Engineering risk and go-to-market risk
Gil Dibner

Good morning from Dublin, where I’m attending SaaStock. Last week, David and I were in Tel Aviv. It’s been said a million times, but the return of in-person meetings has been such a blessing. If any of you are at SaaStock and want to connect, give a shout!

Like most people in our industry, I’ve been reflecting a bit on the nature of risk lately. Broadly speaking, the early-stage tech companies I’ve worked with over the past twenty years seem to fall into two broad categories: (1) engineering risk and (2) go-to-market risk.

• Engineering risk companies are building something where there is significant risk around whether or not it can be built at all. This works best when there is little doubt that the market will want some version of the product. These companies can often afford to defer their struggles with go-to-market (GTM) for a later date. In many cases, the bar for some initial product-market fit (PFM) is also lower, because the core value proposition is so large and unique that customers will accept a lot of imperfections in its delivery. At the inception stage, these companies are characterized by significant engineering risk and low PMF/GTM risk.

• Go-to-market risk companies are typically building something where there is little engineering risk, but significant PFM/GTM risk. Both PFM and GTM pose significant risks and challenges, primarily because customers will face multiple competitive offerings and the company can only achieve meaningful success if it quickly emerges as the leader in its category.

From both the venture capital and entrepreneurial perspectives, these are two completely different playbooks — often requiring different types of founders, teams, investors, and board dynamics. The milestones and early signs of oil are quite different. Unsurprisingly, some of the most persistent failure modes we observe occur when founders (and boards) misunderstand which paradigm applies to the company in question.

Type 1 error: When an engineering risk company is mis-identified as a GTM-risk company, there is a tendency to blow a lot of resources on marketing and sales efforts on a product that is not ready from an engineering perspective. Similarly, boards can misjudge the traction that exists. Sometimes $100K of sales on a truly revolutionary product to a key reference account can be enough to justify the risk of continued financing. But attempting to pre-maturely scale a pre-PFM deeptech company can burn through significant capital and trust — which can tank the company.

Type 2 error: When a GTM-risk company is mis-identified as an engineering-risk company, the dynamic is reversed. Management and boards are overly impressed by product progress, not really wrestling with the implications of low differentiation, high competition, and problematic pricing and CAC. Corporate leadership doesn’t prioritize hiring tier 1 marketing and sales talent, mistakenly trusting that their product or vision is enough to carry the day. And product management can be lulled into thinking that a few key features might be enough to win customer love, when — in reality — customers will be expecting a far more feature-complete offering before they would consider a switch.

We see both of these errors pretty consistently in companies that are pitching us for their first check — and we sometimes encounter them over time in portfolio companies as well. What’s also perhaps worth observing is that as technologies commoditize, Type 2 errors become more common as a given market is more likely to give rise to GTM-risk companies, as opposed to engineering-risk companies. As a software-oriented investor, nearly every company I backed in the late 2000s was an engineering-risk company. Today, all those companies would be GTM-risk companies, and we need to look harder to find companies that truly represent engineering-risk.

As with so many things in the startup journey, the key thing here is seeing reality clearly: knowing where your biggest risks really lie. Figure that out — see it clearly enough — and your plan to achieve de-risking will begin to present itself.

If you are building the next big thing in B2B in Europe or Israel — whether you are engineering-risk or GTM-risk — we want to hear from you!

EVENTS

Jan 25 / Lessons Learned From Investing Early in Over a Dozen SaaS Unicorns Including Salesforce, SuccessFactors, Box, Gusto, SalesLoft, ServiceMax, Veeva, Bill.com, Doximity, Yammer and Zoom Among Others
Jason Green, Founder & General Partner, Emergence Capital

Feb 15 / The Evolution of Collibra’s Product Positioning & How They Created a Category
Stan Christiaens, Co-Founder & Chief Data Citizen, Collibra

FROM THE BLOG

It’s Never too Early to Build your Growth Model
What are the specific mechanisms by which one user turns into many, and an initial investment turns into revenue?

How to Think About Revenue Quality as an Early Stage Founder
What does “quality revenue” mean when you don’t have much revenue at all?

It’s Not All About Bottoms-up
Two recent trends indicate that we may finally be past the mistaken belief that bottoms-up is the only “fundable” business model in town.

