What's 🔥 in Enterprise IT/VC #353

How not to blow your hard-earned Series A round + Andy Jassy - we're just steps into a marathon for Generative AI in the enterprise

Here’s the best of Andy Jassy from AWS earnings transcript this past week. Net net, AI is everywhere, cloud optimization is over and lots of net new workloads, and it’s still early for AI in the enterprise. These are all themes I’ve been exploring the last few weeks, and Andy nails them all.

Andy Jassy on AI…”every single one has multiple generative AI initiatives going right now”

On the AI question, what I would tell you, every single one of our businesses inside of Amazon, every single one has multiple generative AI initiatives going right now. And they range from things that help us be more cost-effective and streamlined in how we run operations in various businesses to the absolute heart of every customer experience in which we offer.

Optimization is over + new workloads coming to the cloud - 12% YoY growth 📈

As the economy has been uncertain over the last year, AWS customers have needed assistance cost optimizing to withstand this challenging time and reallocate spend to newer initiatives that better drive growth. We've proactively helped customers do this. And while customers have continued to optimize during the second quarter, we've started seeing more customers shift their focus toward driving innovation and bringing new workloads to the cloud. As a result, we've seen AWS' revenue growth rate stabilize during Q2 where we reported 12% year-over-year growth.

More on what’s ahead - this certainly bodes well for enterprise infrastructure startups - Brian Olsavsky, CFO

So, while that is 12%, there's a lot of cost optimization dollars that came out and a lot of new workloads and new customers that went in. So, there was -- on our base, it's very large numbers. And when customers start to that cost optimization work, they can take some of their spend down for a while as they do that, and we help them do that, and been part of our DNA ever since we started AWS. So, that's all good.

What we're seeing in the quarter is that those cost optimizations, while still going on, are moderating, and many maybe behind us in some of our large customers. And now we're seeing more progression into new workloads, new business. So, those balanced out in Q2. We're not going to give segment guidance for Q3.

But what I would add is that we saw Q2 trends continue into July. So, generally feel the business has stabilized, and we're looking forward to the back end of the year in the future because, as Andy said, there's a lot of new functionality coming out with -- and there's a lot of spend that will be in this area for all the great solutions that are out there for generative AI and large language models, as well as machine learning solutions that we've always had for customers. So, optimistic and starting to see some good traction with our customers' new volumes.

It’s about the data!

But while we will build a number of these applications ourselves, most will be built by other companies, and we're optimistic that the largest number of these will be built on AWS. Remember, the core of AI is data. People want to bring generative AI models to the data, not the other way around. AWS not only has the broadest array of storage, database, analytics, and data management services for customers, it also has more customers and data store than anybody else.

AI Monetization - will be huge but still so early in enterprise

Yeah. Good question, Brett. What I would say is that we have had a very significant amount of business in AWS driven by machine learning and AI for several years. And you've seen that largely in the form of compute as customers have been doing a lot of machine learning training and then running their models and production on top of AWS and our compute instances.

But you've also seen it in the form of the 20-plus machine learning services that we've had out there for a few years. I think when you're talking about the big potential explosion in generative AI, which everybody is excited about, including us, I think we're in the very early stages there. We're a few steps into a marathon in my opinion. I think it's going to be transformative, and I think it's going to transform virtually every customer experience that we know.

But I think it's really early. I think most companies are still figuring out how they want to approach it. They're figuring out how to train models. They want to -- they don't want to build their own very large language models.

They want to take other models and customize it and services like Bedrock enable them to do so. But it's very early, and so I expect that will be very large, but it will be in the future

Switching gears, founders/investors, this is how not to blow your Series A 💰. Trust me it goes quickly if you ramp too fast without the underlying fundamentals.

As always, 🙏🏼 for reading and please share with you friends and colleagues.

---

Scaling Startups

1. Great reminder - must watch:

   “And the war is called survival. The war is not running out of money until we get our product on the market.”

   

2. Which means don’t be afraid to take a down round if need be

   

3. PSA…must read 🧵 on founders who build a data logic and analysis tool and didn’t make it…

   

4. 💯 - great for hiring, company culture, decision making

   

5. Startup founders, want to get paid faster? Check out Common Paper - contracts that get you paid…turns contracts into a billing workflow
   
   Gui Laliberte, CEO of Integral, says, “With Common Paper's automatic billing, 50% of our new customers pay us immediately. That means a huge reduction in manual work for our team and dramatically faster cash collections.”

---

Enterprise Tech

1. AI adoption in enterprise - McKinsey Survey

   

2. The SaaS Leaderboard: Q2 2023 from Vendr - great to see portfolio cos Snyk as #1 for Cloud and Application Security and Kustomer as #3 for CRM

3. What is AI’s impact on developers? Nicole Forsgren (Github, Microsoft Research) shares her thoughts - can I trust and rely on AI - also seeing that people shift the way they work when they work with AI-enabled tool - now instead of just writing code, you spend more time reviewing code instead of writing code - 50% of time now spent reviewing instead of writing code

4. Too many picks and shovels infra startups who don’t know what they’re building and for who - as I’ve said before, market is moving so fast, that first mover advantage is not one at the moment - from co-founder and CTO of Loom

   

5. Power of open source for foundational models - co-founder/CTO of Hubspot

   

6. The opportunity for every dev tools startup

   

7. Existential question - incumbents vs. startups when it comes to AI infrastructure

   

8. How to secure data from LLMs

   Cape Privacy relies on enclave technology to enforce privacy constraints. What is the value of enclaves and how do they relate to ML privacy?

   A secure enclave enables “confidential compute” which means it keeps data confidential while it is being computed, even from system admins and cloud providers. The Cape API runs entirely within a secure enclave, so no human can see what’s being processed (including the humans at Cape and AWS).

   We use secure enclaves for inference, embeddings, vector search, de-identification and even custom user-defined models and python functions.

   The Sequence Chat: Gavin Uhma – CTO, Cape Privacy on Security Guardrails for LLMs

---

Markets

1. Sign of the times…“Brookfield and Sequoia offshoot launch fund to target cut-priced start-ups” (FT)

2. End of an era: once worth $7.7B at height of pandemic and now…“Hopin, the struggling virtual conference unicorn, sells events and engagement units to RingCentral” (TechCrunch)