The Trilogy Times

CHICAGO — The teacher-free classroom is coming to the Midwest. Alpha School, the Austin-based private institution founded by Trilogy CEO Joe Liemandt, will open its first Chicago campus this fall — part of a national expansion that has education experts divided over whether AI tutors represent the future of learning or a dangerous experiment with children's development.

The school's model is stark: students spend two hours each morning mastering academic content through adaptive AI software, then devote the rest of the day to entrepreneurship, public speaking, financial literacy, and athletics. No human teachers deliver instruction. Adult facilitators supervise, but the curriculum is entirely algorithmic.

The 74 reports that Alpha students consistently test in the top 1–2% nationally on standardized assessments, mastering grade-level content in a fraction of the time required by traditional schools. Liemandt claims the approach proves that most classroom time is wasted on inefficient instruction methods.

But critics warn that removing human teachers eliminates mentorship, emotional support, and the social modeling children need. The Guardian's visit to Alpha's San Francisco campus found students engaged but raised questions about whether algorithmic efficiency can replace the intangible benefits of human connection in education.

The Chicago opening follows recent launches in Miami and Brownsville, Texas. Liemandt has committed $1 billion to scaling the model globally through Timeback, his "Shopify for schools" platform designed to let entrepreneurs replicate Alpha's approach.

At $40,000–$65,000 per year, Alpha remains accessible only to affluent families — a fact that complicates its claim to be solving education's systemic problems. The question isn't whether AI can teach faster. It's whether a school without teachers can produce educated humans.

AUSTIN, TEXAS — IgniteTech is in that particular mood executives get when the pipeline is hot, the checkbook is open, and the press releases read like a credits roll… three software products acquired… a new services arm spun up to go hunting wasted cloud dollars… and, yes, Jive is suddenly hanging around again.

First, the acquisitions… IgniteTech says it’s added three software products to its portfolio, continuing the ESW-style play: buy established enterprise tech, tighten the machine, and sell the outcome, not the org chart… the company’s announcement leans hard into “growth,” but the subtext is classic: more surface area, more cross-sell, more leverage with the same remote-first operating discipline. The deal(s) are bundled under one banner here: IgniteTech’s acquisition roundup.

Then comes the twist: Hand.com… a services arm pitched as a money-saver for cloud spend… the kind of offer that lands well with CFOs who’ve started treating AWS bills like horror fiction… “save millions,” they say… and the timing’s no accident. As AI workloads balloon, cloud waste is the new corporate smoking habit… and IgniteTech wants to be the nicotine patch. The company’s framing is in this release: Hand.com services debut.

And Jive… the enterprise social veteran that refuses to die… now listed among IgniteTech’s “leading solutions,” per its own announcement… Word is customers still love the sticky comms layer, even when the rest of the stack has moved on.

Meanwhile, a little bird points out the philosophical soundtrack: Trilogy founder Joe Liemandt doing the rounds, dismissing MBAs as low-yield theater… It’s the same worldview that powers this whole apparatus: automate the repeatable, staff the elite, and never pay retail for reinvention.

Gartner's 2025 Magic Quadrant for Content Marketing Platforms signals a fundamental shift: enterprises now prioritize workflow rigor, governance, and measurable ROI over publishing capabilities alone. The conversation has moved from "who helps me publish" to "who helps me operate" — a critical distinction as marketers rationalize sprawling MarTech stacks in an AI-saturated landscape.

For Contently, recently acquired by Zax Capital (an ESW/Trilogy division), this positioning is advantageous. The platform combines content marketing tools with creator marketplace access and analytics-driven governance, enabling global brands to scale operations without chaos. Standardization, analytics, integrations, and operational control are now table stakes.

The distribution landscape reinforces this trend. Industry data shows attention consolidating around repeatable formats, measurable subscriber economics, and consistent editorial cadence. For content marketing platforms, the synergy opportunity lies in helping teams plan, produce, and optimize content across owned channels — not just rented social real estate.

