Tag: Enterprise Software

AI Musings #7 – latest on AI in the Valley

Given the exponential rate of change in AI, Dev Tools appear to have the most risk of use case durability, compared to Infra and Applications.

Earlier this week, I attended the AGI Builders Meetup in San Francisco. The event had 5 product demos ranging from new AI features by Twilio to a YC S24 startup building an AI agent to handle calls on behalf of users.

These demo-based events are always helpful for me to track the latest in AI. Also ended up chatting with a few engineers and founders attending the event, to get their thoughts on what they are seeing in their respective domains within AI.

One key insight I got from this event was that LLMs aren’t the real future of AI. No one really knows what’s going on inside them. They hallucinate much more than desirable (especially for accuracy-driven enterprise use cases). They are prone to prompt injection hacking.

In fact, the presenting Twilio PM said that “working with LLMs is like getting toddlers to do something”. They don’t take instructions promptly. You don’t know what’s going on inside their heads. You have to proactively ensure their safety as they are doing a task. I found this framing really interesting.

Building on this further, I have an updated (but still working) POV on the 3 buckets of AI – infra, applications, and dev tools.

#1 Infra

Irrespective of where AI goes, hardware infra like GPUs will always be needed. Hence, it makes sense that Nvidia is doing so much capex.

Also, Big Tech software infra players (Microsoft, Google, Meta, AWS, etc.), as well as AI-native hyperscalers (OpenAI, Anthropic, etc.), will continue playing a key role in defining where AI goes from here.

Sadly, as a micro VC, I can’t play much in this bucket (except personally investing in the public markets).

#2 Applications

Application layer founders that are starting up today are leveraging the capabilities of AI from Day 0 to solve customer problems. Their core focus still remains commercial-first – using the best-available software capabilities to solve customer problems, rather than getting overly enamored by the research aspects of AI and where it’s headed.

In a sense, these startups are centered on customer problems, not AI per se. Wherever AI ends up going, LLMs and beyond, these founders will leverage whatever capabilities they can get their hands on, and modify their architectures accordingly.

As an investor, the key is to back founders who are starting up now with an AI-first mindset and therefore, are fresh enough and agile enough to keep evolving their software as the underlying AI capabilities evolve.

Therefore, it’s reasonable to expect that these AI-native application startups should be fairly resilient to changes in the overall AI landscape. Hence, I feel reasonably comfortable in backing them (eg. portcos like Confido Health, Loop, and Soulside).

#3 Dev tools

This is the bucket I am most confused (and concerned) about. From seeing these demos, it seems like dev tools startups are essentially using the mental models of the previous cloud & mobile waves to make assumptions on use cases.

Further, I have observed that many of them are solving short-term, immediate pain points that could easily become irrelevant due to where AI goes from here, and/ or from the competition (eg. open source alternatives, AWS quickly launching it as a feature, etc.).

As I was seeing these demos, I looked up how much capital some of these companies had raised. Many of them have raised anywhere from $10-35Mn. The capitalization of these companies seems out of sync with the durability of their underlying use cases and revenue.

Essentially, what all this means is that I have a macro “Why Now?” question around the AI dev tools bucket. A top Bay Area engineer who recently left a cushy Big Tech job to start up was recently saying – “Given how things are changing every month, I am really not sure what to build right now”. I feel this is an intellectually honest view, rather than a FOMO-based approach that many VCs are taking.

Based on what AI practitioners like this person are saying about the exponential rate of change in AI, I fear that a majority of these dev tool use cases won’t endure.

Again, this is just my working POV. Would love to hear your views on what you are seeing.

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SMB SaaS at Scale: Founder Learnings from HubSpot

SMB SaaS is hard. Getting the positioning right, increasing ACVs, controlling churn – it all becomes harder when your customer is a small business that is resource constrained & perpetually dealing with its own execution challenges.

