Robert Nishihara

2) 2026 setups coming into focus: Heading into 4Q EPS, average CY26 total revenue estimates are effectively unchanged vs. pre-3Q25 (-0.2% on average), though TTAN/VEEV/GWRE/WK all saw a >1% increase in estimates. Over the same period, CY27 total revenue estimates saw modest downward revisions (-0.8% on average), with only TTAN/VEEV/GWRE seeing positive revisions in our coverage (Exhibit 8). When assessing 2026 setups across our coverage, we note that Consensus estimates currently imply a ~2.5pt deceleration in YoY growth on average vs. 2025 Consensus growth. Across our coverage, only BL/WAY Consensus estimates imply an acceleration in growth off of 2025 levels (though we note 2026 estimates for WAY include inorganic contributions from the company's acquisition of Iodine Software, Exhibit 9). On 2026 total revenue estimates, we are most above Consensus for TTAN/WAY/VERX/GWRE and most below Consensus for VEEV/SPT/NCNO. For CY27, we are most above Consensus for TTAN/WK/NCNO (Exhibit 10). While we anticipate management teams across our SMID-cap software coverage universe will maintain a prudent approach to FY26 guidance (a continuation of the trend we observed throughout FY25), we expect investors will increasingly focus on the magnitude of quarterly beat-and-raises to get more positive on shares against what we view as effectively de-risked 2026 estimates.

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Systems first. AI next. The order matters, a lot. Over the past few weeks, Evan Allen and I have been digging into agentic workflows - reading papers, testing tools, and mapping patterns across different implementations. One insight keeps standing out. Agentic workflows aren’t inherently “AI.” They’re decomposition systems. The core idea is simple: take a complex task, break it into smaller, well-defined steps, execute those steps independently, then reassemble the output.  Who performs each step is secondary. 👉 Some steps are best handled by machines: speed, recall, pattern matching. 👉 Others still benefit from humans: judgment, accountability. 💡 Once you see it this way, the false binary between “automated” and "manual” disappears. For human-in-the-loop systems - like what we're building with Nexus - it shifts the goal from replacing people to assigning work to whoever (or whatever) does it best, step by step. AI becomes a delegate, not an endpoint. That also forces more discipline. You have to be explicit about what the task actually is, what good looks like, and where judgment truly matters. Sloppy workflows don’t magically improve when you add AI - they just fail faster. I suspect the most durable systems won’t be the most automated ones, but the most intentional about where humans stay in the loop. Where have you seen the balance work well, or break down?

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We recently had Brian Barritt, Co-Founder and CTO of Aalyria on Valley of Depth. Aalyria just raised a $100 million round at a $1.3 billion valuation. The company is building two core technologies: 1) Spacetime, a software orchestration layer designed to manage networks in motion, and 2) Tightbeam, a laser communications system delivering fiber-like speeds through the atmosphere. Together, they aim to solve one of the hardest infrastructure challenges in aerospace and defense: how to coordinate satellites, aircraft, drones, ships, and ground systems into a seamless “network of networks.” Brian and I cover: – How laser communications can reach 100 gigabits per second through atmosphere – The technical challenge of Earth-to-space vs. space-to-Earth optical links – Why interoperability has been a 40-year ambition inside the DoD – What resilience and roaming look like in hybrid satellite architectures and much more... Full episode in comments 👇

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