exec team working together to define, what’s our mission? What’s most important in the next 24 months? How does that break down to the next 12 months?

What kinds of results can we reasonably expect? What markets are we going after? What problems are we going to solve? And that might sound daunting and there’s tons of frameworks out there. And as an early-stage startup, you don’t have to adopt a super complex framework, but you have to go through some planning exercise before you can think about how you’re going to apply resources against that.

And I think that when you don’t start with the upstream planning, you can end up in a situation where you’ve got a budget where people aren’t aligned. They don’t understand why they have, or don’t have certain resources. You’ve got sales targets as a part of your financial plan, but do you actually have the resources on the sales and marketing side to achieve that? Has product delivered enough product to go hit those sales goals?

So the budget itself is a culmination and a representation of all the planning, the metrics that you’re expected to hit, the investment that’s needed, the resources, and without going through a planning process, you’re going to miss the alignment that’s needed to go execute on your business strategy.

MV: I like that flag a lot where a lot of people miss the upstream planning portion and focus too much on the nitty-gritty of setting the financial plan and annual budget.

If you were to prescribe a rough percentage on what you think time allocated looks like, how much time should teams be spending on that upstream planning portion versus this downstream, like setting the financial plan in Excel?

CH: That’s interesting. I haven’t thought about it quite that way before. It might surprise you. But I think it’s 80 percent is up front and maybe 20 percent is on the actual budget. Not to downplay—there’s a lot of nuts and bolts and nitty-gritty.

But it’s a lot easier to get through the nuts and bolts when you’ve done the upfront work and you know what you’re targeting towards and you have that alignment.

So that when you’re making those inevitable resource tradeoffs that are needed, if you’re starting with an executive team that’s thinking holistically about the problems that they’re trying to solve together and the resources that the company needs rather than the resources that a specific team needs, it makes that budgeting process and that planning process so much easier on the back end.

MV: And when you are reviewing an annual budget for the first time, are there specific rules of thumb or things that you’re paying attention to, that you as a CFO are looking at that you wish your team paid more attention to?

CH: There’s obviously some high-level metrics that matter most, right? Is the growth rate tied well enough with the spend rates, with the ultimate net, let’s say it’s a net loss, but hopefully moving to net profit.

So do those things all hold together? Do they line up appropriately? I have certain checks that I’ll always use. So as a percentage of revenue and a percentage of spend, how much are we spending on the big buckets using the SEC categories, of sales and marketing, research and development, G&A.

Even though there’s not a right answer, but there are reasonable ranges. So I’m going to look at things like that. And then I’m always going to pay attention to the cashflow. That may sound super, super obvious, but sometimes that gets lost in the shuffle.

So I’m going to always use those as my guideposts. And I have sanity checks that I can apply against that might pop out—are we overweighted in a certain area?

And then I’d also start looking downstream a little bit, at some of the underlying investment areas, like if it’s sales capacity on a sales rep standpoint. So looking at some of those other pieces and making sure that I’m confident kind of how that all lines up together.

MV: You mentioned cashflow, if we can spend a few minutes on that. If you’re a Series C finance leader versus a pre-IPO finance leader, how should you be thinking about the cashflow item of the budget differently? 

CH: In an early stage, it’s expected that you’re going to be investing heavily into the opportunity. Somebody has given you money from their venture fund because they see a great opportunity.

They believe in the team and they want you to go spend their money to get that product to market, to get those first customers, to go after that opportunity. And you’re there to do that. And so you need to monitor your cashflow in that you need to have enough cash to get you to your next financing opportunity.

And in today’s market, that might look different than it did a few years ago, in terms of how freely you’re spending against that opportunity because that next financing opportunity might be a lot harder to come by. But the general concept, you’re there to spend that money to go after the opportunity.

You may not even have any revenue yet, and that’s okay. The closer you get to going public, the more your financial model has to make sense. Whether you’re going public as cashflow positive or close to cashflow positive or breakeven or incredibly profitable, it’s going to be expected that you are tightly managing that. And at that point, at the point you go public, you’re not managing to your next financing event, right? Like you’ve had your financing event and you’re managing towards delivering the most value to your investors.

So there’s the common thread with the early stages: You’re trying to deliver value to investors, but how you get there is going to look really different depending on your stage.

MV: Your point about the financing event changing how you should think about the annual budget is one of the reasons why we’re talking today. 

Even for Anrok, we recently raised a Series B and that has made us think, should we revise our annual budget? How common is it for teams to rethink the annual budget after a fundraise?

CH: It’s a really interesting question to think about because it’s like everything changed and then nothing changed at the same time. And what I mean by that is, hopefully, you had a financial plan or board-approved budget going into the period that you did your fundraise. And then I imagine through your fundraise, there’s certain financial projections you made and promises around expected growth, around metrics, around how that shape of the business is going to look and what you’re going to do with that money that those investors just gave you.

I really think about it as: What has changed? So the financing event— it’s simply a financing event. You got more cash. Great. Now, what’s changed in the business? How does this now free you up to go invest into things that because you didn’t have that cash in the bank?

And that might cause you to reassess your budget. But it also may be that your budget has already reflected anticipating that those funds came in.

Dangerous things I’ve seen at other companies have been that celebration of, we’ve raised the money and now everybody on the exec team is coming. Like great, now that we raised the money, I need four more sales reps, and I need another million dollars of demand gen spend, I need another financial analyst now to figure out how to analyze how we’re going to use that $50 million we raised.

And so everybody thinks things are going to open up, and I think it’s really important for a company after a fundraise to maintain rigor around those investments and with a lot of clarity at the exec team level around what are the expected outcomes for any incremental investments that get made because the company now has the resources to make those investments.

MV: One thing I’m hearing you say is there’s a difference between a budget and a forecast. Can you say a bit more about the key difference in your mind, and what teams should keep in mind?

CH: That’s a great question. A budget is typically for governance and just good business practices, something you’re going to have every year. It’s going to tell you what are you expecting is coming in from your customers on the revenue side, and then how are you investing into your company all the way through to cashflow as we talked about.

That budget gets stamped, sealed, approved by the board. It’s a static document, but the minute it gets approved or even before it’s approved, the forecast of what’s actually going to happen is already changed. It memorializes things and captures things at a point in time and immediately starts changing.

And so I think about a forecast as: Great, we set our plan. But now we’re actually going out and executing, and nothing’s ever going to look exactly like that plan that you set out. And so the forecast becomes the living, breathing document of how are we actually now doing and projecting we’re going to do for the rest of the year. And that’s something that needs to be updated, monthly, maybe quarterly, if you’re on a really small team.

Are we on course? Are we off course? Do we understand why? Is that okay that we’re off course? Is that not okay? What I typically see at fast-growing startups is you set your budget for the year, and by May you’re wildly off from that, hopefully on the positive side, meaning you’ve got way more revenue than you expected.

Typically, what I’ve done at most startups I’ve been at is that in the middle of the year, we revise our budget for the rest of the year and have the board formally approve this budget reset. 

It gives me some confidence and security knowing that the board is fully in the loop on everything going on financially with the company. If you’ve somehow managed to set a budget at the beginning of the year that you haven’t varied from too much, great. You don’t need to reset it. I also don’t advise resetting it every quarter because that shows me that you’re just spending your time on the wrong things. 

MV: How have you broached that question to the board of should we revise the budget mid-year?

CH: Let’s say it’s April and things are starting to look pretty different than that initial budget. First of all, that wouldn’t be a surprise at that point, because we already would know that and have been communicating that to the board.

But I’ve often sat down with a few key board members and said: Hey, look, our forecast now for the rest of the year is looking like this. Our budget was this. I think we really need to realign with the board on where we’re investing. And ask them: How do you feel? I would be more comfortable resetting the budget for second half of the year. Do you agree? Here’s my reasoning why.

I’m getting their buy-in up front, and never spring that on them in a board meeting. I think it comes down to showing that you’re understanding the business. So it’s really for alignment purposes, is where I see it coming into play.

MV: A big learning for me as Anrok has grown is, a lot of the budget in the beginning, everyone is dedicating their time to building the product, R&D. And then at some point you hit product-market fit and you start building teams and systems and processes to get your product out there, distribution, and scale the product. And then at some point you should be getting economies of scale from those systems and from that process.

How have you seen that transition be reflected in an annual budget? Like, how soon should you start factoring in those potential economies of scale into your annual budget?

CH: You really need to have a three-year financial plan. And the annual plan is then one step on that journey of what that three-year plan looks like. Three years out from now, what’s the shape of the business that makes sense?

There’s different groups where you can get some of the data around other companies that are at that growth stage. How are they spending their money? And I like using that data to help shape out. So three years from now, if we’re, let’s say, a company that’s at $30 million in ARR, I could have an idea of, okay, we’re not going to be fully efficient on sales and marketing, but we’re going to look a lot more efficient than we look today.

And our investment into engineering and product is probably going to taper down a little bit at that point, but it’s not going to be as efficient as a company that’s about to go public. And that’s where there’s art mixed in with science, because I can’t tell you that in three years at $30 million in ARR that, R&D should be 36.9 percent of your revenue, right? But I could tell you that it shouldn’t be more than 50 percent and it probably should be more than 20 percent.

So it’s using that long-range plan and then backing that up to where you are today and thinking about the path to get there, is the approach that I would take.

