The AI Daily Brief: Artificial Intelligence News and Analysis
How Harness-as-a-Service Will Change Agents
30 Apr 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is Harness-as-a-Service and why is it important?
Today on the AI Daily Brief, the emergence of Harness as a service, what it means for the agentic era. And before that, in the headlines, a big tech AI earnings blowout. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in.
First of all, thank you to today's sponsors, KPMG, Blitzy, Granola, and Section. To get an ad-free version of the show, go to patreon.com slash aiDailyBrief, or you can subscribe at Apple Podcasts. If you want to learn more about sponsoring the show, send us a note at sponsors at aiDailyBrief.ai.
Chapter 2: How are major tech companies performing in AI earnings?
And lastly, if you haven't yet, go check out the new AgentOS program. It's a tool-agnostic, adaptable system for building an agentic operating system. And I have a feeling after you listen to today's Harness as a Service episode that you will want to dig in even more. You can find that off of the main site, ai-dailybrief.ai.
Today is one of those rare days where the headlines are all around the same theme. And that theme is, of course, big tech earnings. And to not bury the lead, let's go over to Shea Ballour, who writes, Hard to take the AI bubble argument seriously when some of the largest companies on Earth are still putting up these growth numbers. Google Cloud. plus 63% year over year.
Microsoft Azure, plus 40% year over year. MetaRevenue, plus 33% year over year. AWS, plus 28% year over year. We're going to go through all of these and talk about the winners and losers and what it means for the market's assessment of AI overall. Google was the clear winner on Big Tech Earnings Night, delivering huge beats across the board.
They reported 22% top-line revenue growth, but as I just mentioned, the big numbers were in their AI-related businesses. Google Cloud has experienced 63% revenue growth over the past year. They also reported a $460 billion backlog in new orders, up from 240 at the end of Q4.
Their new deal with Anthropic contributes a decent chunk of that growth, but it still shows that GPU demand is off the charts. Analyst Joseph Carlson posted the chart of Google's cloud backlog going exponential and commented, "...this is so crazy it literally looks fake." Gemini growth was similarly strong.
Google reported a 40% surge in paid enterprise customers quarter over quarter, meaning maybe I'm going to have to eat my hat rating Google as low as I did on enterprise in the AI lab power rankings. In addition, Google's infrastructure is now processing 16 billion tokens a minute, up 60% quarter over quarter.
Even search, which is tangentially related to some of Google's AI changes, is experiencing a boom, with search revenue up 19% year over year. Google is also maintaining very healthy profit margins, hitting $62.6 billion in net income for an 81% year-over-year gain.
CEO Sundar Pichai told analysts that AI is now the largest tailwind for cloud, commenting, Our enterprise AI solutions have become our primary growth driver for cloud for the first time in Q1. He added, though, We are compute-constrained in the near term. Our cloud revenue would have been higher if we were able to meet the demand. CapEx targets, meanwhile, remained pretty anchored.
Google slid up their forecast slightly, increasing this year's guide from a range of $175 to $185 billion to a range of $180 to $190 billion, although right now they're not coming close to hitting that target. They only recorded $35.7 billion in CapEx spending for Q1, which annualizes out to $140 billion and change.
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Chapter 3: What are the implications of Google's recent AI growth?
Responding to questions about losing exclusive access to OpenAI's models, Nadella downplayed concerns, stating, We have a frontier model, royalty-free, with all the IP rights that we will have access to all the way to 32, and we fully plan to exploit it.
Over the past few months, the market has lumped Microsoft in with software stocks and included it in the SaaSpocalypse, with the stock down more than 10% so far this year. Unfortunately for them, last night's earnings didn't seem to do anything to change that viewpoint, with the stock whipsawing up and down throughout the overnight session, but ending the night basically flat.
Ultimately, nothing disastrous or spectacular came out of the earnings call. They continued to add GPUs to their fleet at a tremendous pace, but a pace that feels conservative compared to Google's numbers. It's a big positive to see Copilot growing, but the negative take is that $20 million is still a drop in the ocean compared to the roughly $320 million paid seats for Office 365.
