The Trilogy Times

AUSTIN, TEXAS — ESW Capital, the enterprise software acquisition machine built by Trilogy founder Joe Liemandt, has completed three acquisitions in recent weeks, adding social collaboration platform Jive Software and two smaller firms from rival acquirer Avolin to its sprawling portfolio.

The Jive acquisition, valued at $462 million, marks ESW's largest known purchase since its 2006 launch. Jive, once a high-flying social intranet provider that went public in 2011, has spent years struggling to compete against Slack and Microsoft Teams. ESW will fold it into Aurea, its CRM and customer engagement division, where it joins 16 other acquired brands.

Meanwhile, IgniteTech — ESW's meta-acquirer that itself buys enterprise software — quietly absorbed two companies from Avolin, a smaller competitor in the enterprise software roll-up game. The deals include sales acceleration platform XANT and an unnamed second asset. Terms were not disclosed.

The pattern is familiar: ESW targets mature, often underperforming enterprise software businesses with sticky customer bases, acquires them at 1–2× annual recurring revenue, and drives margins toward its internal benchmark of 75% EBITDA by staffing with rigorously vetted global remote talent from Crossover, Trilogy's recruiting arm.

Jive's customer list — which includes enterprise clients locked into multi-year contracts — fits the profile perfectly. So does XANT, whose predictive sales tools serve a niche market with high switching costs. Both will likely see support pricing increase and headcount shift to lower-cost geographies within months.

ESW does not disclose portfolio-wide financials, but industry observers estimate the division now manages 75–80 software companies generating roughly $1 billion in combined revenue. The playbook has not changed in 18 years: buy cheap, cut costs, raise prices, extract margin. The acquisitions keep coming.

AUSTIN, TEXAS — Skyvera is wasting no time turning telecom software consolidation into an operating strategy.

The ESW Capital-backed telecom portfolio company has completed its acquisition of CloudSense, a Salesforce-native CPQ and order management platform built for telecom and media providers. The move expands Skyvera’s ability to help operators modernize everything from quoting to order capture—two areas where legacy workflows have historically been a margin and customer-experience drag. TelecomTV framed the transaction as a portfolio expansion play, and the message is clear: Skyvera is assembling a more robust, carrier-grade suite with fewer integration headaches and more repeatable deployments. (See: Skyvera completes acquisition of CloudSense.)

But CloudSense is only one piece of the story. Skyvera has also been active in carving out cloud communications capabilities—TelecomTV has previously reported on Skyvera’s appetite for Kandy cloud assets—suggesting a deliberate push toward customer engagement and digital channels alongside core monetization plumbing.

Now comes a more pointed signal to the market: CEO Danielle Royston’s Skyvera has made an $18 million bid for Casa Systems’ wireless business, according to Light Reading. If the deal advances, it would extend Skyvera’s footprint deeper into wireless network-adjacent software, further rounding out a portfolio designed to serve operators facing capex pressure and modernization mandates. (Light Reading report.)

Key Takeaways

Skyvera is leveraging best-in-class M&A discipline to assemble an end-to-end telco software stack.

CloudSense strengthens the quoting-to-order journey inside Salesforce, a pragmatic path for many operators.

The Casa wireless bid underscores Skyvera’s intent to expand beyond BSS into broader telecom infrastructure-adjacent capabilities.

We’re just getting started.

AUSTIN, TEXAS — Two Forbes features published this week cast a harsh light on Joe Liemandt's Trilogy empire, describing Crossover — the company's global talent platform — as a 'global software sweatshop' and accusing Liemandt of attempting to 'turn workers into algorithms.'

The articles describe a model where remote workers are monitored constantly, their keystrokes tracked, their screens recorded. The implication: Liemandt pioneered remote work not to liberate talent, but to surveil it at scale.

But if you read between the lines, there's a counter-narrative here. Crossover claims to hire the top 1% of global talent and pay them identically regardless of geography — a model that, if true, would be among the most meritocratic in the industry. The question Forbes doesn't quite answer: Is rigorous performance tracking exploitation, or is it simply what elite remote work looks like when you remove the office?

The timing is notable. While Forbes was preparing its takedown, Liemandt's education venture — Alpha School — published a blog post claiming it has doubled students' odds of earning Division I athletic scholarships compared to traditional schools. The school's thesis: kids learn more from sports than from sitting in classrooms, so build a school around that insight.

Alpha's approach — 2 hours of AI-powered academics in the morning, the rest of the day spent on athletics, leadership, and entrepreneurship — is either visionary or reckless, depending on who you ask. The D1 athletics claim is bold. So is the idea that you can automate most of K-12 instruction.

Which brings us back to the Forbes framing. Liemandt has spent 35 years building systems that replace human repetition with software. The workers aren't becoming algorithms — the algorithms are doing what workers used to do. And this is where it gets interesting: whether you call that liberation or exploitation may depend entirely on whether you're the one being automated.

