Tech companies are laying off employees. Layoffs.fyi shows 1,000 companies laid off ~152,000 people in 2022.

Fundraising has become more difficult. According to Tribe Capital (A Record Shortage of Venture Capital - Tribe Capital), there 1/3 of the capital that will be asked for at Series B, for example. Cooley also released their venture trends Trends | Cooley GO showing a major reduction in financing at Series A and beyond.

Summary

• Low interest rates made venture capital a much more attractive asset class, funneling more money into VC and by consequence startups.

• Two consequences:

  • Investors valued startups based on higher revenue multiples. With more venture dollars, financings were also more competitive so investors pre-empted rounds ahead of round specific de-risking milestones. This put further pressure on the true limiting factor to growth, which is talent, sending talent to the wrong companies while also increasing salaries.

  • Startups were being rewarded for growth with very high revenue multiples. Every initiative and new hire could meet the hurdle rate, instead of just the very best projects and hires, resulting in suboptimal use of capital. Startups most aggressively growing could raise follow-on capital, often within months, at higher valuations.

• With rates rising, projects and hires that were justified no longer meet the hurdle rate, leading to cancelled projects and layoffs.

• What should you do?

  • Reset mentally. Treat capital as extremely precious. Rank order every product and revenue investment and focus on those directly tied to PMF or most likely to accelerate new revenue, retain customers, or drive revenue expansion. Read Everything is a DCF by Michael Mauboussin

The expected Reward in Risk vs. Reward is set by the Fed

• The economy is largely influenced by monetary policy, which is the setting of interest rates by the US Federal Reserve. This is the rate banks can borrow from the US government and is considered 'risk-free' because of the low risk of the US government defaulting on its debts. We borrow money to invest in projects that create economic growth. The higher interest rates, the higher the bar a given project must meet to justify an investment.

  • Here is the historical data for the 10 year risk-free rate. The reason I'm showing the 10 year treasure for the purposes of the risk-free rate is because when investing in a startup (or venture fund), a typical time horizon would be 10 years. If you were investing in a project with a shorter (or longer) time horizon, you would use a different risk-free rate.

    

• In 1980 for example, you could get 16% by buying and holding US 10 Year Treasuries. Think about that for a moment...when the long-term average of the S&P500 is 7-8% per year.

• Investors compare other investment opportunity to this risk-free rate. There is a continuum of investment opportunities that are riskier. Risker investments must offer investors a higher return than lower risk alternatives to justify the risk. Note that the returns are *not* guaranteed. You can (and many do) lose their money in riskier asset classes.

• On the continuum, debt is risker than treasuries, public equities are riskier than debt, and illiquid early stage startups are riskier still. The yield is calculated as that cash payment over the price of the bond and must represent a premium over the Treasury rate available to investors. So even after a company issues a bond, if interest rates rise, the yield of a bond must also go up to make the additional risk worthwhile. The market solves this by reducing the price of the bond (yield increases as fixed interest payment over a less expensive bond).

  

• This same principle explains why many people believe 'real estate only goes up in value'. It doesn't. Real estate acts like a bond and the 30 year mortgage rate has been consistently declining since 1982. As rates increase we should see real estate prices come down.

Diving into the VC / Startup Asset Class

• Startups are one of the riskiest asset classes because many fail. Some create successful products, but do not become profitable businesses, and some become profitable businesses but don't distribute cash back to investors via M&A or IPO. Furthermore, startups are non-liquid, meaning you might have to wait 5-15 years for an opportunity to sell the shares. These factors contribute to investors’ expecting, but not being guaranteed!, a much higher return over the risk-free rate.

• Many VCs would be thrilled to return 3x after fees over 10 years to their LPs (limited partners, the investors in their funds - typically pension funds, endowments, high or ultra-high net worth individuals). This works out to ~4x before fees, or about 15% per year for 10 years. This is very hard to do. If the fund has raised $250M, it means creating $1.25B in value across 10-15 investments, where many will go to zero. Companies can only create liquidity in 4 ways:

  • Paying dividends, which few venture backed startups do because it is hard to become profitable and most profits are reinvested into growth and R&D.

  • Being acquired. Typically a strategic acquirer, although we're seeing increasing examples of private equity firms like Vista, Carlyle Group, etc. buying high cash-flow yielding technology companies.

  • Going public. Typically requires over $100M in revenue and the potential for long-term profitable growth.

  • Secondary share sale. Finding an investor to take the shares off your hands.

