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From every startup I work with, I learn about the different ways of building a company. It’s one of the best parts of the job. By their nature, founders hold strong world views about how to craft their companies.

Though it’s early in the company’s life still, Barr Moses, the founder and CEO of Monte Carlo, is an extraordinary leader.

Many startups face a strategic question early on of whether to create a new category. It’s a heavy lift. A former executive at Gainsight which created customer success, Barr jumped in. Monte Carlo is creating the data observability space.

With all the talk of recession, changing market dynamics, and the importance of efficient growth, many teams will be looking to boost their sales efficiency. How can the company drive more sales with less expense?

Let’s start with the formula for sales efficiency.

Sales efficiency = New_Bookings x Gross_Margin / Sales_and_Marketing_Cost

Three paths emerge from the equation:

1. Increase bookings: increase pricing without sacrificing sales velocity.
2. Improve gross margins: reduce infrastructure spending, eliminate software bills, and/or reduce customer success costs.
3. Decrease sales & marketing cost: increase quotas, focus on marketing channels with superior ROI (at the expense of exploration), and improve sales training/support.

Each strategy is viable. Teams should select the path or combination that suits their business best. But these strategies won’t impact the sales efficiency number the same way. Why? Convexity.

As the fiscal quarters of many startups draw to a close, board members and management teams are having one of four conversations: The World is Your Oyster, Time to Strategize, Chewing Gravel, or Go Big/Go Profitable.

Here’s how these scenarios fall onto a 2x2 matrix. The x-axis is the Zero Cash Date: when the startup runs out of money. The y-axis is sales efficiency: a proxy for product-market fit (PMF). Typically, most startups selling into the small-and-medium business segment would like to be in 14-18 months’ payback. Enterprise startups should be between 18-28 months.

The past three days may be the most eventful in web3 ever. Crypto assets have fallen by half or more, following their software counterparts. The attack on an algorithmic stablecoin’s peg harkens back to Druckenmiller breaking the Bank of England.

All the tumult in the crypto markets will catalyze change. Here are my five mid-year predictions for the major evolutions that arise in response.

1. Consolidation. Five to ten L1s emerge as leaders driven by apps that attract users and net new GDP into their ecosystems. More dollars and users incite a positive feedback loop reinforcing the dominance of these L1s. In Defi, Lindy’s Law rules: institutional investors opt for protocols with the longest track records. Crypto-gaming succeeds in bringing 100m users to web3, starting with casual games which are easier to build, experiment, and iterate. AAA gaming titles arrive two to three years later because of their development lifecycles and studio risk aversion to invest tens of millions into a new platform.

How much further can the market decline?

The answer is quite a bit more. That may be hard to believe given the falls from recent highs in software companies approach 70% collapses.

I went through my archives and found this post from 2017 that showed that the most expensive stock at the time was Veeva at 11.7x forward.

The public markets are deep crimson. The last time VC sentiment was so publicly negative, Twitter turned two. Term sheet re-trading rumors have surfaced.

Public markets do impact startup fortunes, but only inasmuch as the prices at which venture rounds clear. IT spend is the more important canary in the coal mine.

If customers cut their software spend, startups should expect a harsher climate.

AngelList published their quarterly state of venture report. I wrote down five data points that struck me:

1. Q1 2022 was the most active quarter ever in Angellist history, and likely venture history. The term sheets signed in November and December closed in Q1, which may buoy these figures. In addition, late-stage investors moving into the seed stage market also buttress these stats.
2. 83% of rounds in Q1 were up-rounds, which is statistically identical to Q4. late-stage market prices have declined about 30%. No parallel compression exists in the early-stage market, yet.
3. Startup valuations. The 75th percentile of Seed startups raises at 30m valuation and Series A at $100m. Seed has become the new A. $100m post is consistent with what I’ve seen in the market for the most sought-after investments. Some raised a seed first; others particularly in infrastructure decide to go straight to A.
4. 65% of seeds choose to raise capital via SAFE, a form of debt, rather than an equity round. If Seed is the new Series A, then this implies a meaningful change. Most SAFEs forgo a board-of-directors. A decade ago, businesses raising $3-5m Series As would elect a board. This data point confirms founders have maintained leverage in fundraising conversations.
5. Web3 deals represented more than 11% of investments, the largest share, superseding fintech and healthcare. Web3 is a term that will disappear like web2 and mobile investing before it. It encapsulates software, infrastructure, and consumer services- unlike the other buckets which are more narrow. While the segmentation may skew the data somewhat, the data point does underscore investor interest in the category broadly.

The Q4 charts may not differ much from those published in this report. Web3 will remain a top area of interest. SAFEs should persist as a dominant form of financing early-stage startups, and consequently inform board construction. Perhaps valuations and activity will change, but given the amount of capital in the ecosystem, the magnitude might be muted.

A founder recently told me he would have built his company differently in another fundraising market.

When I asked him what he meant, he replied because capital was so plentiful and accessible today, he hired more expensive people, spent more time developing a product, and invested with a longer time horizon before demonstrating evidence of success.

In my notebook, I sketched this 2x2. Capital availability on the x-axis and evidence on the y-axis to illustrate his point.

When I hosted this blog on Amazon Web Services, I used 5 products. I paid for them each in US dollars every month. One invoice.

Suppose I wanted to rebuild my site on web3 using fully decentralized components. I would pay each product provider in their own token: one for storage, compute, caching/CDN, email subscription management, etc. Tokens reward the validators and the stakers powering the decentralized networks.

Paying five decentralized providers in five different tokens means managing several wallets and monitoring token prices to hedge expenses. That’s much more work than the automatic credit card payment with AWS. It’s too much complexity for a simple static blog.

I used to have a clear answer to a founder’s question, “How do you value a company?” The question is just as important in conversations within a VC partnership as with founders. A valuation must have some justification to be compelling.

Reflecting on this question a founder posed this week, I remembered how we came to be here, the three eras of startup pricing: cash-flow, multiples, and discount-to-future-value.

Cash Flow
In the aftermath of the dotcom crash, a valuation depression kept valuations low. A few months before Lehman fell, I joined Redpoint. I was taught to ask founders how much capital they needed to attain milestones and circumspect the financial model. How much money did the company need to have 18-24 months of runway? The model says $4m? Great.