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Private equity puts a floor under software valuations, and you can work out that floor with simple arithmetic. A private equity buyer takes the company private, removes unnecessary staff, lifts margins to 30%+ and keeps growth in the low teens. A company restructured that way should be worth at least 20x EV/EBIT. At a 33% EBIT margin, 20x EV/EBIT is about 6.7x EV/Sales. That gives a simple test for any software stock. If it trades well below that level, the market values the business at less than a buyer could get from it just by cutting costs. In our view, the market sees no differentiated future for that company. So why don't more of these companies get restructured? One reason is ownership. Dual-class share structures are common across large-cap and mid-cap tech, and they limit how much pressure private equity and activist investors can apply. That makes it hard for a company like $OKTA to accept legacy status and carry out a deep restructuring. We expect many SaaS companies to keep fighting slowing revenue with high operating expenses rather than restructure.
@convequity

Convequity on X

$FTNT already has a fast-growing OT security business of more than $1bn. Robots will face the same security problems as factory machines. Factory equipment and IoT devices were designed for availability and safety. They shipped with weak or no security, and controls were added afterwards. Robots and humanoids are likely to follow the same pattern. Many will run custom or stripped-down operating systems, have tight CPU and power budgets, and sit under safety-certification rules that make heavyweight endpoint agents and frequent patches difficult. When security software is hard to install on the device, enforcement moves to the network: segment the environment, control which systems can talk to which, and inspect the traffic. That work needs a lot of switches inside the plant, in addition to a few firewalls at the perimeter. FortiSwitch is built to be price-competitive for cost-conscious industrial buyers, with security functions embedded and ASIC acceleration for always-on inspection. Endpoint vendors such as $CRWD can help protect higher-value machines, but their software has to run on the device itself, which is often hard on robots for the same reasons. As inference moves into factories, hospitals, warehouses and vehicles, every new AI-controlled actuator still sits on a network, often on the industrial backbones Fortinet already protects.
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Convequity on X

$NVDA vs $AVGO: The Optical Edge NVIDIA can keep growing while facing pressure from more directions. Custom chips, optical supply constraints and specialized inference systems give customers more reasons to consider alternatives. Broadcom’s optical capabilities could help it win the accompanying compute order. Customers need both accelerators and the optical components that connect them to bring capacity online. Securing those components alongside an ASIC purchase could shift the customer’s platform choice toward AVGO. Control across lasers, photonic and electronic chips, and packaging also gives Broadcom more freedom to optimize the complete design. Embedded lasers could reduce the laser power required, easing dependence on scarce, high-power external lasers, although replacement becomes harder. For AVGO, this remains a potential design route. NVIDIA can respond through partnerships, but separate suppliers still have their own margins and product priorities. Google ($GOOGL) and Amazon ($AMZN) are expanding the alternatives through TPUs and Trainium. Our ABBX model allocates 10GW of Anthropic’s 13.8GW planned capacity to those two platforms. The plans span different timelines, but show how a few large buyers could redirect substantial spending away from GPUs. Huawei adds pressure through its integrated optical approach and the prospect of Chinese models supplying cheaper tokens internationally. That could affect both hardware choices and the prices compute providers can charge. Cerebras ($CBRS) targets workloads where faster responses matter. It can compete for latency-sensitive decoding without replacing GPUs across every stage of inference. NVIDIA’s defenses remain substantial. GPU batching supports high total output, flexibility across workloads matters, and its ecosystem is deeply established. Customers can use GPUs for prefill and specialized chips for decoding. Wider enterprise model development would also favor NVIDIA because smaller buyers have less reason to fund custom-chip programmes. At comparable valuations, we prefer adding to AVGO: it can benefit from custom-silicon adoption while helping customers secure the optics needed to deploy it. For NVIDIA, the key question is how much market share and pricing power it retains as customers gain more alternatives.

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