• When venture capital returns 15% per year and the risk-free rate is 2%, more investors are going to invest in VC. But imagine what fundraising for VC would have looked like in 1980 when treasure yields were 16% and an investor could have gotten higher returns (16% vs. 15%) via buying treasuries.

  • Low interest rates --> VC asset class returns very enticing --> More investor money flows into VC

  • High interest rates --> VC asset class less enticing --> Less investor money flows into VC

• One point that I'm ignoring is whether or not venture returns themselves increase in low interest rate environments - I don't have a defensible position on this because the returns are often captured 5-10 years after LP capital is committed, so I don't believe LPs consider this too strongly in their decision to invest.

• Because most venture funds deploy funds within 3 years, there is a lag between the LP commitment and when VC dollars get deployed. You can see why January 2020 was a great time to raise a venture fund, and that in the 3 years afterwards there were record deployments.

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The 2nd and 3rd order effects of too much Venture Capital

• Bad allocation of financial capital led to bad allocation of human capital. I won't dwell on this, but I believe venture capital is a noble calling and capital allocation to risky, innovative businesses is critical to society. There was method to providing capital and advice to the riskiest, highest potential impact companies in increasing chunks as the entrepreneurs de-risked key parts of the technology and business. We saw some investors investing huge dollars into startups before those risk reducing proof points existed, meaning the true limiting factor to growth, talented people, went to companies that are unlikely to succeed. It will still take 2-3 years for the cash and talent to flow out of those companies into the ones that will make an impact.

• Within startups, extremely high revenue multiples and preemptive funding rounds led to an extreme focus on growth at all costs. In sales, for example, the historical rule of thumb was that an Account Executive should deliver 4-5x their fully loaded cost in revenue to meet the hurdle rate. So an AE costing $200,000 a year should deliver $800,000 to $1,000,000 in ARR. In the world of 0% interest rates, even an AE that only delivered $250,000 a year, a 'poor' ROI, would add $5M or more ($250,000 times 20+) to the startups valuation. Ten such hires would add $50M+ to a startups valuation in 12 months. This wasn't real, long-term, value creation. I worked with many excellent revenue teams during these years and everybody was laser focused on making new sellers extremely successful, I'm just highlighting the perverse incentives for uncomfortably high growth and hiring. Charlie Munger is reported to have said "Show me the incentive, I'll show you the outcome."

Things to consider in 2023

• Reset any prior expectations around growth rates, team size, valuation and dilution and understand what investors will look for in a future round. One triangulating point is to ask yourself what ARR you will need to achieve at a 10x revenue multiple to your last round’s valuation. Then assume you will need to allocate $1.5-2.0M of capital for sales and marketing to add $1M of ARR, with a goal of getting to $1.33 in S&M cost to add $1 of ARR (i.e., Magic Number of 0.75). Pressure test with a reasonable Burn Multiple.
  
  https://a16z.com/growth/guide-growth-metrics and David Sacks’ Substack are great sources.

• Treat capital as extremely precious. Rank order every product and revenue investment and focus on those directly tied to PMF or most likely to accelerate new revenue, retain customers, or drive revenue expansion. Think of your company as a black box that must efficiently convert precious cash into business outcomes. Imagine that you will only get credit for the business outcomes and metrics investors will see in 12 months. What will you stop spending on? Review your team. People join startups for many reasons and I wouldn't be surprised if many people joined startups in the last years that are not truly suited to the risk, pressure and expectations. Many leaders were also enticed by the ability to quickly scale large teams versus creating capital efficient machines. Ensure everyone is on the same page that efficiency is what is in. If you are pre-PMF, my advice is that your rate of building cannot exceed your rate of learning. If you build faster than you learn, you are introducing yield loss and are unnecessarily burning capital. You will also move faster with a smaller team.

• Read Everything is a DCF by Michael Mauboussin

This week we announced the close of Chorus.ai’s first act as we join forces with ZoomInfo. I’ve known Henry Schuck, ZoomInfo board member Ashley Evans and the ZoomInfo team for years and have been inspired and humbled by their vision, customer focus, culture, and execution. Henry’s blog post explains why this is so exciting.

Thank you to all that reached out with kind words. If you texted my old US number, please email me!

Chorus.ai started as a crazy idea 6 years ago that businesses would want to understand and improve their increasingly online conversations. In the years since, our team built the #3 highest rated Software product on G2. We’ve helped support the growth of hundreds of iconic companies like Zoom, DocuSign and GitLab.

