Another week and software continues to grind lower. However, despite all of the carnage, there was another big winner this week! Fastly is up ~100% over the last week. The week prior, 8x8 had the big week (they were up ~70% in a week). Always an opportunity somewhere… I thought I was done talking about “is software dead” after the last couple weeks Clouded Judgement posts, but I just had more thoughts I wanted to share… I think two things are true. I think people are simultaneously under and over estimating the impact AI will have on the existing software complex. The difference is the timing. Overestimating in the short term, and underestimating in the long term. I see a lot of arguments claiming software is dead because everyone will just vibe code their own software. I don’t buy this at

Well, software is dead again! At least investor confidence is dead… The median NTM revenue multiple for the cloud software universe is 4.1x. That’s the lowest it’s been in 10 years (it was about the same very briefly in 2016, when the fed started hiking rates for the first time after the GFC ZIRP period). The current median FCF multiple is 18.9x. The previous low in the last 10 years was ~26x! However, the “narrative violation” metric here is the growth adjusted revenue multiple median is still 0.35x vs the pre covid average of 0.28x (you can see the graph below, I post it every week). So while multiples are at historical lows, so are growth rates. The FCF multiple is the most telling, however. With the current median ~30% lower than the prior low point in 2016 So what’s going on?! I think

Every few weeks I see some version of the same claim: “The new system of record is the agent,” or “Workflows swallow systems of record,” or “Data is the system of record, apps are just thin views.” There is a grain of truth in each of those, but I think it is also easy to over-rotate and accidentally throw out the thing enterprises still need most, which is not a “system of record” as much as a reliable source of truth.When I think about systems of record, I do not actually think in product categories. I think in the much more boring lens of “where does the truth live.” In other words, if an enterprise workflow needs to know something at a specific step, where is the one place that answer is considered canonical. Because as workflows get more automated and more agent driven, the fragility

I want to write this week about a topic that’s been bugging me recently. It’s an irrational (in my opinion…) behavior that’s starting to emerge in earlier stage startup land more and more. The challenge is - I can see how we are getting to an irrational end state with a rational starting point… And it all comes down to how employees view and value startup equity. So this post is meant for all of the employees evaluating different offers from startups. Let me set the stage with a hypothetical conversationFounder: “I need to raise at the highest valuation possible. And after I do, I need to raise again in 6 months at an even higher valuation.”Me: “Why?”Founder: “Because that’s how I will attract and hire the best talent”Me: “Why? Wouldn’t new hires want a lower valuation for more upside?”Fou

Over the last year we have all been trained (at least I have…) to look at model launches through one lens. What is the benchmark score and who sits at the top of the leaderboard. It felt like every product announcement was immediately reduced to a scatter plot and a few social media hot takes about who beat whom by a few points on MMLU or GPQA. But something subtle started to shift with the Gemini 2.0 family and became much clearer with this week’s Gemini 3.0 launch. The story is no longer only about raw model quality. The frontier is getting crowded, the performance gaps are narrowing, and the real competition is moving up the stack into platforms. Said another way, the next decade in AI will be defined not only by model breakthroughs, but also by distribution, integrations, and the shape

The AI FactoryIf I zoom out for a moment and look at the current trajectory of AI infrastructure, it’s hard not to see an entirely new pattern forming. Inference keeps getting faster. Inference engines keep getting smarter. And the ecosystem around them keeps getting more modular and open. What once felt like specialized machinery locked inside a handful of labs is now drifting into the hands of every company with a GPU budget and a few strong engineers.Neoclouds like $CoreWeave, Inc.(CRWV)$ and $TOGETHER PHARMA LTD.(TGPHF)$ have rewritten the economics of GPU access. Inference clouds like Fireworks, Baseten and fal have done the same for reliable serving (and we’ve already separated into separate infere

If I had to simplify software market cycles, I’d say they come in two phases: the expansionary phase and the consolidation phase.In the expansionary phase, buyers scoop up software almost indiscriminately. There’s little concern for cost or efficiency, what matters is speed. It’s about accelerating product development, capturing market share, or outspending competitors to stay ahead, all under the assumption that growth will take care of everything else. During this phase, public markets shift their focus entirely to growth over profits. Take a look at the multiples chart I post later on breaking out multiples by high, medium, and low-growth companies. You can see the high-growth bucket has seen multiple expansion this year, while the mid-growth bucket has seen steady contraction.In the co