MV: What’s the one piece of advice that you want to leave founders with that are working with their CFOs, with their finance teams, on revising an annual budget after a fundraise?

CH: I think the highest-level thing is the exec team needs to be aligned around where the company is headed, what’s most important, what’s expected, what’s been promised to your new investors.

And then there’s alignment between the CEO and the CFO on the overall shape of the financial picture, like how much are we willing to invest at a high level? What is a reasonable level of cash burn?

Then those two things can come together.

MV: I like that a lot. I think for me, the big two takeaways that I got from you today was one, the upfront investment in the big picture vision before jumping into the weeds of the numbers with that exec team.

And number two, revising the annual budget partway through the year-that is normal, right? It’s not too atypical to say partway through, the forecast is looking different than the budget. It’s time to revise.

So thank you so much for sharing your wisdom. It was really fun having you today.

CH: You’re welcome. ⊞

The past 12 months have been tumultuous for the public markets, and almost every finance leader at a tech company has been affected. The Federal Reserve’s decision to abandon the notion of transitory inflation and consistently raise interest rates has sent shockwaves through the economy, particularly in the technology sector. 

The end to the zero interest rate environment hit technology companies the hardest. SaaS—with its growth potential and recurring revenue base—was once seen as an attractive alternative for investors hungry for yield. With the rise in interest rates, public software valuations have gone through a roller coaster ride.

As we enter the second quarter of 2024, the economic landscape for technology has improved but remains uncertain. The three-month annualized CPI still hovers at 5%, and the Fed Fund Rate has been at 5+% for nearly a year, suggesting that the higher interest rate environment is here to stay. This market dynamic trickles down to private software companies. For many months, it seemed that only startups with “AI” in their name could successfully raise an up-round.

Earlier this year, Anrok raised a $30M Series B from some of the top investors in Silicon Valley: Khosla Ventures, Sequoia Capital, and Index Ventures. 

Anrok is not an AI company in the traditional sense. So, how did we do it?

Here are three lessons from our recent fundraise:

1. Business fundamentals still matter, a lot

In a challenging market, it’s crucial to know what metrics make your business stand out. Whether it’s efficient revenue growth, net dollar retention, or consistent execution quarter over quarter, be prepared to dive deep into the numbers and tell a compelling customer story. A two-page memo is an effective way to provide a story to introduce the metrics and link to the underlying spreadsheets in the data room.

The other big question investors will likely have is customer segmentation. Can you demonstrate that you’re consistently moving upmarket and winning enterprise clients? How do you know your product is ready for prime time?

2. You don’t have to be an AI company, but you have to have an AI strategy 

In a world where the step functions between AI models are truly exponential, it’s essential for founders and their finance leaders to have a clear point of view on why their business not only survives but thrives in an AI world. 

For Anrok, we see our business as the unexpected infrastructure of this growing digital economy. As software continues to eat the world, and AI accelerates this trend, every revenue-generating software company will need to figure out sales tax, and Anrok is here to help.

Beyond market positioning, it’s also important to communicate how AI fits into your operations and product roadmap. At Anrok, the goal is to create an environment where everyone is comfortable using AI assistants to augment operational workflows and build intuition to embed LLMs into the product.

3. Focus on the right partner at the right fund

There are two inputs into a promising long-term partnership: the person and the fund. Focus your time on the people you can see yourself working with in the long run. These relationships matter, whether you’re planning for a raise or find yourself in a pre-emptive situation.

For us at Anrok, it was important to have a partner where we felt like we learned something new every time we met. On the fund, we made sure that every fund we spent time with saw the near future clearly, and was prepared to make contrarian bold bets.

Your fundraise is too important to compromise on either. 

The biggest takeaway from Anrok’s recent fundraising process is that business fundamentals matter more than ever, but so does understanding why your business should thrive in an AI world. Investors are right to focus on this. A lot is about to change. ⊞

A year later, I’m sharing the resource we put together for startups navigating cash management. I also discussed this framework and other banking strategies for finance teams with former Dropbox CFO Ajay Vashee here:

Journal Entries

3 post-SVB banking strategies for startups

After the collapse of Silicon Valley Bank earlier this month and the liquidity crisis it caused for the startup ecosystem, many founders and operators are re-thinking their banking strategies. In a recent conversation with Ajay Vashee, a General Partner at IVP and former CFO at Dropbox during my time there as Treasurer, we talked through some best practi…

Read more

3 years ago · Brad Silicani

Cash management approach

Every business is going to be unique in its risk tolerance and liquidity needs but the below framework provides a starting point for the conversation.

Practical tips

Remember: Investors gave your company money because they thought your team could build great software, not because they thought you were great at generating interest income. The board won’t celebrate your extra 25 or 100 bps of yield or amazing investment tip, but they will definitely have some very difficult conversations with you if you lose any money

Investment policy

• Example Investment Policy

  • This is a pretty “plain vanilla” policy with all high quality credit rated investments

• Have the board approve a policy to get alignment on risk tolerance and investment preferences

  • Solicit feedback directly from board members in advance

  • Remember diversification is your friend

Commercial banks

• Pick one top tier (JPM, BofA, Citi, Wells) 

  • Strong credit and building a relationship helps a lot with growing into future debt and equity transactions

  • They are tough to move quickly with unless you are large or about to have a debt or equity transaction

• Pick one more aligned to your size 

  • They are much easier to get things done quickly with

  • Some can have much better tech too

• Have one bank support your daily operations (payables, receivables)

• Have the second support your periodic transactions (payroll, rent, etc)

Money market funds

• Recommend Goldman Sachs Mosaic portal because:

  • It gives you a third place to execute and move money should one the commercial banks go down or act slowly. Funds can be wired easily to/from to any account.

  • It’s free and they don’t bother you about your deposits, cash accounts, etc because they don’t offer those services as an investment bank

  • You can invest in any fund family (i.e. any money market fund out there) -> typically commercial banks significantly limit your investible options

  • You can access all share classes of funds with no minimums -> you can get a better yield on the same fund with access to the higher share class

• Government funds are easier to deal with from an accounting perspective and also easier to get comfortable on credit risk questions than Prime funds

Short duration debt

• Only get here when you have:

  • A really, really strong forecast

  • At or approaching free cash flow neutral to positive

  • AND a lot of extra cash

• Use external managers called Separately Managed Accounts—they are experts on credit assessments, your internal team is likely not.

  • Pick 2 so you have an easy performance comparison

    • One from an existing bank relationship

    • One that is a pure investment advisor so they don’t have any bias towards their own Issuances or service lines

Disclosure: Anrok does not provide tax or legal advice. Please consult with a tax or finance professional regarding your specific circumstances. ⊞

In our first episode, I spoke with Dropbox VP and Controller Sarah Schubach, who’s seen the company grow from Series B to IPO and beyond.

Sarah shares plenty of useful insights from her 10+ years of experience growing Dropbox’s finance function, including:

• Why it’s critical to hire the right people early on

• Her top tip for preparing for audit (as an ex-auditor!)

• What her team has learned from using generative AI

Watch the full conversation to hear Sarah’s expert perspective.

Transcript:

What’s one thing you were glad you took care of early in your company’s growth?

Early on in our growth, we made sure that we hired really smart people and that we thought about how we could scale processes once we were public. We were in this state of perpetually 18 months away from an IPO and always trying to make sure that we were thinking about how we would tackle different problems once we were a public company.

I think that was super critical because obviously we didn’t do everything perfectly, but we were able to think into the future and make sure that we were putting the right processes and people in place so that once we were public, we wouldn’t be scrambling on the backend to deal with things like an audit.

I think that helped us out really well. If we had delayed that, it would’ve been a huge challenge to catch up once we went public. Because there are a lot of other challenges that you go through on the finance team once you are a public company.

What’s your top tip for preparing for an audit?

Make sure you have everything documented really well. And you document that in a few different ways. You want to know what your processes are at the time so that when you’re talking to your auditors, you’re not scrambling to try and figure out what you did six, nine, twelve months ago.

And also make sure that you document, in the actual work you’re doing, the review process that took place, how people were calculating things—very standard if you’re an ex-auditor, but it can be lost when you’re a small company, small team, growing really quickly.

I think in some ways we did that well early on in my company and in some ways we were challenged in that regard. So documenting processes was really critical.

What’s your biggest worry or challenge heading into 2024?

In the last couple of years, and I think this continues in 2024, it’s making sure that we are retaining our really strong performers on the team. The job market, especially in tech, is hot, and people are able to bounce from company to company. And when you spend a lot of time recruiting and hiring really good, smart, driven people, you want to make sure to keep it that way.

You don’t want people just doing kind of the same thing over and over, repetitive mindless tasks. So we’re thinking about how we can leverage some of the current technologies that have been emerging over the last few years to take away some of those repetitive tasks from our team. And make sure that we can put people on high value, really interesting projects that are going to keep them at the company even as we continue to scale.

We have been definitely trying to experiment with AI to try and reduce some of the time that we spend looking at a lot of data. We have a lot of transactions at our company and it can be really challenging to sift through some of that data. So we’ve been experimenting with some generative AI on things like accounting fluxes, both for revenue and operating expenses.

And it’s been good so far. There have been some challenges. You learn a lot about your data infrastructure underlying things like your journal entries or your subledgers, that can be either helpful or not very helpful when you’re trying to get some of those insights. And so we’ve also taken a step back and gone back to some of our underlying data infrastructure to say, if we want to use generative AI, we’re going to have to do X, Y, and Z. So let’s make sure that we set ourselves up over the next few months to be able to get those scalable processes in place.

