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Friday, October 16, 2026· 7222 stock ideas· 217 writers·
16,887 views· 7,825 clicks

Is The Market Turning The Page?

CoreWeave’s debt and equity price action is framed as a wake-up call for investors amid a broader market discussion.

CRWV $43B
WHAT ARE WE DOING? “Contrarians At The Gate” 38 clicks

From X

@convequity

Convequity on X

The market still prices $NVDA's moat on one idea: the GPU is special because it can do anything. But Nvidia has spent every generation trading that flexibility for raw AI horsepower. Its chips now look less like general-purpose GPUs and more like the AI-specific chips Google and Huawei build. That's great for performance, but it chips away at the very thing that set Nvidia apart. The closer its GPU gets to a TPU, the easier it becomes to replace. Deeper dive available at Convequity.
@StableBread

Fajasy on X

AI labs and data labeling companies (firms that turn experts’ knowledge into training data for the labs) could make my $12-19M sale range for Nubeva Technologies $NBVAF (TSXV: $NBVA) look conservative. My previous write-up’s sale comps were five cybersecurity companies buying small startups that detect, stop, or recover from ransomware, and I valued a Nubeva sale at $12-19M, the low end of those deals. AI labs and data labeling companies would widen the set of buyers beyond cybersecurity companies, but I found none that has bought a ransomware recovery company like Nubeva. The closest deals were for a stake in a ransomware recovery company, malware data, and specialist teams: → Microsoft $MSFT: Made an equity investment in Veeam, a data protection and ransomware recovery company, in February 2025, as part of a partnership to build AI-based threat detection and other Microsoft AI services into Veeam’s products. → Google $GOOGL: Owns VirusTotal, a malware-scanning site it bought in 2012, and Mandiant, the incident-response firm it bought for $5.4B in 2022. Its experimental Sec-Gemini model, introduced in April 2025, draws on Mandiant’s threat intelligence data. → OpenAI: Agreed to buy Promptfoo in March 2026 for an undisclosed price. Promptfoo, which tests AI systems for security holes, was valued at $86M after its July 2025 funding round, per PitchBook. → Mercor (a data labeling company): Agreed to buy Deeptune, which builds practice environments for AI agents, in July 2026, months after Andreessen Horowitz led Deeptune’s $43M Series A. The two 2026 deals didn’t disclose prices, and Mandiant and Veeam are far larger businesses than Nubeva, so I can’t put a new number on a sale. Still, my $12-19M range is probably conservative now, since it only counted cybersecurity companies as buyers, and an AI lab or data labeling company would be paying for Nubeva’s engineers and model rather than its ~$500K of FY2026 revenue. But an AI buyer wouldn’t get patents on what it’s paying for. Nubeva’s patents would cover how it recovers files and how it detects hidden encryption, but it won’t patent its binary analysis (figuring out what malware programs do) at all, and CEO Randy Chou explained why: “Anything you patent, that means you have to show how you did it. If you show how you did it, well, somebody can train an LLM to go do it.” So the model is only worth what Nubeva can prove it does, through a signed contract, a named partner, or test results.
@convequity 1 click

Convequity on X

3D chips could create more room for specialist EDA startups. We are not convinced that more difficult chip design automatically translates into a large revenue windfall for $SNPS, $CDNS and Siemens. Fine-pitch stacking changes decisions that used to be made within one die. Put a circuit upstairs and its wires may get shorter, but its heat affects the circuit below. Placement, signal timing and power delivery have to be optimized together across the stack. That creates a reason for $AVGO and $NVDA to build custom tools around their architectures. Broadcom already describes a proprietary design flow built on industry-standard tools. Nvidia publishes its own research on assigning connections across tiers in 3D chips. Our inference is that the chip designers will keep some of the most differentiating optimization work in-house. A startup that solves a costly gap in cross-die planning or optimization could also win business without replacing an entire EDA suite. The incumbents still supply implementation, verification and manufacturing sign-off tools, and they are developing 3D capabilities themselves. Internal tooling is evidence that customers need more than the standard flow; it does not establish that incumbent spending falls. We see a more interesting startup opportunity where a tool can shorten repeated design iterations or improve the resulting chip enough to earn a separate budget. That is the part of the 3D transition we would investigate before assuming the largest EDA vendors capture most of the upside.
@convequity

Convequity on X

We think $MNDY needs to make its platform verifiable to lead work management. MNDY has not disclosed its AI ARR in dollars. Our recent SaaS Survival Scorecard report estimates it at about $20 to $30 million as of the end of Q2 2026. Leading AI-native startups in other fields are far larger. In coding, Cognition reported crossing $1 billion in annualized revenue run rate on 25 September 2026, company-wide across Devin and Windsurf. In legal work, Harvey reported on 1 October 2026 that it crossed $400 million in ARR during Q3 2026. We think MNDY has long been highly adaptive, with great leadership and a strong recent cadence of features and products. But revenue growth has slowed, and in our view it has gained little from AI so far, because work management is a hard area for AI. In our view, coding agents suddenly became highly effective around early 2026, and work management has not yet had its own coding moment. Verifiability is the key difference: code can be tested, while most work management outcomes are hard to check. Rather than wait for that moment, how can MNDY make work management more verifiable? It could track and measure the actions taken on its platform and their outcomes, so that everything that happens there is verifiable. MNDY can learn from Palantir. Palantir launched AIP in 2023, its AI platform built on its ontology, and we think that ontology helped Palantir succeed with models much weaker than today's. Could MNDY build a similar substrate or ontology for work management in order to make the work done on its platform more verifiable? We do not claim to have the answer. Our view is that to thrive, and not only survive, MNDY needs to lead work management by making its platform verifiable, however it chooses to do that.

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