Don’t be Fooled by the PLG Mullet
How to know if you should be building a PLG Now, PLG Later or PLG Never company.

EUROPE & ISRAEL FUNDING NEWS

Spain/HR. Factorial raised $120M for its cloud-based platform for HRMS suite.
UK/Security. Immersive Labs closed $66M for its cyber awareness and training solutions.
Estonia/Industrial. Katana MRP raised $34M to give manufacturers a live look at their business with visual, intuitive, and easy-to-use ERP software.
Israel/Security. SecuriThings raised $21M for its real-time security for IoT applications.
France/SW Development. MeiliSearch raised $15M for its open-source, lightning-fast search engine that fits into your apps, websites, and workflow.
Germany/Automation. Levity (an Angular portfolio company) closed $8.3M for its no-code ML workflows platform.

WORTH READING

ENTERPRISE/TECH NEWS

The State of AI. The State of AI 2022 report is live, outlining the most important work in AI research, industry, talent, and politics. Co-editor Ian Hogarth outlines his most interesting observations in this tweet storm.
“5/ Compute requirements for the largest AI models have potentially entered a new phase. From 2010–2015 they were doubling every 6 months. From 2016 there was a 100–1000x jump, then doubling every 10 months. I think of these as being the DeepMind & OpenAI inflection points.
15/ It’s striking to see how in this regard, academia is passing the baton to decentralised research collectives funded by non-traditional sources, which are making their research more accessible to the public.
24/ Returning to the theme of bottlenecks, NVIDIA’s chips remain incredibly dominant with a massive 78x difference in usage by researchers compared to Google’s TPU.”

Social commerce. TikTok appears to be deepening its foray into e-commerce with plans to operate its own US warehouses, the kind of packing and shipping facilities more associated with Amazon or Walmart than the social media platform best known for addictive short videos.

VR to robotics learning. Lerrel Pinto, a professor of CS at NYU, tweeted about a breakthrough about using VR to teach robots “this is perhaps the most surprising robotics result our lab has produced in the last few years. Vision-based dexterity with no RL, no simulation, just from human teachers in mixed reality”. The video is worth checking out.

AI podcasting. In a trend of AI powering content, cloned voice synthesis company play.ht created a podcast that is entirely generated by artificial intelligence where AI Steve Jobs is interviewed by AI Joe Rogan.

HOW TO STARTUP

Questions to unlock your team. Matt Mochary, the most sought-after executive coach in Silicon Valley, has 5 “MAGIC” questions he has all his leaders ask their teams.
“1. How are you feeling about your life at work?
2. How are you feeling about your work-from-home set up?
3. How are we performing as a company?
4. What is it like to work with the rest of the team?
5. What is it like to work with me?”

First 1000 fans. Patrick Chase from Redpoint discussed how some of the best OSS companies got their first 1k community members from Hashicorp, Confluent, Databricks to CockroachDB.

HOW TO VENTURE

VC funding. Crunchbase reported venture funding for the third quarter of 2022 totaled $81B, down by $90B (53%) year over year and by $40B (33%) quarter over quarter, according to a Crunchbase News analysis. Dealroom and Sifted reported similar results for Europe. At $18.5B, aggregate Q3 investment has significantly plunged below previous quarters, although it still sits higher than pre-2021 levels. To get a sense of VC funding expectations looking forward, Andrew ran a poll on if people think the funding environment in 2023 will be better or worse than 2022. At the time of writing, 67% of people think next year will be worse. Villi Iltchev, a VC at Two Sigma, thinks “the pain in venture will not become acute until Q2 2023”.

PORTFOLIO NEWS

Levity announced the launch of their open beta and their $8.3M seed round led by Balderton Capital and Chalfen Ventures, with participation from Angular Ventures, System.One, Discovery Ventures, as well as a number of angels.

Planable’s CEO, Xenia Muntean, shared in Forbes three tips for digital agencies to better manage their client relationships.

Forter is fighting chargeback fraud with Smart Claims. Smart Claims enables merchants to resolve chargeback disputes more intelligently and efficiently, improve win rates, and recover lost revenue.

Snyk announced that it has formally joined Pledge 1%, a global movement to inspire, educate and empower companies to effectively leverage their financial assets for positive social impact. With this announcement, Snyk has set aside 1% of its current equity to fund Snyk Impact initiatives long-term.