The Five Guide Lines To Category Creation, Step Three Technical Feasibility

For Founders and Angel Investors

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In this deep dive, we will cover what technical feasibility consists of, how to spot technical flaws at the seed stage, and what goes into creating and winning a new category from a technical feasibility point of view. I love this section because, unlike some of the other parts of category creation that are much more gut feeling-based and future facing, technical feasibility is quantitative in nature with yes or no answers.

For angel investors, this will arm you with prudent first principles to assess quickly a potential investment into a startup at the earliest stages. For founders, this offers a view into how to measure and operationalize your technology offering as well as how to support your customers.

As a reminder, my five guidelines to category creation are:

1. Market timing
2. Community
3. Technical feasibility
4. Founder market fit
5. Business market fit

Here is what to use to measure technical feasibility:

Why this matters? Technical feasibility can make or break a pre-seed and seed-stage startup.

For perspective, here is what it takes to raise a pre-seed:

1. Completion of 50 - 200 validation calls that clearly demonstrate the market need and product fit if built.
2. An open-source repository with over 10% MoM (Month over Month) growth, if possible.
3. An MVP that users can log in to and try out, if possible.
4. Slack or Discord with 200 - 300 members, if possible.
5. 5 - 10 design partnerships that genuinely validate what you are building and why. They should be open to speaking to VCs and providing quotes. Quality is more important than quantity.
6. Clarity on what problems you solve, who your competitive set is, who you are selling to, how you could make money, how much money you can make, a roadmap, a financial plan, and overall clarity on what you do and why.

If you are curious how to go from pre-seed to seed and beyond

Jordan Segall from Redpoint analyzes 80 companies; 69 Seeds and 48 Series A in great depth.

Some of the data-driven take-aways (note this is for open source companies only.)

• 69 companies in the dataset that raised a seed round, at least 49 of them had no revenue at the time of their financings
• Of the 48 Series A companies in the dataset and the 34 I know of their commercialization at the time of the A, 25 had no revenue at the time of the A. Of the 9 that did, 5 had <$1M ARR.
• Of the 69 seed companies, 54 had released their open source project at the time of their financing. The median value of these projects’ stars was 2850
• Of the 48 Series A companies, all of the open source companies had released their projects by this point in time except one, with a median star value of 4980
• The average month over month star growth rate of the 27 companies in the dataset from seed to series A is 7.9%
  

• On design partners.. what matters?
  The quality of the design partners - for example, if you are a YC startup, having a bunch of other YC startups as design partners is not as impressive as having well funded and respected growth stage or larger tech startups.
  The extent of the design partners and the use case your project is being deployed to solve.

Now let’s un pack technical feasibility at the seed stage in a new category.

Whether you are a angel investor or founder this will apply to you and help you. If any of the core tenants are missing or lacking you will have risk in the investment or risk a founder that you can scale and win the market.

The core tenants of technical feasibility are as follows:

1. Ease of use

• How easy it to use the product?
• How user friendly is the UI?
• User experience in 2023 dominates software usage, adoption, and growth this is something that should be looked at firmly and rated.
• How long (in terms of hours, number of people, type of people needed) does it take to deploy a use-case?
• How long it take to fully deploy?
  

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If there is one thing we can agree on it is that PLG has lead to explosive growth, quickly. This is due to ease of use above all else.

2. Source: PLG and Sales – A Powerful, One-Two Punch

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3.Scalability

• At what scale does the product stop working?
• What is the gap in scale, and is it solvable?
• What is the scale the market requires to win?

4.TLDR:

If you are going to close Netflix and Uber, you need to build a platform that can handle their scale.

5.Stability

• How stable is the product?
• Are there bugs?
• Is a stable product something the team can reach? Why or why not? How long will it take?

6.In a new category, it is critical to always gain and never lose customer trust. Part of this is not allowing forced errors by proving a software that lacks stability.

7.Enterprise readiness

• Can the team deploy on-prem and VPC?
• Does the enterprise require on-prem and VPC? (99% of the time they do in MLOps and Generative AI)
• Has the founding team built and deployed on-prem and VPC software in their past?

8.To win the enterprise, you either get this right, fast, or you do not. I have not seen a team get this right that has not done it before and done it well. I also see a trend where founders who have not built and sold to the enterprise before speak as if this is something that is easy to do and something they can outsource. This is a red flag to me and is something to look out for, mapping past experiences to current market requirements.

9.Support

This seems obvious, but like so many other obvious things, it is done wrong all the time. Customer obsession is either in the founder's DNA or it is not.

10.As a founder or angel, you need to be confident this will be handled the right way.

To create or help create a new category and then proceed to win the category, angel investors should use these first principles to determine if the company has the proper technical feasibility to win. As a founder, each decision made should be measured against these first principles to stay on track and win.