So it’s been really interesting. There’s definitely a lot of potential, but it does uncover some things and there can be some challenges when you don’t have a perfect data infrastructure, which nobody does. ⊞

(For my thinking on where we are today: The fog of AI and what investors are missing)

An inherent assumption in my predictions is that we will see a step function change in LLM capability in the next release of models from OpenAI and Anthropic.

The possibilities

What are the ways in which the next generation LLM could augment a finance leader's workflow? If I were to distill the plethora of answers I’ve heard, there are three general directions this could take:

1. AI Product: Software companies utilize LLMs in product offerings

2. AI Agent: Users provide their LLM-powered AI agent with access to their software subscriptions

3. Custom software: Finance leaders write custom software with LLM-powered AI coding assistants

I notice people are overwhelmingly anchored to the first direction. Every CFO dinner conversation is a frantic exchange of what financial tooling one has tried, of little to no avail.

The third direction hinges on how large the step function change between models will be. Even in a superintelligent world, the principles of economics still apply. Economies of scale will still mean that most finance teams would benefit from using off-the-shelf software. Until we get to a post-scarcity world, Hal Varian’s Information Rules still provides a relevant blueprint on digital supply and demand dynamics.

My money is on the second as the primary way finance leaders see value in the near term. Now, we could still benefit from software companies incorporating LLMs into their products or see legacy software companies meaningfully disrupted by a 10x better AI-first upstart. What I’m trying to underscore here is that many of us aren’t considering the other possibilities.

The near term prediction

Imagine an intelligent agent that can log in to your NetSuite and Stripe accounts and reconcile transactions with your bank account. Like an analyst on your team, it can click into the different accounts, identify anomalies and discrepancies, and take screenshots to provide supporting evidence in your general ledger. It uses the same tools that you do today.

Similar to us, the LLM can document its work. Interestingly, when an LLM is asked to think through a problem step-by-step and show its work, the results are more accurate. AI researchers call this prompting phenomenon as “Chain of Thought” (Wei et. al, 2022).

Take this one step further. You spin up another copy of the AI agent to then check the other model’s work. It flags any areas for the manager (you) to review. This “acceleration of existing trends” future might seem less attractive than the “AI in all of our products” for many. But this is precisely why we underestimate this possibility.

To borrow a phrase from the former CTO of Microsoft:

“Many of the most powerful effects of the information highway, especially in the early days [like where I believe we are at with AI in 2024], will come by accelerating trends that are already well under way.” 

—InterOffice Memo: Road Kill on the Information Highway (1993)

What intelligent agents tell us about possible losers

When I first signed with Airtable in 2018, my then-boss Howie Liu shared a reading list before I started.  One of the books on the list was Engines that Move Markets by Alasdair Nairn. A quote that stayed with me is:

“When a new technology arises, it is a lot easier to spot the losers than to find the winners.” 

—Engines that Move Markets

With claims of GPT-5 on the horizon, the importance of having some mental model of industries to avoid cannot be understated. Every Fortune 500 CEO (and CFO) should, at the very least, have an opinion on where NOT to deploy their capital in a superintelligent world.

If one is bullish on possibility number two—AI agents augmenting the finance workflow—the losers are obvious. Any business that relies on cheap knowledge workers is at risk of disruption. The cost of quality bookkeeping, data cleansing, and IT consulting goes way down. If Tata Consulting, Infosys, or outsourced bookkeeping services don’t start leveraging AI assistants to augment their services, they may soon find their cost basis too high to compete. 

In some ways, if you aren’t already using software to automate such bookkeeping workflows, you are already at a disadvantage. Forget AI. For example, many fractional CFOs and accounting firms use Anrok’s software to automate nexus study analysis and augment their services. This is a win-win for both the firm and their client. Firms can provide faster and more accurate analysis to clients at a lower cost, and take on more clients or upsell strategic services in their free time.

There are qualities that make a business immune. Namely, businesses where prestige and authority matters. I am bullish on The Big Four and reputable accounting, audit, and advisory firms. Their prestige and authority are valuable intangible assets. Today, clients pay premiums for the legitimacy from a Deloitte, EY, KPMG or PwC seal of approval on their audits. This "reputational capital" constitutes a moat. Though new technologies may reduce their costs and provide new ways to compete for market share, the differentiation from the average IT consulting business lies in the signaling value of their brands.

The bottom line

No one really knows what the capabilities of future models may hold, but we ought to know what to avoid. The bottom line is that we need to couch predictions in existing trends and principles of economics. The tools that worked best over the last few years could still be relevant in the few years ahead. ⊞

When it comes to making mundane workflows and complex objectives like tax compliance feel seamless, LLMs allow us to take on problems that would otherwise be cumbersome and time-intensive. Multimodal models, and in particular those that take an image as input and provide text as output (we’ll call this a Vision model), presents a compelling opportunity for automation. 

Today at Anrok, we’re launching our first LLM-powered feature to do a small thing with big impact for our users: using Vision models to extract critical data from lengthy tax compliance documents.

What we’re solving for

Anrok provides an end-to-end solution for sales tax and global VAT compliance. Part of the challenge companies face with compliance is collecting and storing exemption certificates from their customers who are legally exempted from sales tax (typically non-profits, religious organizations, and government entities).

Hundreds of software companies rely on Anrok’s suite of features for exemption certificate management. However, there hasn’t previously been an easy way to extract relevant details from a given certificate—issue date, expiration, applicable tax jurisdiction, and so on—to store and record as discrete data.

Relying on manual input or even optical character recognition (OCR) technology left much room for error. This led us to explore how multimodal LLMs could improve the process of reading data from exemption certificate images to streamline finance teams’ workflows.

Why LLMs are more accurate than OCR

While tools and techniques like OCR exist for a similar purpose, they have a few disadvantages when compared to multimodal LLMs:

1. Contextual understanding

Traditional OCR techniques output a mangled block of text that can be hard for both humans and machines to parse. Compare the result of these two methods for a blank Texas Sales and Use Tax Exemption Certificate, as an example:

• Original document

• OCR result

• Vision model result

From the examples above, it is clear that OCR has become an antiquated technology. Beyond text extraction, Vision models allow us to focus on the relevant fields and interact dynamically with the data.

2. Flexibility

Traditional OCR typically relies on a set of standard document formats, such as a form where dates, names, and signatures are found in predictable positions on a page. This limits predictable OCR outputs to reading documents similar to those you designed for.

In our exemptions certificate use case, each state has their own document format (if not multiple) and non-standard text such as handwriting or images are prevalent. Governments can also change this document format at any time, without notice. The flexibility of an LLM to navigate around those elements and adjust to changing document structures is a huge win for predictability and accuracy.

3. Speed of development

Our team was able to build a prototype for extracting data from exemption certificates in a matter of hours with this new technology. This remarkable pace enables the team to focus on providing the best user interface and iterate quickly based on customer feedback.

Just the beginning

Our team initially envisioned this project as a way to experiment with LLMs—the new multimodal models in particular—and found both the process and result to be more effective than expected. We’re launching this feature today as a beta to help our customers reduce manual processing time and focus on the strategic problems that matter most to their business.  Given the speed and ease of building our first AI-powered feature, we expect there will be more to come soon. ⊞

While your board members have ample experience and useful perspectives to share on your company’s trajectory, it’s often easier to get lost in the weeds of last quarter’s results than have a meaningful discussion about your future strategy.

So what does your board really want to talk about this quarter? And how can you as a CEO or CFO maximize what you get out of your next board meeting?

To answer these questions and more, I sat down with Ron Gill, one of the most sought-after independent board members in the tech space today.

Gill has over three decades of experience in finance leadership at some of the biggest companies in tech, as former CFO at NetSuite, VP of Finance at Hyperion, and Chief Controller at SAP. Today, Gill sits on the boards of HubSpot, Amplitude, and Benchling, among others.

In this conversation, Gill shares his insights from time spent on both sides of the board meeting, including:

• How board meetings have changed in the past 30 years

• What your board does (and doesn’t) want to spend time on 

• Why you’re probably showing too many slides in the board meeting

Watch the full conversation to learn Gill’s best tips for nailing your next board meeting as a CEO or CFO. ⊞

Yet the art of investing relies on peering past the fog of today to glimpse tomorrow’s possibilities. Many of the best early stage investments were bets on markets that were just showing signs of forming. As a former investor, the question we often asked ourselves was: How big could this be if things go right?

Thesis investing requires the ability to see the present clearly, as well as the foresight to anticipate the possible steps that lead up to an amorphous—but potentially outsized—outcome. Without hard growth data, finding flaws and passing on startups is the easy task.

Take the typical venture fund model, where investors are tasked to lead only two to three investments each year. Assuming they have capacity to meet four new companies a day, this means not leaning into an investment happens 99.997% of the time. Passing is the easiest and statistically expected behavior.

What differentiates outlier investors from the pack is an understanding of one’s own epistemic confidence. Instead of seeing their assessments as binary—good investment vs bad investment—being able to recognize the degree of confidence in their conclusions and what would make them update their priors is key to a prophetic investment recommendation.