I need to thank a few people as the dust is settling. First, my wife Claudia for her unwavering support and encouragement. Hamutal Meridor, who introduced me to Micha Breakstone on one of my many trips to Israel to find the perfect co-founder for a voice-based NLP startup. Micha is one of the most impressive, empathetic and people centric leaders I’ve worked with. Beyond his formal credentials in speech and NLP, his ability to inspire great people to join and excel as a team is unparallel.

Russell Levy wrote every line of Chorus’ original code base and is the most impressive technical mind I’ve ever known. In the early days, he single-handedly built the technology demo that showed investors and early customers that it is possible to analyze conversations in real-time. Over the next 6 years he had a hand in solving countless technological and scaling challenges. Russell also has one of the best understandings of our users, customers and market, and was the person I could turn to during difficult moments. Chorus wouldn’t have been Chorus without him and the endless patience and support of his wife, Miriam.

Dominik Facher led Product for the last 5 years. I can't imagine us being here without his leadership, contributions and counsel.

I’d also like to thank all of our earliest investors and their teams. Specifically our Board Members Gordon Ritter, Santi Subotovsky, Tomasz Tunguz, Simon Chong and Greg Holmes. Each of you immediately saw the multi-billion dollar potential of Conversation Intelligence, supported me and Chorus unwaveringly through the ups and inevitable downs as we defined and created a new category. I feel privileged to have shared this journey with you and learn from your experience.

Thank you to each of you that bet on us and the early Chorus product (AffectLayer!) so we could learn and navigate the fog of the early days, and to the hundreds of customers and thousands of users that inspired and challenged us to build the best product in the space.

Thank you to each of our employees past and present. This product and outcome is the culmination of your ingenuity, creativity, teamwork and grit. Getting to know you all and working with you was the highlight of this journey for me.

Last but not least, thank you Jim Benton and the Chorus Executive Team (Thiago Sá Freire, Natalie Severino, Persha Valman, Sam Kramer) for their incredible teamwork scaling the business.

Formula 1 cars race at speeds in excess of 300 km/h (190 mph) and need to navigate inhuman turns, like Turn 8 at Istanbul Park below, where cars maintain speeds of 265 km/h to 285 km/h and 4.5 - 5.5g for 7 seconds.

One of the fascinating things about Formula 1 is that the only way to successfully drive the course is much faster and more aggressively than you ever dreamed of. Approaching a turn cautiously at 100 km/h will lead to a crash. Approach that same turn at 250 km/h, brake aggressively to 100 km/h in under 50m and then accelerate out of it, and the car handles it easily.

In 2009 I was the first hire at Eric Schmidt’s early stage venture fund, Innovation Endeavors. I spent that year meeting many brilliant founders. A common observation I saw with those early in those journeys is not dreaming as big as they could. I’ll do a separate post about what VCs look for in startup, but I believe vision is critical.

I often tell founders: “It’s hard to pivot out of a small market, and it’s even harder to pivot out of a small mindset.” Startups are limited by their founders’ vision.

We overestimate what is possible in 1 year and underestimate what is possible in 5 years. In this post I’ll use the imperfect analogy of a Formula 1 race car to argue that a moonshot vision is one of the most important things to figure out and articulate early on and increases your company’s likelihood of success.

Before reading on, take a breath and ask yourself: What would your moonshot vision be if you could recruit the best people in the world and the resources you need?

Tie this back to startups, please

Startups live and die based on the quality of the people. The probability of any startup succeeding is already low. A startup with average people is even less likely to make it. In my experience, world-class people care about working on something big and meaningful, working with other incredible people, and with autonomy and independence. They can’t be lured with high salaries. The right people will take the former benefits over a high salary and walk away from incredible opportunities at Google, Facebook, etc. where they are working on some of the world’s most challenging technical problems at a scale few of us comprehend. As a founder, your vision, the role / opportunity and your character are what will attract these types of people.

Going back to the F1 analogy: Even with the same car and components, at slow speeds you only benefit from the significant advantages of the engine and mechanical grip. High speeds and high brake pressure heats up the brakes, which heat up the tires, making them extremely grippy. The spoilers and surfaces start generating downward force up to five times the weight of the car - meaning it could in theory drive upside down (!). That additional weight creates more friction that allow them to brake faster, accelerate faster and handle unlike any other car on the planet. Extreme speed bestows capabilities that don’t exist at lower speeds. The same is true for challenging your startup to do more.