This week the 3 hyperscalers reported ( $Amazon.com(AMZN)$ AWS, $Microsoft(MSFT)$ Azure and $Alphabet(GOOG)$ $Alphabet(GOOGL)$ Google Cloud). What did we learn? Most importantly - they ALL called out still being meaningfully capacity constrained. CapEx guides are going up, data center builds are going up, power constraints are meaningful. This isn’t the telecom bust where the world laid fiber that was “dark” (ie unused). GPUs are being used the second the come online…Here are the numbers:AWS (Amazon): $132B run rate growing 20% YoY (last Q grew 17%)Azure (Microsoft): ~$93B run rate (estimate) growing 39% YoY (last Q gre

1. $Microsoft(MSFT)$ Azure at a ~$93B run rate growing 39% constant currencyQuarterly YoY growth trends below Line chart titled Azure YoY Growth (cc) displays blue line plotting quarterly year-over-year growth percentages from about 27 percent in Q1 2021 rising to 39 percent in Q3 2025 across x-axis quarters labeled Q1 to Q3 from 2021 to 2025 and y-axis from 0 to 60 percent with data points at 27 percent, 28 percent, 31 percent, 34 percent, 39 percent Estimates of Net new quarterly ARR added:ImageYoY growth in quarterly net new ARR added:Image2. $Alphabet(GOOG)$ $Alphabet(GOOGL)$ Google Cloud at a ~$61B run rate growing 34% (not constant currency). Google cloud

For years, every model release followed the same pattern: a flurry of charts showing performance gains across MMLU, HumanEval, GSM8K, and whatever other benchmark happened to matter that quarter. It was a scoreboard for intelligence, a nd every model came with its proof point. A few points higher here, a few tenths lower there. But something changed. Those charts stopped being interesting (at least to me…). Every model sits within a rounding error of each other now, and people seem to have quietly stopped caring. Benchmarks have become saturated.It reminds me a bit of the “index wars” during the early days of search. Back then, Yahoo, AltaVista, and Lycos all bragged about the number of web pages they had indexed, and the bigger the number, the smarter the engine. Then Google came along, a

If we look back over the last few years there are pretty clear patterns of “hot topic debates” that seem to pop back up every so often around AI. One I want to discuss today is the broad topic of “will scaling laws hold.”It’s a nuanced question, because “scaling laws” really mean many things, all of which trace back to data and compute. The debate was broadly could you keep throwing more data and compute at the models to make them better, or would they start to plateau. Over time, nuance has emerged. It’s not just about watching performance scale with more data / compute, but watching performance scale based on where / when / what type of data / compute you throw at models to make them better. Regardless of the nuance, the debate seems to perpetually oscillate between “they wont hold!” to

OpenAI just had their app store moment, and we may look back on it as one of the most significant announcement of the company’s history. The company introduced “apps” inside ChatGPT, and a development platform called Apps SDK to help develops build custom native ChatGPT apps. You can now call an app by name, “ $Spotify Technology S.A.(SPOT)$ , make me a playlist,” “ $Expedia(EXPE)$ , find me a flight,” “ $DoorDash, Inc.(DASH)$ , order my usual,” and ChatGPT will handle the interaction in-line, blending natural language, APIs, and lightweight interfaces into a single conversational flow. It’s a simple but profound shift: ChatGPT is no longer just a reasoning engin

This week on Clouded Judgement: The New RL Training Grounds- Median software multiple: 5.1x- High Growth software median: 27.0x- Mid Growth software median: 7.8x- Low Growth software median: 3.9x- 10Y: 4.1%ImageMore on this from Clouded Judgement today:The earliest days of reinforcement learning were fun to watch. Algorithms trained on Atari games, Starcraft, Go. The appeal was obvious: constrained digital sandboxes with clear rules, infinite repeatability, and instant feedback. You could run agents a million times through Pong and see them get better.But those environments were toys. Beating Pong doesn’t teach you how to navigate a hospital system. A high score in Breakout doesn’t tell you much about running a logistics network. The truth is, if agents are ever going to matter in the ente

I’ve found myself having a similar conversation with a number of investors and founders recently, and wanted to flesh it out a bit into a post. It’s a similar topic to the ERR vs ARR debate. I’m calling this one the “Easy come, Easy go?” debate…Let’s first start out with an undeniable truth - the fastest growing AI companies are defying the laws of gravity when it comes to scaling. The growth some of these companies are seeing is eye watering. 0-$100m in ARR in less than 12 months! Sometimes faster! There are many reasons this type of growth is possible, but I think it boils down to the fact that many markets in AI are truly greenfield, can demonstrate ROI incredibly quickly, and these together lead to crazy adoption cycles and growth.Yet despite this, I’ve found myself less “sure” of the