The current disillusionment seems to stem from an overconfidence in two places:

1. Our ability to forecast progress

2. Conviction that we’re looking in the right places

In Part One, I argue that investors—and even the AI experts—are bad forecasters. We have continued to underestimate the step-function changes between models.

In Part Two, I attempt to synthesize a model of where investors should be looking, both within and around AI. Most investors are looking in the wrong places.

In Part Three, I share how I’ve been thinking about where to invest, both for our own product opportunities at Anrok as well as my own portfolio. There is a lot that we don’t know, but there is more we can do to prepare.

Part One: Underestimating AI

When it comes to investing in AI, we face uncertainty on two fronts.

First, the pace of progress in AI is difficult to predict. The rapid advances in large language models (LLMs) that underpin many of the AI investing opportunities today have consistently surprised us. Second, it's challenging to imagine the impact AI could have on the economy if progress continues to accelerate. We have no lived experience with technologies that meaningfully speed up the rate of economic growth.

Overconfidence in our ability to anticipate LLM progress will lead to missed investment opportunities, both for investors as well as operators who are also internal capital allocators. By leaning on examples in our recent past, I attempt to illustrate how far our intuitions are from forecasting transformative progress and what we can do about it.

Why we’re likely overconfident in our ability to forecast AI progress

Predicting the future of LLMs is hard, especially when our familiarity with the technology is still in its infancy. The average tech worker in Silicon Valley first encountered LLMs less than one year ago. When OpenAI released ChatGPT in November 2022, they provided an interface to interact with a fine-tuned version of GPT-3.5: a follow-up to a language model that was published over two years earlier in May 2020. This upgraded 3.5 version was soon replaced by GPT-4 in March 2023.

It is no surprise that disillusionment is emerging now. No new generations of LLMs have been released since investors have habitually begun using the technology. Progress from the outside seems flat. Startups are building on models with the same capabilities to that at the beginning of the year.

However, LLMs were around prior to chat interfaces like OpenAI’s ChatGPT and Anthropic’s Claude. Without an easy way to interact with the models, it was unlikely one was exposed to the then capabilities of LLMs. Even when GPT-2 was published in 2019 - in the pre-covid era - researchers did not initially release the model weights. Few have truly experienced the capability jump between GPT-2, GPT-3, and GPT-4.

For most investors, LLMs have only become part of the everyday vernacular and workflow in the past few months. This is not an area investors can rely on intuition or experienced pattern-matching to predict progress.

To be fair, AI experts have also underestimated the performance and time-to-release of next generation LLMs. A comprehensive primer on this is Ajeya Cotra’s coverage on ML forecasting competitions and available performance benchmarks.

The short version is that superforecasters undershot reality so much so that actual capability improvements were well outside of the bell curve of consensus estimates. When AI experts were asked in mid-2022 to predict milestones like “write an essay for a high school history class,” their multi-year prediction was soon proven wrong a mere few months later.

The time between model deployments might be viewed as a trough for some. However, this lull is an opportune time to position one’s company, team, and data, to be ready to build the best products when the next generation of models arrive.

Part Two: Unlikely but possible

AI is the frontrunner to impact the pace of economic progress. Specifically, transformative AGI in the form of LLMs. Thus, LLM progress is an important variable in our future world view. An examination of how AI could impact our broader economy will reveal more about the environment that our startups will be operating in in a few years time and where the best investment opportunities may exist today.

Typically, progress comes in the form of gradual change. As the research team at Open Philanthropy puts it, most technologies don’t accelerate the pace of economic growth. They merely assist it.

The kind of progress that opens up real investment opportunities comes in the less familiar form of punctuated equilibrium. Some call these platform shifts. There is a chance that this is more than that.

The last time we experienced real change—let’s define this as the acceleration in the doubling rate of GDP—was the Industrial Revolution. This table from Bradford DeLong’s paper in 1998 examines how many years it took the global economy to double.

It took 200 years for GDP to double in the years immediately before the Industrial Revolution (1570-1765). In contrast, the doubling period immediately after took 40 years (1860-1900). This is a 5x speed up.

In the years since the Industrial Revolution, the doubling time of the economy has hovered around 20 years. This century-long trend has been so pronounced that economists have called this The Great Stagnation. Even investors who have seen multiple market cycles cannot claim to have experienced real change in the pace of progress.

So, what does this mean for an investor’s ability to forecast progress and pick winners?

Today’s investor has been placing bets in a static world. While technology has led to economic growth, it has not meaningfully accelerated the rate of growth in recent years.

Given this historical backdrop, how confident are you that progress in the next hundred years looks similar to the last hundred? Even if you’re skeptical of AI, a longer view of history suggests that the status quo is not the only potential reality.

If we continue to underestimate the step-function changes between models and take a narrow view of technological progress, our ability to pick the downstream (or upstream) winners looks more like a gamble than a calculated bet.

Part Three: Unexpected winners

Today’s environment for early stage investing is unlike any other. Whether you believe this is simply a long awaited platform shift or glimpse possibilities of more transformative progress, there has never been a more exciting time to invest in and build technology.

We’re being shortsighted with software TAM

Recent developments in AI are already accelerating software development productivity. GitHub’s CoPilot claims to increase developer output by 1.5x with just the models we have today. Besides expansion of the existing software market, AI will further digitize analog industries like customer support, consulting services, tutoring, art, and much more.

The impact of digitizing the analog is underrated. Many investor forecasts of TAM miss the mark on how big and how fast this shift is going to be. The TAM calculations I’ve seen from investors take existing AI revenue data (i.e. looking at sector productivity gains of current models) instead of working from looking at how large existing analog industries are.

The problem of relying on AI revenue today is that it has a short history and only considers capabilities of the current models (recall the step function changes in Part One). Despite reports that AI revenue generated by these players today are impressive and massive—The Information has reported that OpenAI is generating $1B in annual revenue—investors seem to forget this revenue was essentially zero a year ago.

To illustrate how big the outer circle of this could be, the consulting services market is about US$300B today alone (Statista, 2023). How much of that industry will be transformed by and perhaps even enlarged by the next-generation model?

The growth potential of these industries when coupled with AI is unprecedented. Investors still don't seem to be factoring in the speed at which revenue has scaled, perhaps dismissing them as outliers. In the short few months, there are product examples across different domains, such as AI chatbot, AI art, AI programming and more that have scaled from zero to hundreds of millions in revenue. Factoring speed into TAM projections produce some weird results that could make some investors uncomfortable.

Where to look

If I were to distill what is interesting about the Silicon Valley startup investing environment today into five general buckets, they would be:

1. Research-driven teams that have many years of machine learning experience, like Ilya Sutskever at OpenAI, Dario Amodei at Anthropic, Noam Shazeer at Character, and David Holz at Midjourney.
   These tend to be capital intensive bets that have high valuations out of the gate, which are hard for smaller funds to stomach. Here, you’re betting on the caliber of technical talent early and their ability to attract other top notch researchers. The good news for investors is that the best teams have a long track record of leading and publishing conference winning papers, a proxy for whether they can stay on the cutting edge.

2. Growth stage startups with a corpus of valuable data and a team that can innovate, like Jason Boehmig at Ironclad, Tim Zheng at Apollo, and Mathilde Collin at Front.
   LLMs are primarily good at two things: content creation and data synthesis. Arguably the latter is the precursor step to unlocking the most value. Even if your product is ultimately creating content, outputs based on some personalization and understanding of the user’s context tend to be more useful. It all starts with having proprietary industry or user specific data.

   Companies who have yet to ship a public LLM-powered product could still be an AI gem. The best product teams know that any products shipped to users today will require ongoing maintenance. While they might iterate quickly behind the scenes, they ship intentionally. God forbid they ship a simple chat interface product named InsertCompanyNameGPT…

   In addition, the capabilities of LLMs are prone to punctuated equilibrium (see Part One). Companies might need the next generation model to make their ideas possible. Hallucinations and the limited number of parameters might mean today’s models aren’t smart enough for the tasks at hand. Those who have access to the right data and sit in a strategic place in a user’s workflow are best positioned to win when the time comes.

3. Startups that were “AI” from the get-go, such as Glean by Arvind Jain, Forethought by Deon Nicholas, Moonhub by Nancy Xu, and Harvey by Gabriel Pereyra.

   This is the category that is getting the most investor attention. Many exciting applications have yet to be built around LLMs. There is room to build products around specific workflows and deliver a superior user experience.

   Similar to #2 of companies, access to or aggregation of proprietary data is paramount to building a defensible business. Without a data strategy, many startups will never get out of being a thin wrapper around an LLM.

4. The obvious picks and shovels on the path to transformative AI, like Scale by Alex Wang, Tonic by Ian Coe, Gantry by Josh Tobin, and Coactive by Cody Coleman.

   Infrastructure for model training, data, or deployment are classic derivative bets on AI. Opportunities range from chip hardware to model deployment platforms. Companies like Scale were ahead of the curve and outexecuted competition to lead the data labeling space. Other companies like Tonic AI take the synthetic approach to meet the demand for data. Identifying which picks and shovels deliver real and enduring value, coupled with the team that has the horsepower to execute, is a tried and true investment strategy.

5. The unexpected picks and shovels on the path to transformative AI, see the approaches below.

   This is the category most overlooked. As LLMs get smarter, every engineer can be transformed into a 10x contributor. Without development resource constraints, every startup can be a compound startup. Software will truly “eat the world”.

