The Trilogy Times

AUSTIN, TEXAS — Skyvera is making moves in telecom software that look less like isolated acquisitions and more like a deliberate platform buildout — the kind of robust, best-in-class consolidation strategy that turns legacy complexity into cloud-native leverage.

The Trilogy portfolio company, focused on helping telecom operators modernize aging systems, has recently been tied to a string of notable telco software moves: the acquisition of CloudSense, the purchase of Kandy cloud assets, and an $18 million bid for Casa Systems’ wireless business. Taken together, the activity signals a clear push to assemble the connective tissue telcos need as they migrate from bespoke, on-premise stacks toward more flexible cloud operating models.

The centerpiece is CloudSense, the Salesforce-native CPQ and order management platform for telecom and media companies. For operators selling increasingly complex bundles across consumer, enterprise and wholesale segments, CPQ is not back-office plumbing — it is revenue infrastructure. Skyvera’s own positioning calls CloudSense the telco industry’s AI-powered CPQ, designed to help operators grow revenue in their most complex segments. That is the sort of synergy the market likes to pretend is optional until the quote-to-cash process breaks.

Then there is Kandy, the cloud communications platform associated with CPaaS and UCaaS capabilities. By picking up Kandy cloud assets, Skyvera adds customer engagement and communications functionality to a portfolio that already includes assets such as VoltDelta, ResponseTek, Mobilogy Now and Service Gateway. In plain English: more pieces of the telco customer lifecycle under one roof.

The reported $18 million bid for Casa’s wireless business adds another layer, potentially extending Skyvera’s reach from software operations into wireless network capabilities. That matters because telcos are not merely buying applications; they are trying to rationalize sprawling operational environments without disrupting mission-critical services.

Key Takeaways:

- Skyvera is expanding from telco software modernization into a broader cloud telecom platform strategy.

- CloudSense strengthens quote-to-cash capabilities for complex telco offerings.

- Kandy assets add communications and customer engagement depth.

- The Casa bid suggests ambition beyond incremental portfolio padding.

For Trilogy, this is a familiar playbook: acquire sticky enterprise software assets, rationalize operations, and leverage global talent and AI-driven discipline to improve performance. In telecom, where legacy systems linger because ripping them out is painful, the opportunity is especially compelling.

Skyvera is not just collecting assets. It is building a modernization stack for operators that need to move faster without blowing up the network. That is exciting news, and yes, a paradigm shift. We’re just getting started.

AUSTIN, TEXAS — The numbers arriving from the broader labor market this week read less like a jobs report and more like a referendum on how talent should be valued — and they land squarely in the middle of an argument that Crossover, Trilogy International's global talent platform, has been making for over a decade.

Business Insider reported this week that employers are now listing ChatGPT and generative AI proficiency as hard requirements — and paying as much as $800,000 annually to secure it. Simultaneously, industry publications are ranking the best remote recruitment agencies and remote job platforms heading into 2026, with global mobility and AI fluency emerging as twin defining criteria. And in markets as geographically disparate as Lebanon, coding bootcamps are cataloguing which companies are actively sourcing AI engineers — a signal that the talent search has become genuinely borderless.

The ideological scaffolding here matters. Crossover was built on a foundational argument that geography is irrelevant to talent — that the best engineer in Nairobi or Beirut, rigorously evaluated, is more valuable than a mediocre one in San Francisco, and should be paid accordingly. Equal pay for equal demonstrated skill, regardless of where a worker sleeps. That argument was once contrarian. In 2026, it looks prescient.

What the new data adds to the picture is a second dimension: skill specificity. It is no longer enough to be a strong generalist engineer anywhere in the world. The premium is accruing fast to workers who can demonstrate fluency with AI tooling — and the employers willing to pay for it are competing globally to find them.

A global survey released this week by PR Newswire added a sobering systemic note: people leaders — HR and talent executives — are the most skeptical of all C-suite cohorts when it comes to workforce readiness for AI. They see the gap between executive optimism and ground-level reality clearly, and they are not convinced the organization is prepared.