Despite this, given SMBs are the most frequent early adopters of new products, the reality is that most startups tend to start mid-market. Though, in my experience, a majority get stuck in unfavorable economics of this customer segment & are unable to achieve breakout PMF.

So, what is the secret sauce founders can learn to effectively scale SMB SaaS? Hubspot is a great case study. I recently came across this SaaStr podcast with the HubSpot CEO Yamini Rangan, where she shared some of the company’s SMB strategy & learnings. Here are the key highlights:

  1. Go after a large TAM: given the fragmented nature of SMB verticals, it’s really important to have a large TAM. HubSpot made the smart decision to transition from marketing automation to CRMs, basically going after Salesforces’s lunch.

Mid-market verticals tend to have open opportunities for startups as SMB customers are usually sandwiched between either buying a host of solutions & stitching them together or buying an expensive, enterprise-grade solution. In this context, I had recently posted a Twitter thread about how Zoho followed a similar multi-use case bundling strategy to position itself as an “operating system for SMBs”. This strategy works well as SMBs have a tendency to simplify their tech stack & procurement processes by buying multiple solutions from the same vendor.

2. Customers gravitate towards competitively-priced, mission-critical products: in times of economic uncertainty like today, SMBs tend to become really sensitive about budgets. Customers start asking tough questions internally around (1) where are they spending?, (2) do they have a clear path to getting enough value from the spend? and (3) can they do more with less?

Acting per this analysis, SMB customers are then likely to consolidate their tech stack to a handful of mission-critical platforms that are competitively priced & deliver the most value. This is the bar startup products need to cross while selling in this tough macro environment.

3. PLG-based distribution is king: to achieve break-out growth in SMB SaaS products, startups need to have the widest possible distribution. The front door needs to be big enough so that most people can come in.

For the first 8-9 years, HubSpot was mainly driven by a sales motion comprising Direct Sales & Partner Sales. Around 2016-17, in order to exponentially grow distribution, the founders made a counter-intuitive bet to go from sales motion to product motion. Today, HubSpot has a massive user base of ~1Mn WAUs to monetize off of.

4. A strong “free” product is key to PLG: One of HubSpot’s truly differentiated product strategies has been to offer a strong, full-featured free product. Rather than making a “free” product free just for the sake of it, they have focused on making it really valuable.

Some important benefits of having a strong “free” plan:

  • Drives high top-of-funnel growth & user engagement, improving the probability of monetization once the value is proven out.
  • Puts product org. under pressure to deliver enough features at the top, in order to maintain the competitiveness of paid versions.
  • Forces the product team to maintain a “consumerized” ease of use, which benefits all customers, free or paid.

Irrespective of whether your GTM is sales-led or PLG-led, a founder should never give up on the “free” plan as it’s key to keeping your product competitive.

5. North Star Metric should be Net Revenue Retention: NRR is the best health indicator of an SMB SaaS business given it represents whether or not: (1) you are retaining the customer, (2) you are continuing to drive enough value so they buy more from you and (3) you are protecting yourself from churn.

6. Don’t underestimate the value of a Partner ecosystem: once you reach a certain scale, PLG & Direct sales aren’t enough. A thriving partner ecosystem can be a strong GTM moat. Interestingly, a majority of HubSpot solution partners *only* sell & deploy HubSpot as a CRM, thus creating valuable network effects for the company.

7. In geo-expansion, less is better: PLG-driven companies will always have customers in many countries eg. Hubspot has 130+. But in order to deeply localize for elements like language, currency, customer support etc., it’s important to focus only on a few markets. As an example, HubSpot has chosen 7-8 markets to deeply localize their offerings in, based on factors like TAM, existing installed base, net ARR growth being seen & the company’s ability to serve the market locally.

While SMB SaaS can be a tricky business model, it compounds beautifully once the founders figure out its key levers, as HubSpot has shown.

PS: if you enjoyed this post, you might also find this post on Top 10 enterprise SaaS learnings from a unicorn founder helpful.

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