   Companies facilitating digital commerce are the hidden backbone of the rise of AI. It’s another approach to the picks and shovels / AI Index bet. Here is how I think about the different categories as of September 2023:

   • ❓Create software

     • I’m still not convinced that there is much within the “Create software” category.

     • Coding productivity does feel like something the AI assistants built by OpenAI and Anthropic will do well. Given how closely Microsoft / Github Copilot is with OpenAI and was built by the team at OpenAI, I group them largely in the same bucket. Maybe Replit has the best chance to do this well, but I haven’t looked into the company in detail since 2017.

     • Building / connecting different APIs also seem like something that can be easily disrupted by the next-generation of custom programming assistants. That said, if you’re unifying regulated private APIs that you don’t have access to as the user, like Plaid, Modern Treasury, or Column, I still think there is a moat here.

   • ✅ Manage, deploy, and secure software

     • Developer supply chain scanning tools like Socket by Feross Aboukhadijeh

     • Hosting platforms that make app deployment easy like Render by Anurag Goel

     • Cloud security that secures our most important assets like Wiz by Assaf Rappaport

   • ✅ Deliver software

     • End-user access controls and / or auth software like Multiparty AI from Indent by Fouad Matin

     • Procurement software that enables companies to organize and effectively purchase more software like Zip by Rujul Zaparde

     • Compliance software that enables companies to sell software across borders like Anrok (where I’m all-in)

   Above reflects my personal opinion on the categories that are interesting and is not intended as investment advice 🙂

It is easy to be overconfident in early stage investing where the universe of companies is large and you’re rewarded to be decisive. Most of the time, it pays off to move quickly. Identifying when a key input to our world view has changed provides an opportunity to step back, reassess priors, and recalibrate.

This three-part post has attempted to (0) address some of the herd mentality and disillusionment by (1) illustrating how we’re underestimating AI progress, (2) outlining how we’re thinking too narrowly about unlikely but possible futures, and (3) providing a framework on where to look for the unexpected winners in this AI environment.

At Anrok, we believe the Internet economy is only just beginning. We’re at our Wright brothers moment, where the plane looks nothing like where we might go in a short few years time. If you’re building something ambitious in the software space and could use help expanding across state and global borders, I’d love to connect. ⊞

My former Dropbox colleague Scott Woody and I both know from experience that your billing system is one of those core foundational problems to get right early. Both Woody, former Director of Engineering at Dropbox and now Co-founder and CTO at Metronome, and I went on to help build new companies focused on improving the finance stack.

In a recent conversation at the Ascent conference in San Francisco, Woody and I looked back on our time at Dropbox and shared lessons learned—from taking billing more seriously to building a better relationship between finance and engineering.

‍

Scott Woody: You joined Dropbox in 2011, when the company had just raised a Series B. As one of the first finance hires, what was the lay of the land?

Brad Silicani: I remember very vividly on my first day, my boss said, okay, we need to go get our initial  financial statement audit done. Why don't you go talk to the engineering team and figure out revenue first? So, of course I said, great, I know how to do this. My background was at EY before that, and I was well equipped for that job.

I walked over to one of the engineers and said, can you give me the report on our billings for the past three years? And I remember just getting this absolute blank stare. And having the feeling that I was bothering this person, and also that they had no idea what information to share with me.

At the time Dropbox had both a homegrown billing system to support the website, and was also starting to do some enterprise sales motions, primarily using Salesforce.

What I quickly learned is that no one had thought about what we needed to do from a finance perspective, which was fairly normal. But the information actually wasn’t even there for us to be able to figure out quickly what had happened.

And the primary reason for that is the homegrown billing system was architected to be a real-time database. So it always captured the latest thing that was happening for the customer, which was super important obviously for product and engineering. But on the finance side of things, we needed to know what had happened in the past.

We also quickly learned that the company had done nothing on the sales tax and VAT side things. By the time we got to solving that problem it cost Dropbox over $10 million in back taxes—money that we could have been using in other places in the business.

So it was a rocky start on day one, trying to figure those things out. And it took us about 18 months before we were in a position to get the database in the right place, as well as be prepared to move forward on solving some of those blocking and tackling issues on the finance side. 

‍

SW: As the finance team, what was your vision for what the billing stack was supposed to look like and how did you go about getting that built by the engineering team?

BS: I think our vision of the billing stack came from pattern-matching against what we had seen from our prior experiences and the other systems we saw out there.

Directionally, we knew we had to tackle a number of foundational projects to make sure we were compliant from an audit perspective, and from a tax perspective, to really set ourselves up to be able to help grow the company.

So for us that was a very clear vision—these solutions already exist, can you just build that for us? But that definitely wasn't a message that landed well with the engineering team. It was a challenging relationship for sure.

‍

BS: When you joined in 2013, what did things look like from your perspective? How was the relationship between engineering and finance?

SW: I worked as an engineer who was internally facing, building a number of internal tools. And the thing I would say about that company stage—this was a Series B company growing very quickly—is what you saw on the engineering team was this desire to focus on whatever the most impactful thing was, which at the time was to grow the user base.

And so you would get this really interesting dynamic between finance and engineering, where finance would ask for something and engineering was hard to wrangle and try to convince that this could be the most important thing for the business.

And so what we saw on the engineering side is that things related to billing, tax, accuracy, compliance, or auditing got serially pushed to the bottom of the queue. I used to joke that our billing system was an intern project, because what would happen is every year you'd have these big grand plans for what would happen on the finance and the engineering relationship, and then nothing would happen.

And then summer would roll around and a bunch of interns would show up and get put on it. I remember that even just supporting annual billing was an intern project that instantly minted like $10 million. But the only person who was willing to work on it was an intern. 

I think it was kind of emblematic of this interesting tension for a fast-growing company, where engineers want to work on whatever the most interesting problem is, and it's rare at that stage that people are able to convince them that it could be finance.

Over time that changed. But definitely in those days it was like pulling teeth. You had to basically become good friends with an engineer and then bribe them into working on your project.

‍

BS: Do you have tips on what finance teams, particularly in those early stages, can do differently in terms of engaging with engineering?

SW: The first thing is understanding how engineers’ brains work. Engineers are attracted to problem solving. And so if you come to them and say build this, it's almost going to have the exact opposite effect.

Whereas if you say, here's this problem, I have no idea how to solve it, it opens it up to them to be a creative problem-solving exercise. That’s really important framing.

The second thing that I think engineers really, really care about is impact to the business.

And so if you say, we need to do tax because of money reasons, engineers may or may not be motivated by that. The way you need to do it is say, if we do this, it enables the company to do that. It's critical for us to properly bill our customers so that X, Y, Z can happen.

I find that the right engineers are heavily motivated by impact, and you have to message-test against what an engineer cares about. At the end of the day, they're super autonomous, very creative people, and people who ultimately just want what is best for the company, especially at that stage.

So you need to convince them that your project is on par with scaling up the company or whatever. Ultimately the good thing about billing and finance is that these are really hard technical problems at the end of the day.

‍

‍

BS: As we continued to grow, on the finance side we were really pushing on using external tools, but the ultimate decision from Dropbox was to continue building an internal billing system. I'm curious how that build-versus-buy tension felt from the engineering side?

SW: Finance is an interesting thing. You mentioned that there's a lot of prior art in the world about the right way to do billing or revenue recognition. At Dropbox we were very in favor of building, not buying, and I think that’s partially a consequence of the timing. In 2013, 2015, there really weren't great solutions available, and the enterprise landscape for software businesses was not very mature.

But in retrospect, the way we approached the build was taking whatever problem we had in 2015 and solving for that moment, but not fully anticipating how things would change in the future.

A great example is when we started internationalizing. Our initial pricing on the website was $9.99. But on the backend in the database, it was literally coded as 9.99 without a currency attached to it. So when we wanted to launch in Japan, we would be charging 9.99 yen. But yen isn't even a fractional currency—so it just wouldn’t work.

It was a situation where engineering, in their infinite wisdom, built a system that worked really well for this point in time, but didn't anticipate the future. And that led to this herky-jerky evolution of the product, and it caused a ton of pain.

I would say part of it was that tool availability wasn't there. Part of it was the maturity of the business—like, we never hired billing subject matter experts until we were pretty close to IPO. And then part of it was, I think, a natural consequence of growing really fast.

‍

SW: Did you feel the relationship between finance and engineering start to change as we approached IPO?

BS: Definitely, around that same time you’re discussing. I think it was about 2014, which ended up being four years before Dropbox went public, when we started thinking about preparation on the finance side.

We knew that to be successful in becoming a public company, we needed to have a good controlled environment to make sure that we could successfully pass an audit in year two after the IPO. And we knew that we needed to have a lot of confidence in the forecast. We needed to know that we had reliable reporting, not only on revenue, but on the key metrics that would drive revenue—to allow us to have confidence in setting really aggressive forecasts, knowing what the levers were to achieve those goals.

And I think that was the starting point of an evolution of the relationship with engineering where we started to get some traction on moving the ball forward on some key projects. 

‍

BS: What did that look like from your side?

SW: It was clear in 2015 that we needed to take this stuff more seriously. Beyond the finance perspective, I also came at it from the growth side where I was in charge of monetization for new product lines.