The Supreme Court has declined to hear Thaler v. Vidal, affirming lower court rulings that artificial intelligence systems cannot be designated as inventors or authors under current patent and copyright law. Both the District Court for the Eastern District of Virginia and the Federal Circuit Court determined that existing statutes require human authorship and inventorship for intellectual property protection.

The petitioner argued that technological advancement necessitates reinterpreting the Copyright Act of 1976 and Patent Act, but the Supreme Court's refusal to grant review maintains the current requirement that only natural persons can be credited as creators. Legal scholars suggest the Court deferred to Congress, which has yet to act on AI authorship despite numerous proposals.

The decision has significant practical implications: AI-generated inventions and works lacking sufficient human involvement cannot receive federal intellectual property protection, regardless of their commercial value or creative merit.

BEIJING — The open-source AI race just hit a new phase, and I cannot overstate how significant it feels: China is moving at startup speed at national scale, U.S. labs are counterpunching with downloadable enterprise-grade models, and the entire world is watching one uncomfortable truth—“open” still runs on a surprisingly closed foundation.

On the China side, the momentum is no longer a curiosity; it’s becoming infrastructure. A growing wave of Chinese open models is showing up with solid benchmarks, permissive licenses, and a relentless cadence of releases. As The New Stack reports, China is increasingly “winning” the open-source AI race in terms of visible model output.

But here’s the plot twist: much of this supposedly open ecosystem is still chained to a U.S.-controlled substrate—especially the GPU supply chain and the software platforms developers build on (think CUDA-like gravity wells). In other words, you can open-source the brain, but if the body can only run on one kind of nervous system, control hasn’t disappeared—it has just moved down a layer.

Meanwhile, U.S. players are not standing still. Google is pushing the ceiling on what “open-ish” can mean with Gemma 4, pitching “byte for byte” capability gains that matter in the real world: lower cost, smaller footprints, and faster iteration loops. Add Google’s broader March 2026 AI announcements, and the message is clear—model efficiency is now a product strategy, not a research footnote.

And then there’s the enterprise angle: Arcee’s newly released, open-source Trinity-Large-Thinking is being positioned as a rare U.S.-made model that companies can actually download, customize, and run where their data lives—an increasingly non-negotiable requirement.

Put it together and the future is now: the “open model” era is becoming a geopolitical supply chain story. The next battle won’t just be whose model is best—it’ll be who controls the compute, the tooling, and the standards that make open AI real.

STANFORD, CALIFORNIA — For four billion years, intelligence on this planet had exactly one substrate: carbon. Now, in the space of a single decade, silicon has begun not merely to mimic that intelligence but to interrogate it — to peer into the living brain with a fluency that neuroscience alone never achieved.

Three converging lines of research published this week suggest we have entered a remarkable feedback loop: AI systems modeled on brains are being used to decode brains, which in turn is teaching us to build better AI.

At Stanford, researchers are deploying generative AI to better understand brain diseases — using large models to synthesize patterns across neuroimaging data that human analysts might take years to identify. The approach promises to accelerate research into neurodegeneration, that slow unraveling of the most complex object in the known universe.

Meanwhile, a team has built what they call a "mini-AI" — a deliberately compact neural network — that can decode the visual processing of the macaque brain with startling fidelity. The finding is poetic in its recursion: a system inspired by biological neurons is now accurate enough to predict what biological neurons will do next. The fact that a small model suffices is itself a revelation, suggesting that the computational principles of primate vision may be more elegant — more compressible — than previously assumed.

And at a major global conference, Georgia Tech researchers spotlighted brain-inspired AI architectures that abandon the brute-force scaling paradigm in favor of neuromorphic designs — circuits that spike, adapt, and consume energy the way actual cortical tissue does.

Taken together, these developments describe something unprecedented in the history of science: a tool that is simultaneously the subject and the instrument of inquiry. We built neural networks as loose metaphors for brains. Now those metaphors have matured into microscopes.

The practical stakes are enormous. Neurological diseases affect over a billion people worldwide. If generative models can compress the diagnostic timeline from years to months, the human payoff dwarfs any benchmark score.

But the deeper resonance is almost philosophical. Every time an artificial network successfully predicts a biological one, it narrows the space of possible theories about how minds work. We are not just building smarter machines. We are, for the first time, building machines smart enough to help us understand what smartness is.

The mirror between carbon and silicon grows clearer by the week. What stares back is starting to look familiar.

NEW YORK — A growing body of evidence suggests artificial intelligence’s long-promised productivity revolution is encountering a familiar, highly preventable obstacle: the continued requirement that humans possess expertise.