This is a fun video from Top Gear of Richard Hammond driving a F1 car for the first time. He talks about struggling to have the confidence to drive fast enough and aggressively enough to keep the tires warm and generate the downforce necessary to make the turns. The entire video is worth watching!

At Chorus, we started with a vision that enticed incredible people to leave other incredible opportunities to join us:

• Explaining Chorus’ initial vision: Data driven and customer driven businesses win. Customer conversations are a business’s most valuable untapped data asset, but nobody thinks of conversations as data today. Chorus will help every business start capturing and realizing the value of conversational data. We’ll replace biased, incomplete, human-entered, forms-based CRM data with the unbiased voice of the customer by understanding unstructured conversations.

• Explaining Chorus’ initial problem / solution: The highest impact thing a sales person does is talk to a customer. There’s no record of those conversations aside from a few notes that rarely make their way into CRM, so it’s impossible to learn what the best sales people say and do to win business. Chorus automatically records and analyzes Zoom calls for sales people. Sales people can stop taking notes. Others in the organization can use these recordings to learn from the best reps, ramp faster and hear the voice of the customer.

Elon Musk is one of the best at setting extreme moonshot visions. Even if you fall short on execution, you’ll be amazed at what you can achieve. Dream big.

• Excited to dedicate the next 10 years of my career to

• Never attempted idea vs. executing a proven business model

• Unlikely to succeed, but if successful would create a multi-billion dollar software category

I asked myself

What big problem can we solve with today's technology that we couldn't 5 years ago?

That idea became Chorus.ai, and I'm humbled by how far Chorus has come in the last 6 years. As Chorus' founding CEO, I raised over $65M in venture capital from Emergence Capital (Salesforce, Zoom), Redpoint Ventures (Stripe, Twilio) and Georgian Partners (Shopify) and developed a product with an NPS of ~70. In 2021, G2 recognized Chorus as the #3 highest rated Software product in the world. We have have over 180 incredible employees and serve iconic companies like Uber, Zoom, MongoDB, Redis Labs, Segment, and hundreds of others.

The early days of founding a company are hard. It can be hard to tell the difference between spinning your wheels and making progress. The saying that 9 women can't make a baby in 1 month resonated deeply during that year. If you're going through the early days, be patient with yourself, follow your curiosity, and enjoy the process.

It can be hard to pick a place to start when you're open to anything. I started by imposing a constraint. I had conviction that applying machine learning to new data sets would allow us to solve previously unsolvable problems. In the same way cloud first companies displaced on-prem, and mobile first displaced desktop, I believed "ML first" companies that could capture, analyze and act on new streams of data in real-time would displace static, forms based, databases.

To call myself out, and going against many startup playbooks: I started with a new technology in search of a problem I was excited to solve. I would add that if you take this approach, quickly focus on a business function or specific user, understand their Top 3 issues and whether your technology can solve one of them.

Being in Action and getting your hands dirty is the fastest way to develop intuition and understanding of a customer pain point or new technology. In 2014 I knew nothing about machine learning and hadn't programmed in over 10 years. Coursera did a short write-up about my experience learning machine learning with the help of my close friend and future co-founder, Russell.

After completing the course I wanted to go beyond the basics and extend it in a direction I'm passionate about. I love sound, I love music and I love languages, so I focused on Voice.

How is voice represented as data and how would you feed it into a neural network?

What could an organization learn from and predict from their voice data?

Simple tools like Matlab and Octave let me build and play with single frequency audio signals using the math.sin() method and deconstruct mixed frequency audio using Fourier Transforms. Lessons from my undergrad Signal Processing class started making sense, like why there is a trade-off between sampling rate and frequency resolution when transforming from the time to frequency domain.

Once I understood the fundamentals, I reviewed published research on voice based emotion detection and reached out to those experts, acquired the data sets they used, and reproduced their research. Examining the underlying data, I realized the underlying authenticity of the voice recordings the models were trained on was a problem for real-world applications: They were sourced from undergrad acting students reading scripts, and predicting the emotion of a voice snippet didn't solve a practical problem for businesses.

Emotion detection research was cool, but there is a saying that "Companies don't have budget for cool". We would need to capture authentic voice data and apply our analysis to solving a Top 3 business problem.