And whenever we wanted to test out new pricing or something like that, we’d have to file a ticket with the engineering team responsible for the billing system. Four months later an A/B test would go live and we'd realize we completely screwed it up, and then we'd have to wait another four months for that billing system to get updated.

So that super slow cycle time of pricing experimentation put more pressure on the billing team. At the same time that we were trying to build better data systems for finance, we were also trying to make it easier to change pricing and do experimentation. And the combination of those two reached a breaking point around 2016.

The engineering team realized, we need to reset everything that we're doing and they surged staff on that team. It went from 3 or 4 people to 45 in a couple months. And if you know anything about engineering, you know you can't actually solve the problem that way—you can’t just throw more engineers at the problem. And sometimes it makes the problem worse in the short term.

I remember some of the engineers on the team were some of the best engineers at Dropbox. And they spent years trying to make progress, unwinding a bunch of decisions that were made in 2009, and it was very painful for the business. It definitely slowed down the IPO process and I think it explains why engineering essentially had no time for finance. They were too busy trying to keep the system up and kind of rewrite it from scratch.

‍

BS: Did that change after we got through the intensity of the IPO?

SW: Over time, Dropbox started to realize, well, we don't need to build everything. We can start to offload more and more of this stuff to third-party services.

They also hired sufficiently senior engineers who could say, here's the subset of things that Dropbox is going to own, like the user experience. And then there's certain parts of it that we're just never going to do, like payment processing. They started to have a more mature outlook, I would say, that we don't need to build every single last thing in this stack. And we can actually outsource large parts of this infrastructure to third parties.

And that's a long journey. It's taken them 10-plus years. They're still in the midst of it today. But that was one of the big maturations of the business.

‍

SW: How did things change on the finance side as Dropbox went public? What were the things that changed about your relationship with engineering and product and the rest of the organization?

BS: We definitely got more leverage on the engineering organization. Prior to the IPO process, the financial organization was like a support organization for the business—we make sure that the billing’s okay, the revenue forecast is there.

As you switch to being a public company, you almost move towards the forefront—now we're starting to call the shots. Now we're saying, if we want to get to $2 billion in revenue, here are the gaps. And here are the things that we need from engineering to be able to make that happen.

So in terms of prioritization things worked more effectively. But as you described, there was still a lot of technical debt to work off. The impact of that on the finance side of things was that we had to pull back a little bit in terms of how we could go to market and talk to investors about the results that we could achieve. 

I remember very vividly one of those limitations being pricing and packaging related, where we wanted to start launching new SKUs that we could bundle together, but there was a clear limitation in the billing system.

BS: If you were to do it again, what would you do differently?

SW: If you're on the engineering side, the key thing is figuring out which parts of the billing and finance stack you really want to own. And then try to make it work with external tooling wherever you can.

In the beginning, all finance and billing problems can look really simple, but as your business grows, they complexify very quickly. And so it can be very attractive to solve it in-house, but I think it’s a more measured approach to solve some uniquely valuable things in-house, and outsource others to third parties.

You want to put your engineering resources on the subset of things that will uniquely drive extra value to you and your customers. ⊞

Luckily, there’s a very simple way to start getting familiar with AI: chat with it.

AI assistants have exploded in the past few months, with OpenAI’s ChatGPT making a major splash in late 2022. Spending some time with an AI assistant can not only help you understand the current strengths and limitations of AI technology, but it can actually help you do your job more efficiently today.

We asked the AI assistants of two leading LLM providers—OpenAI’s ChatGPT and Anthropic’s Claude—a few questions to explore some easy ways for finance leaders to leverage AI today.

Where AI assistants excel today

Writing and outlining

AI assistants are great at helping break down complex topics and suggesting ways to present information clearly. Try asking for help outlining written documents, slide decks, or presentations.

Prompt: I’m putting together a board deck for my investors. What are the important financial metrics I should show as a growth stage company?

Giving advice

Sometimes a fresh perspective can help with problem-solving, especially when the situation is stressful. AI assistants have access to a wide range of example solutions to various problems, and just might help jog a suitable approach to resolving yours.

Prompt: I’m having trouble with a few people at work. The sales team keeps wanting more headcount, and since I’m the finance leader, they are always coming to me for more budget. I can’t give them more budget until the business starts showing results. Meetings are getting tense and adversarial. What should I do?

Simple calculations

AI assistants are great at performing simple calculations, and their memory of previous questions makes them more than just a glorified calculator. Try working through a multi-step problem or running through different potential scenarios.

Prompt: If I sold $78,000 worth of software in Texas and the sales tax rate is 8.25%, what is the sales tax that I need to collect from the customer?

Prompt: What if only 70% of those sales were taxable?

What AI assistants aren’t great at (yet)

Searching the web

Depending on which AI assistant you choose, a more or less recent set of data may have been used to train the model. ChatGPT and Claude aren’t yet connected to the web in real time, meaning they might share information that’s a bit out of date.

For example, Maryland updated its sales tax laws to exclude B2B SaaS transactions as of July 2022. It’s advisable especially in situations with legal ramifications to do your own research, or consult an expert. Both AI assistants explicitly call out that they do not provide legal advice.

Prompt: Does Maryland tax B2B SaaS transactions?

Visualizing data

ChatGPT and Claude aren’t yet able to ingest files or output visualizations, but it’s reasonable to expect those capabilities in the near future. In the meantime, try prompting your AI assistant to give you some direction on creating compelling visuals to get some inspiration.

Prompt: Here is a CSV with my financial data for 2022. Can you help me create a graph that helps convey the information and unlock new insights?

The capabilities of AI assistants are expanding almost daily. Engaging with them is an easy way to keep tabs on the rapid progress of AI technology—and hopefully, save you some time on everyday tasks. ⊞

In a recent conversation with Ajay Vashee, a General Partner at IVP and former CFO at Dropbox during my time there as Treasurer, we talked through some best practices for startups in a post-SVB world.

Read on to get some takeaways from the conversation, and learn how startups should approach cash management, bank diversification, investment policy design, and asset allocation.

Strategy 1: Diversify across multiple banks

Ajay Vashee: Most venture-backed companies have managed their cash with a single bank, and that hasn't been a major issue for the past 5, 10, 15 years. Over the past two weeks, everyone has kind of been scrambling to open up new accounts. How will the collapse of SVB change the approach that startups take to banking diversification going forward?

Brad Silicani: I think collectively we forgot that the banks were credit counterparties and that when we put our money with the bank, we're really relying on them to pay us back, and we have to have a lot of faith in that. 

Over the past two weeks, we've had conversations with a lot of different companies wanting to diversify. And that means diversifying across two fronts: First, diversifying your credit counterparties to make sure that you always have people who are going to pay you your money back.

And second, which is equally as important, diversifying your execution risk. Over the weekend following the SVB collapse there were many people in scramble mode to make sure that they were going to hit payroll.

It’s important not only to make sure you can have different credit counterparties, but different points of execution to make sure that you can always move forward with your business.

One trend that we're seeing is diversification between a large commercial bank and a smaller bank. I think both have strengths, and long-term there's a very strong place for a smaller bank like an SVB, and there are many of them. They do have a lot more flexibility and can move with a lot more speed than those larger banks can. A mix between those two is what we're seeing and an appropriate strategy for managing risk.

Being able to put some strategies in place to diversify will help in these situations, and allow you to manage risk and feel a bit more comfortable heading into challenging times.

Strategy 2: Design a thoughtful investment policy

AV: Imagine I'm a startup CFO that's just raised a $50 million growth round. I've now diversified that cash across a larger commercial bank and a smaller regional bank. What should my key objectives be when designing an investment policy?

BS: When thinking about an investment policy—and this also applies to thinking about your cash in general and liquidity—there are four main goals you should be trying to achieve.

First and foremost is preserving the money that was given to you. Investors invested in your company and gave you their money, not because you're really great at investing and generating yield. They gave it to you because your team can build great software.

Your job as leading the finance organization is to preserve that, so that you can make a big bet on investing in your R&D teams and your sales and marketing teams to grow. So your number one goal here is to preserve that principal.

Second is to meet your liquidity needs, making sure that you're always in a place where, at minimum, you can execute on payroll. And a good example to think about here is, not just executing payroll, but executing something like an M&A transaction.

If your CEO calls you and says, “Hey, we need $100 million or $50 million to acquire this really great company,” you're probably not going to have much time to get those funds together. So think about that in your approach to liquidity preservation.

Third, you can think about delivering some yield. Particularly with interest rates moving up over the past year, there is an opportunity to make a little bit. That's not the primary goal, but you can pick up 4–5% right now just based on money market fund investments.

Fourth, you want to avoid concentrations of risk. And you should do that at every level. Do it by diversifying the banks that you keep your cash with. Do it by diversifying the money market funds that you're invested in. And if you do have a short duration portfolio, diversify and avoid concentration there.

Your goal should be that if the next SVB happens, you look at your portfolio and have less than 10% of exposure.

Strategy 3: Accept a practical level of risk

AV: Got it. So now I have this investment policy in hand, prioritizing what you just walked through—principal preservation and liquidity. And I'm trying to thoughtfully manage to some yields in the current interest rate environment. The last two weeks have taught me in many ways that I should be wary of holding cash beyond the FDIC insured limits. How much cash should I feel comfortable holding?

BS: You're not managing your money to a completely risk-off scenario. Taking a little bit of risk and going above the FDIC limit is reasonable, and also practical.