Corporate leaders who spent the last 18 months forecasting a future where work is completed automatically by a polite autocomplete box are now learning that, in practice, the box frequently completes thoughts no one would ever have on purpose. According to a recent warning about the “human expertise” still needed to realize AI gains, the problem is not that AI can’t generate output—it’s that it can’t reliably generate output that any organization would like to defend in public or court. In other words, it is astonishingly productive at producing things that must immediately be reviewed by an adult.

The experience has been especially jarring for managers who interpreted “augmenting knowledge workers” to mean “replacing knowledge with vibes.” In theory, AI removes drudgery; in reality, it enthusiastically accelerates the creation of memos, code, decks, and strategic roadmaps that feel like they were drafted by a committee of sleep-deprived interns tasked with summarizing a different committee of interns.

Goldman Sachs has reportedly committed the rare indignity of looking at numbers, with recent analysis challenging the idea that AI adoption is already translating into measurable productivity gains at scale. The data, inconveniently, does not yet reflect the popular theory that adding a chatbot to a workflow instantly converts an average employee into a tireless, cross-functional titan. The financial community’s skepticism, outlined in coverage of Goldman data challenges AI productivity claims, has forced some executives to pivot from “we’re saving time” to “time is a social construct.”

Meanwhile, workplace observers have begun documenting the rise of what Harvard Business Review has dubbed AI-generated “workslop”—a sticky flood of plausible-sounding material that increases activity while quietly lowering the ratio of meaning to keystrokes. Organizations are discovering that the easiest way to become “AI-first” is to become “review-first,” as employees spend afternoons policing fabricated citations, hallucinated policy references, and emails written in the soothing tone of someone trying to get you to sign a lease.

The most disciplined response has come from companies allegedly taking cues from Meta’s purported “AI strategy” around hiring, productivity, and layoffs—an approach described in a viral post as scalable and global, which is a very efficient way of saying “fewer people, more output, and no one ask too many questions.” The concept is gaining traction because it offers a clean narrative: any productivity that appears is proof of AI, and any productivity that doesn’t appear is proof the remaining humans weren’t trying hard enough. The discussion has been fueled by reports like a viral post claiming Meta’s AI strategy is going global, which has helped executives everywhere discover the spiritual comfort of believing layoffs are actually innovation.

Perhaps no corporate transformation better captures the era than the reported pivot of shoe company Allbirds to AI—a move signaling that if you cannot sell footwear, you can at least sell the idea that footwear is now a data problem. Analysts say the pivot is logical: shoes are notoriously difficult to monetize, while AI initiatives can be valued on hope, narrative, and the number of times a CEO says “agentic.”

For now, companies remain confident the productivity promise will arrive as soon as employees stop wasting time being experts and start focusing on what really matters: generating more work for other humans to fact-check.

WASHINGTON — There's a moment in every dystopian novel where you realize the surveillance state didn't arrive with jackboots and midnight raids. It arrived with convenience. With efficiency. With a pair of sunglasses that recognize your ex across a crowded bar.

Meta wants to put facial recognition into Ray-Bans and Oakleys. The ACLU, along with 75 other organizations, is sounding alarms that should make your skin crawl. But here's the thing: we stopped being surprised by this somewhere around the third time we agreed to terms and conditions we didn't read.

And yet.

While we were arguing about whether AI-powered eyewear crosses a line, the Department of Homeland Security was already miles past it. New legislation introduced by Ranking Member Thompson aims to curb DHS's mobile biometric surveillance—a phrase that should make you pause and reread it slowly. The American Immigration Council reports that DHS's AI surveillance has crossed from monitoring non-citizens into tracking Americans. Mission creep, they call it, as if the mission ever had boundaries that mattered.

The ACLU has been fighting overbroad digital search warrants, slowly building case law that might—might—constrain how law enforcement hoovers up our digital lives. Small victories in a war we're losing by degrees. Because here's what keeps me up at night: every surveillance technology ever deployed has expanded beyond its original scope. Every. Single. One.

We build systems that promise precision and deliver dragnet. We're told facial recognition will catch terrorists, and then it's deployed at protests. We're assured AI will eliminate bias, but study after study shows it amplifies existing prejudices, baking discrimination into code that feels objective because it's mathematical.

What does it mean to be human in a world where your face is always searchable? Where your presence in public space is logged, analyzed, cross-referenced? We're not talking about some far-off future. This is happening now. The infrastructure is being laid while we scroll past headlines about it.

The optimists will say regulation can fix this. That we can have the convenience without the surveillance. That we can build guardrails strong enough to contain technologies designed to see everything. I want to believe them. I really do.

But every time I see another story about AI bias, another report of surveillance expansion, another tech company promising to self-regulate, I think about how we got here. One convenient compromise at a time. One efficiency gain that seemed harmless. One feature that made life just a little bit easier.

We built the panopticon and called it progress. We're only now realizing we're inside it.

...but at what cost?