Opportunities from the Venture Capital Perspective

Jason Lemkin of SaaStr and David Sacks of Craft Ventures talk about what type of SaaS company is likely to get venture money in the current market. In the case of Craft Ventures 70-80% of their money is being invested into AI SaaS startups.

They have a way of categorizing SaaS investments which I think is useful as a way to think about AI SaaS.

• AI infrastructure
• Infrastructure is hot right now, with large rounds at very high valuations.
• Co-pilots
• Co-pilots are where startups and founders can do new things that big companes won't go after, department and profession level solutions.
• Pre AI SaaS
• Pre-AI SaaS apps get turbo-charged in value by adding AI functionality. These companies get to exploit new AI APIs to make their products better. This is the category of most existing SaaS businesses figuring out how to use AI in their products.

If you have a startup and are looking for money this article will help prepare you for some expected questions and reactions.

Using Generative AI as a Competive Advantage

This HBR article talks about how to turn Generative AI form a threat to an advantage.

The levels of Generative AI as outlined in this article include:

• Level 1 - Adopt publicly available tools
• Level 2 - Customize tools with customer data
• Level 3 - Incorporate data feedback loops

Level 3 is where companies should be operating. Software of course is a major tool in helping get them to that level.

In the implementation thoughts section the article looks at opportunities and risks in software companies as quoted below:

"Software companies will find that the extent to which their products can be replaced by generative AI will vary a lot. All else being equal, complex software products are more likely to be affected, because a natural-language chat interface could make them a lot easier to use for a wider customer base. On the other hand, software that offers value through integration with other products or services is more immune to disruption. For example, the payment platform Stripe is built on complex and highly secure connections to financial institutions that are not easily replicable by generative AI."

Empowering End Users of AI

A start up from MIT has focused on providing tools that are easy for the end user to use to generate and use AI models. Their experience with this approach has demonstrated that AI models are better if they are created by someone who understands and uses the data regularly in the normal course of business as opposed to an AI expert. This is a great case study on this approach to AI.

In reference to the levels of generative AI in the section above, this approach helps a company move quickly with this approach as a Level 1 public tool and to quickly develop models using proprietary data in Level 2.