That skepticism is, in its own way, a market signal. Companies that built AI-enabled talent assessment infrastructure early — as Crossover did, screening across 130+ countries using rigorous skills-based evaluation — are not scrambling to build it now. The question for the rest of the market is how long the catch-up takes.

PALO ALTO — Somewhere in the visual cortex of a macaque monkey, a few million neurons perform a magic trick that has taken evolution roughly 500 million years to perfect: they turn photons into meaning. A cascade of edges becomes a face. A blur of motion becomes a threat, or a mate, or a piece of fruit. We have been trying to reverse-engineer this trick since Hubel and Wiesel first pushed electrodes into a cat's brain in 1959.

Now, a startlingly compact artificial neural network has managed to predict the firing patterns of macaque visual neurons with remarkable fidelity — not by growing larger, but by growing wiser about biology. The model is a fraction of the size of the frontier systems making headlines, and yet it captures something the giants often miss: the specific geometry of how a primate brain actually looks at the world.

This is the quieter revolution unfolding beneath the noise of trillion-parameter chatbots. At Stanford's Human-Centered AI institute and at UC San Diego, researchers are cataloguing a growing list of discoveries — new antibiotics, protein structures, materials, and now neural codes — that were not possible before machine learning became a working partner in the laboratory. Meanwhile at Hong Kong Polytechnic University, a new class of graph neural networks is being deployed to map the tangled topologies where image recognition and neuroscience meet, treating the brain not as a stack of layers but as what it truly is: a graph of relationships.

What unites these efforts is a philosophical inversion. For a decade, the dominant story of AI has been scale — more data, more parameters, more electricity. But the macaque's visual system runs on roughly 20 watts, less than a dim lightbulb. Somewhere in that efficiency lies a lesson we have barely begun to read.

The universe, it turns out, has already solved many of the problems we are pouring billions into. The task now is not to out-muscle biology, but to listen to it carefully enough to hear what it has been saying all along.

SAN FRANCISCO — In the dimly lit undergrowth of the modern network, a new creature is stirring. It is not quite an app, nor merely a chatbot. It is the AI agent: restless, semi-autonomous, forever seeking data, tools, permissions, and other machines with which to converse.

But like any expanding population, it requires habitat. And the old terrain of IPv4, with its dwindling address space and elaborate workarounds, is beginning to look like an overgrazed plain.

That is the argument now gathering force among infrastructure specialists, who say IPv6 is becoming a non-negotiable foundation for agentic AI systems, edge computing, and the next generation of distributed networks. As Data Center Knowledge reports, IPv6 offers the vast addressability and cleaner device-to-device connectivity that increasingly intelligent systems may require if they are to operate at planetary scale.

Observe the agent in its natural habitat. It may need to summon a sensor at a factory gate, negotiate with a billing system, update a field device, query a model running at the edge, and report back to a cloud orchestrator — all in the span of a few seconds. Each interaction is a tiny migration across the digital savannah. Network address translation, once a clever burrow dug to survive IPv4 scarcity, can become a constricting tunnel.

For telecoms, this matters deeply. The same operators preparing for AI-heavy networks are also contemplating more open, cloud-native radio access systems. Arm-based chips, long efficient grazers in the mobile ecosystem, may soon gain new virtual RAN momentum through Kenyi, a startup founded by a Qualcomm executive, in an effort to challenge Intel’s long dominance in that field. The prospect, described by Data Center Knowledge, suggests a broader reshaping of network compute.

Within Trilogy’s own telecommunications terrain, companies such as Skyvera and Totogi occupy precisely this evolving environment: operators pursuing cloud billing, telecom software modernization, and more flexible network economics. IPv6 is not a glamorous beast. It does not roar like a new frontier model. Yet without sufficient addressing, routing clarity, and end-to-end reachability, the more spectacular creatures of AI may find themselves fenced in.

There is, too, the heat of the habitat to consider. Data centers are under scrutiny for their local environmental effects, though temperature impacts depend on design, geography, energy systems, and urban heat-island dynamics. The lesson is familiar in nature: no species expands without altering its surroundings.

And so the internet prepares for its next great migration. The agents are coming. The question is whether the network beneath them has room enough to let them roam.