I could see an untapped multi-billion dollar opportunity in voice and started down the path of creating a consumer application for voice based restaurant reviews called Bon Appetit. My thesis was that in a voice-first world, hearing the right voice, telling you about a restaurant or bar they recommend would help you make a better, faster decision than reading reviews. Over time, we could analyze the voices and swipes to determine what resonated with users on a personalized level. I built a simple iOS application and hit the pavement in poor German getting friends and strangers to record recommendations. I then tested the application and recommendations with the local blind community in Switzerland. Who better to help me understand the power of voice than those highly attuned to it?

I was having fun and learning a lot, but realized that the cost and complexity of building a user-base and voice based reviews meant the big players would be likely to capture that market. The answer was B2B.

It became obvious that voice communication was the largest, most valuable, and untapped enterprise data set, and that capturing and analyzing sales conversations were the place to start.

It took a full year of exploration before two close friends and mentors that I had been speaking with every few weeks helped me realize that I needed to pull the trigger on what became Chorus: Capturing and analyzing enterprise voice data using machine learning and Natural Language Processing.

The next step was recruiting technical co-founders. The ability to recruit a world-class founding team is an acid test of both the opportunity and your ability to articulate it. If you can't get someone world-class excited and to join you full time, there's likely more work to do. I recruited a world-class engineer, and an expert in NLP to join the founding team and we got to work.

We asked ourselves why was Chorus possible now and not earlier? Why did we believe the market would be massive in 5 years?

There were three reasons:

• Technological progress: Breakthroughs in deep learning improved transcription accuracy to the point it could deliver the accuracy needed to solve the problems our customers had. And it would continue to improve as we trained our models on more data and algorithms improved further.

• Shift to Online Meetings: Phone calls, WebEx, GoToMeeting and Zoom would be used for more business conversations. These could be captured directly through the systems without asking users to do anything.

• Shift to Inside Sales: More businesses were building inside sales teams (those sitting in offices versus visiting customers onsite, over golf, dinners and drinks).

We asked ourselves if we could beat the incumbents providing communications services, despite starting with no data:

• Machine learning models require the call recordings, metadata and outcomes. Recordings on their own were useless. Incumbents could only access recordings and most companies did not record calls. Some of the metadata would need to be generated from the call recording itself (who spoke, for how long, voice speech rate, pauses, interruptions, transcripts, topics), which requires specific capabilities these companies did not have.

• Other metadata and outcome data would come from business systems that these incumbents had never tapped into: Who participated in the meeting? What happened after the conversation? Did we close a deal? All of this information was stored in other business systems (Calendar, Email, CRM). Furthermore, you needed a snapshot of those systems at the time each conversation took place to understand the state and outcome.

• So even if an incumbent had millions of hours of call recordings, they wouldn't be able to put it into the context that mattered to be valuable without connecting to all of these other systems and creating a real-time pipeline to analyze the insights.

• We were all starting from the same starting point.

With this clarity, we developed a killer technology demo (in 2015!) that analyzed a live phone call in real-time combining live transcription, natural language understanding, and voice metadata that appeared on the screen as we spoke. It was a magical experience for anyone watching it and enabled us to raise our ~$5M Seed round to hire a small team, develop the product, and acquire customers.

We coined the term Conversation Intelligence for the new category we were creating, and an incredible journey started. My blog post on Chorus' launch goes into our vision in more detail.

Over the last 6 years, Chorus.ai transformed from a crazy idea I dreamt up in an 11,000 person Swiss village into a product and business I count as my proudest professional achievement. Chorus has close to 150 incredible employees, millions of ARR and counts some of the world’s most iconic companies as customers - including Zoom, MongoDB, DocuSign, Uber, Qualtrics and more. We raised millions of dollars from 3 of the top venture funds in the world - Emergence Capital, Redpoint Ventures and Georgian Partners. I came out the other side of that journey a profoundly different person.

Building Chorus was intense and I rarely had (made?) the time to process my feelings and learning. My goal for this blog is to synthesize the things I learned building Chorus and the most common issues I come across advising early stage founders. I’d also love to learn from your perspective.

Thoughts on what I should cover?

If there’s a topic you’d like me to address, you can let me know via Twitter (@royr) .

Startup Office Hours

I’ve also started 2 hours a week of Office Hours for early stage entrepreneurs that I think I can help in areas of navigating early stage Product-Market Fit, fundraising, Go-To-Market, Category Creation and People and Culture.

Apply here and share some background information and and I’ll do my best to make the time.

I hope this helps you navigate your journey building your startup.

Roy