You're a financial organization—maybe you have nobody on your team, maybe you do have a team of 40 people. Likely even at the 40-person level, you don't have a treasury team who's executing on daily transactions. 

Either way, think about keeping one to three months cash burn in a checking-type account. The more you have confidence about your cash flows, the lower you can go on that number. Take the excess funds and put them into something else, like a money market fund.

You're going to take a little bit of risk on the FDIC side of things, but that's going to allow you to focus on the rest of your business: improving your top line, keeping your sales team focused and hitting the numbers that you need, and managing spend in your organization. ⊞

I sat down with Mark Goldberg, partner at Index Ventures, to discuss how companies and founders are rising to market challenges in 2023.

Watch the full conversation to hear Goldberg’s insights on the current state of the market—and his advice for founders and operators navigating the startup landscape in 2023.

Transcript

Michelle Valentine: Mark, you’ve been a partner at Index Ventures for a long time now. You’ve had a front-row seat to startups and watching the market change, and now’s a really strange and weird time. What is happening in the markets today?

Mark Goldberg: I’ve been doing this for seven and a half years, and this is an environment like I’ve never been in before. We’re at a point in the cycle where we went from “grow at all costs” mode to a real focus on quality and durability.

It’s amazing how fast the pendulum has swung from one extreme to the total other side of it, and it makes it really fun and exciting to be an investor right now.

MV: I remember you gave me the advice—and so did many other investors back in March or April of 2022—that you’d need 36 months of runway as the market started to change.

Now, in January 2023, I feel like people are saying the same thing and that maybe that 36 months wasn’t quite enough. Why are we still in this? What’s going on?

MG: It’s not that it wasn’t quite enough, it’s just—you now also need 36 months. So every month you’re going to need 36 months of burn.

No, I think that we’re all trying to figure out the new landscape, and the reality is most companies don’t want to go fundraise in this environment. And so when we say 36 months as a VC, it’s not necessarily precisely 35 or 37 months. The idea is you probably wanna avoid fundraising in the next 12 months if you can.

Now I think that when you go to market for a fundraise, you also probably want more than a year of cash so that you really can control your destiny and don’t feel like you’ve given up leverage in that process. And what does that mean? You need to think carefully about how you’re spending and controlling burn.

My sense is I’m just continuing to see great founders operate in this environment. And I think what this environment is forcing is: People that are really good are getting challenged in a way that’s making them better.

Or we’re realizing that some of the companies and some of the teams that started maybe didn’t have an idea that was as strong as they thought, and that’s okay. We’re going to see a shakeout that will ultimately make the entire industry stronger and more resilient.

MV: Yes, I definitely feel, as a founder, I’ve been challenged in a different kind of way and it’s something that I’m hearing from other folks in my cohort about how they’re thinking about fundraising differently, and thinking about company-building differently.

For example, I’d say pre-market crash, the venture model was the default market model. You raise almost every year—some folks would raise twice in one year when the market was hot.

And now I’m hearing from founders that some companies are profitable and they want to continue growing that way instead of going after venture dollars. And if you do have the capital and you are pretty efficient, that’s great—to your point, control your own destiny, be able to continue pursuing growth, the VC model, but with perhaps a little bit more constraints.

‍MG: I think one of the really exciting things going on right now is that the well-trodden path that existed for businesses over the last 3, 4, 5 years no longer looks so obvious.

And while that’s probably intimidating, it also means that you can trail-blaze and do a lot of things that would’ve been seen as negative signals in the market in the last few years.

So if you decide to operate towards profitability, that probably wouldn’t have been looked on favorably in the climate of the bull market of 2021, 2020. Now, I think it’s a very valid choice that many companies are making, and I think we’re going to see the exciting breadth of decisions around how people decide to run their businesses now

One of the things that must be exciting for you as a founder, as an operator, is the fact that you’ve also been on the dark side of investing. So how did that previous experience inform some of the choices you’re making now as you guys are heading into a very different environment?

MV: Good question. I think the investor experience does make me a little bit more skeptical—or scrutinize every piece of fundraising advice that founders might receive.

There’s a lot of hype around how in 2020 to 2022, VCs raised a lot of money. There were multi-billion-dollar funds being raised, and that capital hasn’t necessarily been deployed yet. People are calling this “dry powder” and saying that in 2023, there might be a fundraising avalanche where, as a founder, it’s great. You can actually go out and fundraise despite the market environment being a little bit touch-and-go right now.

The investor experience made me skeptical. Does that “dry powder” actually exist? And I think it’s more complicated than it seems. I think about, okay, how do I make sure that Anrok is burning in a way that we can go fundraise when we want to fundraise and not have to rely on the market being good and trying to time it?

‍MG: It’s a great time to be having this conversation because I think every company I’m working with is planning for 2023 and the level of attention on quality is the unifying theme across all my businesses.

So a year or two years ago, the question would’ve been: How fast can we grow? That really would’ve been the main driving question for the businesses we were working with. And that conversation has changed. Now it is: How do we create a business that has durability, sustainability, and a focus on metrics that make sense for this business? And take something that’s working before we really start to scale it.

How are you thinking about the process for planning for 2023? A lot of uncertainty, a lot of excitement. How are you taking all those things and synthesizing it into a plan for the year?

MV: After doing this a few times, I think I’ve found the playbook that works well for Anrok, and that is starting with a narrative.

And I think by starting at that level, you are able to tie in all of the different departments and then focus your financial forecasts, focus your goals in a way that is much more tops down than in the past—where it’s a lot of siloed teams coming up with their own plans, giving those forecasts to finance, and then finance trying to build a model, and then coming back after the board meeting and that investor saying: That’s not aggressive enough, or this doesn’t make sense.

So what I’ve found works is starting with the narrative, and then holistically coming together with the right building blocks and the right financial inputs to build higher-level revenue goals.

‍MG: You use the word aggression. And it’s funny because I think a lot of times in this environment people are thinking: I have to be as conservative as possible.

We need to control our destiny, we need to be profitable. And I think that misses this opportunity to maintain a level of aggression, even if it’s within capital constraints. So a lot of the board conversations I’m having around planning are: What’s the one bet that we want to make this year?

MV: Something I’ve been thinking about a lot is: How can we experiment faster and fail faster to figure out the most efficient way to use our marketing dollars, for example, most efficient way for helping sales ramp up and learn, the most efficient way to make sure we’re building the products that customers actually need and see value in.

Now is the time to build in iterative processes where you can learn quickly.

MG: In the last few years we saw a lot of times that when there was a great idea, ten companies would seize that idea and go start the same business. I think this environment is gonna shake that out. I think we’re going to see one, two, maybe three companies emerge on the other side of this as winners.

Those companies, instead of fighting amongst themselves, will be able to approach very wide-open markets with a lot of runway and a lot of momentum. The survivors of this time have a very bright future in front of them. ⊞

This planning cycle is different. There’s the old way and the new way to plan.

The old way of planning was a relic of abundance. Capital was cheap and the next fundraise was around the corner. Teams were free to hire as they wished and dictate their spend. Finance was the glue, picking up the pieces and magically foisting the plan together. The new way is where finance takes the driver’s seat in planning.

Finance takes the wheel

Unlike previous years, functional teams within startups are faced with real constraints, and it’s up to finance to set those constraints. Finance provides the scaffolding for the teams to each build their plans.  

While complexity of planning differs depending on your company stage, a good general formula to follow is:

1. First, craft the narrative—for the next quarter, year, and two years—with the CEO.

2. Next, outline and group your company’s operating functions to determine which teams need to be planning in conjunction with one another.

3. Finally, get the teams to define goals that roll up to the unified plan.

If you’re a finance lead at a SaaS company, try this approach to get the inputs you need and build the financial model and scenarios right the first time. Then, think about how to minimize the risk in your model (more on that below).

Start with the narrative

Regardless of what size of company you are, you need to have an overarching narrative for where you’re headed. This should be the starting point of any planning cycle. It might make a quantitative finance leader uncomfortable at first, but it is important to point the teams in a shared direction before jumping into the numbers.

A narrative should include clear vision for the future and concrete milestones to get there. 

The vision statement should summarize the highest-level outcome the company wants to create in 1–2 sentences. 

Milestones should be broken up into time-based chapters, ideally covering where you’ve been, the next quarter, the next year (i.e. 2023), and the next two years. For each of these chapters, have the CEO describe what will be built, how you’ll go to market, why you will win, and potential risks. With a strong vision and well-defined milestones, teams can work backward to develop focused plans for the upcoming year.

At Anrok, our company narrative going into 2023 is organized like this:

Company narrative

Where we’ve been

• Past 12 months

Where we’re going

• Next 6 months

  • Business goals

  • North stars

  • Risks

• Long-term strategy

  • Phase 1

    • What we’ll build

    • How we’ll go to market

    • How we’ll win

    • Risks

  • Phase 2

    • What we’ll build

    • How we’ll go to market

    • How we’ll win

    • Risks

Define the operating groups

The main operating groups at a SaaS startup are typically finance, sales, product, engineering, design, and marketing. Each of these groups is interdependent with the others, and some need to define their goals and operating cadences in conjunction with one another. 

For example, sales targets cannot be set without hiring plans, but hiring plans cannot exist without sales targets and revenue estimates to balance against. This is why certain groups (like sales and finance, in this case) need to have a synced operating rhythm, where goals are set and revised based on performance. 