After earnings were released but prior to the investor call, Gene Munster of Deepwater Management wrote, Microsoft is in a tight spot because they can't shake the negative narrative. Stock is down 1% on solid numbers. This past month, Microsoft is up 17% in line with the Nasdaq. My take?
They need to make a statement on the call tonight and make it easy for investors to believe that they have AI-powered products beyond Azure that customers must have. Copilot has been a miss. Nadella ultimately didn't deliver that big statement, and so the company continues to perform like a perfectly average tech stock.
not a bad place to be during the AI boom, but Microsoft is starting to fall behind their peers who are making bigger moves. Now on the meta front, the past year has been all about rapidly expanding CapEx. Beginning in 2024, Mark Zuckerberg seemed to make the decision that he would continue accelerating data center spending regardless of investor concerns.
Meta now find themselves with the largest market cap-adjusted capex commitments among the tech giants. Over the past few quarters, the narrative was that revenue growth was accelerating fast enough to justify the AI spend, and that Zuckerberg had earned the right to spend big on data centers by delivering solid returns.
Analysts were linking increasing advertising revenue directly to AI optimizations in Meta's ad platform. Heading into Wednesday night's earnings, the balance between revenue growth and capex remained the central question. The financial reports showed that Meta had delivered another record quarter, Quarterly revenue came in at $56.3 billion, up 33% over the past year.
Both total revenue and net income beat analysts' forecasts by a fairly significant margin. On the other side of the ledger, Meta hiked capital expenses once again. They increased this year's forecast from $135 billion to $145 billion. Notably, this wasn't because Meta was tacking on another data center or two.
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Chapter 4: What role does Amazon play in the AI landscape?
The conversation has matured to the point where in a recent interview with Ben Thompson, Sam Altman was asked, how important is the harness, the runtime around the model, the tools, the state, to making agents actually work? Sam said, Like, my experience of using these, I am very aware of the fact that I don't always know when I fire something off in codex and it does an amazing thing for me.
I don't know how much credit Ben Thompson fills in was that the model is amazing or the harness is amazing. To which Altman responds, yeah, exactly. So we now have these two very different vectors of increasing AI capability. There's the underlying models and the changes there, but then there's also improvements in the harness that surround them.
Of course, another part of what has made this year feel so different and what has empowered so many different people is that we got an open harness, even though that's weren't what most people were calling it at the time, that actually allowed us to build the structure around agents to have them do the things that we'd always imagined agents being able to do.
I'm talking, of course, about OpenClaw. And yet, OpenClaw was not plug and play. You had to do everything from picking the model, to write the system prompt, to define the tool, to wire the agent loop, i.e. the part that decides what to do next, that dispatches tools, handles results, decides when to stop. You had to manage context. You had to handle errors.
The user building their OpenClaw had to orchestrate sub-agents when they needed parallel work, they had to figure out how to store state between runs, they had to figure out where to deploy it, how to monitor it, and if something broke, you fixed it. If you wanted a new capability, you built it. Every layer of the stack was yours to assemble, configure, and maintain.
I don't think it's a totally inappropriate analogy to look at this almost like the hobbyist era of computing. In a recent post on LinkedIn, Anders Karlsson wrote about the forgotten era in computing. In the post, Anders talks about how they started with a computer in their school, a little mini-computer called an Alpha LSI, but quickly decided that they wanted a computer for their self.
They continue, CompuKit UK 101. This was a kit in the true sense of the word. Not just a pre-produced circuit board that you put in a case, but an unpopulated circuit board, some chips and other components, and a very rudimentary assembly manual. You needed a soldering iron, some basic tools, and a ton of patience to build this thing.
This might be hard to grasp for people using computers these days, but for a few years in the 1970s, just after Ed Roberts had released the Altair 8800, and before the Apple II and that generation of computers came around, there was a short era of these truly hobbyist computing.
In other words, there was this period, believe it or not, where the way that a lot of people were interacting with computers was having to build and assemble them themselves. You got a kit in the mail and you had to put it together. Now, obviously, this period didn't last long and the number of people who were willing to do this was pretty small.
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