The marketing plan also affects sales, but marketing also needs to be in lock-step with the product team in order to effectively drive acquisition and adoption. In this sense, product initiatives and engineering targets will define your marketing plan as much as sales targets will. 

Each of these groups will of course need to set its own functional team goals, but finance must be the connector to make sure they all fit together. 

Set accountable goals

When it comes to setting goals for each function, standardizing the way in which the team sets goals will help build the financial model. As a finance leader, you should help other functional leads set clear goals that keep them accountable to company metrics. 

A clear goal has the following ingredients:

• A target that you want to reach

• A baseline that identifies where you are today

• A trend that describes anticipated velocity

• A time frame that keeps the team accountable

Finance should push sales, marketing, and product development to have goals that fit this framework and that can be measured regularly over the course of the year. 

Checking in frequently to see if you are pacing to hit your target is important so that you can adjust any dependencies in the plan as needed. At Anrok, we check in on sales and marketing targets weekly, and adjust the financial model every quarter, or as-needed if we see unexpected performance. 

De-risk your plan

Of course, even the best-laid plans are full of risks, especially in times like these. Interest rates—which is arguably the most influential factor in average software valuation multiples—continue to increase as the Fed tries to manage the highest inflationary environment of the last 40 years. The most savvy finance leaders are extending runway via prudent planning to avoid raising at the trough of the market.

For 2023 planning, it’s more important than ever to identify the risks in your plan and address those that you can easily mitigate. Depending on your company stage, you can do some quick and dirty “t-shirt sizing” (small, medium, large) of the risk and the corresponding cost to mitigate it, or you can start to actually quantify risks in terms of dollars. 

Risks to your plan could include people risks, financial risks, and reputational risks. As a finance leader you should understand all the monetary liabilities that may exist, including things like—our favorite topic here at Anrok—your sales tax exposure. 

When your company narrative, operational targets, and business risks are mapped out with a financial framework in mind, your 2023 plan will be more accurate. Your board will thank you. ⊞

The premise is that venture funds continue to raise at record rates. While public markets saw a significant sell-off in the last six months, venture funds raised an unprecedented $261 billion. The rosy picture is that this capital build-up will lead to a flood of investment activity.

I’m here to burst that bubble. The dry powder does not exist. 

Here’s why.

Follow the money

When venture firms raise investment funds from Limited Partners (LPs), the LPs commit to an allocation in the fund. The actual money changes hands in a series of capital calls.

A capital call is a legal right for these venture firms to demand a portion of the money agreed upon at the time of the fund’s raise by their LP investors. This reflects the reality that not all the capital can be deployed at once. It takes time to find the diamond-in-the-rough startups to invest in.

Typically, the funds are distributed from the LPs to the fund in thirds over the course of the fund’s lifetime. So while an endowment might commit $50 million to a fund, they would only wire a fraction of the money to the fund in the first few months. The remaining $33 million is called and wired at intervals over the next few years.

Calling capital only when required allows LPs to maintain control of their capital for longer and continue to earn returns on the capital through short-term investments. In addition, the capital call math conveniently produces a higher internal rate of return for the venture fund. 

So what does this mean for the billions of dollars raised by venture capital firms in the pandemic? Only a fraction of the committed capital is actually in the bank and under the firm’s control. Optimists are assuming LPs will actually wire the remaining funds.

They will… won’t they?

Limits to liquidity

For LPs to meet their capital call obligations, they must have the liquidity to do so. 

The question of liquidity is opaque. The world is looking pretty weird right now, with some of the biggest exchanges struggling to meet withdrawals and disappearing literally overnight.

It wasn’t that long ago when we experienced a liquidity crunch on a mass scale. Let’s look at what happened to capital calls in 2008. 

Spoiler alert—the capital was not actually there.

When the market crashed in 2008, so did many investment holdings. The S&P 500 lost more than 50% of its value from the peak. Some investments went to zero. 

Remember those LPs who still had two-thirds of their capital obligations to venture firms outstanding and decided to invest in short-term holdings to earn interest in the meantime? Their short-term holdings are now worth a fraction of what they originally expected. They may be forced to sell positions at a loss. In some cases, they simply may not have the capital to commit. 

Like venture firms, there is a spectrum of the caliber of LPs. For tier-one venture firms who have built up relationships with LPs over decades, the agreements could be more ironclad. For the 12,000+ new firms that came into existence in the last six years, it is less clear how well-managed and what pressures exist to salvage their investment positions. 

What happens next?

Of course, there is a secondary market for this. Back in the dot-com crash and the Great Financial Crisis of 2008, pawn shop investors stepped up to buy secondary holdings of venture funds or LPs themselves. However, these pools of capital are limited and prone to similar market exposure.

Other alternatives include borrowing money at soaring interest rates from banks. Or larger LPs may have the power to tell the fund not to call capital at all. Neither party is incentivized to make this information public because it makes other LPs lose confidence in the venture firm and LPs likewise don’t want to be perceived as being unreliable.

The pressure from LPs to deploy funds as fast as possible (and reduce the fees) has been replaced with patience. Fund cycles that were 18 months in a bull market could now stretch past the typical 2–3 years to possibly 4. New investment activity could continue to decline, or even freeze.

Frankly, it’s foolish to predict what happens next. All that history indicates is that some of the perceived dry powder may be more prone to evaporation than a fundraising avalanche. The question is what percentage of the publicly announced funds will remain. Continuing to extend runway from the coldest summer in the valley to ride out the possible winter is not a bad idea.

As Howard Marks once said, bull markets rhyme. Maybe prior bear markets also have something to teach the optimists. ⊞

The panelists share their experiences and recommendations for founders, first-time finance leaders, and startup operators at any stage.

Watch the conversation to learn:

• How do you decide which risks to tackle first?

• What are the biggest risks founders and finance leaders face at every stage of company growth?

• How are companies shifting their approach to risk in the 2022 market?

• Where is the balance between mitigating risk and taking enough risk to be successful as a startup?

As a result, CFOs these days are on an ongoing hunt for tech tools that can enhance finance workflows by automating processes and enabling collaboration. Doing so helps finance teams better meet the demands of the strategic role they are increasingly taking up in today’s companies.

The financial stack is evolving

Finance is seen as a strategic function in today’s businesses; CFOs and their teams are expected to support sales to close deals and to advise C-suite leaders on financial repercussions of important decisions. Tapping into evolving tech resources is essential for CFOs as finance takes a more central place in company operations.

The spreadsheets that have typically been put to use for finance workflows like annual planning, quarterly reporting, and reconciliation lack automation and collaboration features. While spreadsheets certainly have their place, modern-day CFOs know that other tools, especially more integrated and collaborative systems, are sometimes necessary to ensure accuracy and provide essential analysis.

As new tools come online, CFOs have the chance to transform workflows from manual and disconnected to automated and collaborative. And as a result, the CFO tech stack is in a state of constant evolution, getting better and more helpful all the time. CFOs are able to tap into resources that suit their unique business model in order to grow revenue fast.

For subscription-based businesses in particular, tech is an essential element of finance teams’ success, as the processes they must handle are more complex than retail or traditional software licensing. As these businesses increase in number, so do the tools that serve their particular needs.

Automation is key in finance software

Finance teams are busy, as they must keep up with many competing priorities, all of which require handling data. Automation can vastly speed up processes, especially those repeated demands that can take up large amounts of employees’ time.

Considering the high expense of headcount in today’s market, helping employees do more in less time is imperative. By automating rote tasks, software tools help finance team members devote more time to higher-value work that helps push the company forward. Tools can also reduce the ramp time required when onboarding new employees that may not have worked with usage-based billing, sales tax, headcount planning, or the many other tasks at hand.

Another advantage of automation is accuracy; less manual intervention leads to cleaner data. Accuracy is paramount for finance teams, especially when their work contributes to executive decision-making that can have major repercussions. Automated tools reduce errors without requiring extra time and energy from the finance team.

The modern finance stack, by company stage

The finance tech stack by necessity must evolve as the company grows. Processes that can be handled by accounting software and a few spreadsheets at one stage may become unwieldy in the next level of growth.

Here’s a run-down of how the CFO tech stack may evolve as the company grows and some of the best tools for the job at each given stage.

Startup

At this stage, companies often outsource finance or have an ops person in charge, are sensitive to price, and look for ways to manage repeatable processes. Some of the tools we commonly see at this stage are:

Growth

At this stage, an in-house finance function may be in place, and that person or team may prioritize ease of implementation, scalability, and the ability to centralize data analysis. Some of the tools commonly in use at this stage are:

Scaled

At this stage, finance teams most value collaboration capabilities, auditability, and automation. The most highly valued tools in these environments boast end-to-end functionality. Some of the tools commonly is use at this stage are:

Evolution is ongoing

The above listing of useful tools is a point-in-time snapshot; this will surely change as finance continues to take on a more strategic role and as software vendors continue to improve their offerings. The opportunity for CFOs to enhance their tech stacks and get their tools increasingly dialed into their teams’ needs will only grow as the software environment evolves.

Companies that are ready to invest in finance software that boosts performance, improves efficiency, and enhances data-driven decision-making ability will find that their finance teams are more functional and valuable than ever before. All the more so if they are ready to iterate repeatedly over time as the CFO works to evolve the team’s tech stack to be the ideal FinanceOS for the business. ⊞