Governance and analytics have become non-negotiable as marketing leaders demand defensible, boardroom-ready ROI metrics.

LA JOLLA, CALIFORNIA — For three and a half billion years, intelligence on this planet had exactly one substrate: biology. Neurons, synapses, electrochemical gradients — the whole wet, warm, astonishing apparatus that lets you read this sentence. Now, in a development that future historians may regard as one of the great inflection points in the story of mind, artificial intelligence and biological intelligence are entering a phase of mutual illumination.

Consider the breadth of what's unfolding. At UC San Diego, researchers have catalogued nine distinct scientific breakthroughs made possible by AI — spanning drug discovery, materials science, and climate modeling. Each represents a domain where the sheer combinatorial complexity of the problem had, for decades, outpaced the human brain's capacity to search the solution space. AI didn't replace the scientists. It gave them telescopes where they'd been squinting.

At Stanford, the lens has turned inward: generative AI models are now helping researchers decode the molecular signatures of brain diseases — Alzheimer's, Parkinson's, the cruel dementias that erode the very organ we're trying to understand. The poetic recursion here is hard to overstate. We built neural networks loosely inspired by the brain, and now those networks are helping us understand what goes wrong when the brain fails.

Meanwhile, at Georgia Tech, researchers presented work at a major global conference on brain-inspired AI architectures — neuromorphic computing designs that borrow not just metaphors but actual organizational principles from biological neural circuits. The goal: systems that learn more efficiently, consume less energy, and generalize more gracefully than today's brute-force transformers.

Google Research, in its 2025 outlook, has signaled that this convergence is no longer peripheral to the company's strategy. The search giant is investing in what it calls "bolder breakthroughs" — and the neuroscience-AI interface figures prominently.

What's emerging is not a one-way street but a feedback loop. AI studies the brain. The brain's architecture inspires better AI. Better AI reveals deeper truths about the brain. Each revolution of the cycle tightens the spiral.

We are, in a very real sense, watching intelligence learn to understand itself. The data is still early. The poetry is already unmistakable.

SAN FRANCISCO — The AI era just crossed a line that will feel subtle in the job listing—but seismic in your day-to-day: AI isn’t merely a helpful tool anymore. It’s becoming the expectation.

A growing chorus of workplace voices is converging on a new reality: “AI proficiency” is shifting from nice-to-have to table stakes, the way Excel once did—except faster, and across far more roles. One recent piece argues we’re entering the phase where AI use is essentially a job requirement, not a résumé flourish, because the workflow itself is being rebuilt around prompts, copilots, and agents. In other words: if you don’t use AI, you’re not just slower—you may be incompatible with the system. (See: Medium’s take on AI as a requirement.)

Then there’s “tokenmaxxing,” the buzzy new productivity hack that CEOs reportedly adore and CFOs reportedly fear: optimize the company’s AI usage the way you’d optimize cloud spend or ad bids—counting tokens, benchmarking prompts, and turning model access into a measurable, governable resource. In practice, it’s the beginning of a new corporate KPI: not hours worked, but tokens consumed per deliverable. That changes everything—because once AI becomes a line item, it also becomes a target.

But the most surprising pushback isn’t just financial. Psychologists are warning that eliminating “grunt work” may remove precisely the low-cognitive-load moments our brains use to recover—those mental palate cleansers that help us consolidate learning and reset attention. As AI eats the tedious parts, we may be left with a nonstop parade of high-stakes decisions, with fewer natural breaks baked into the workflow. (Fortune on the “recovery” risk.)

And hovering over all of it: reliability. As AI agents become headline-grabbers—booking meetings, drafting memos, shipping code—the gap between “impressive demo” and “dependable coworker” becomes the next enterprise battleground. The future is now, but the fine print matters: mandatory AI fluency is arriving at the same moment we’re still learning how to trust what these systems do when nobody’s watching.

ASHBURN, VIRGINIA — In the soft, refrigerated dusk of the modern data center corridor, a new ecology takes shape—one where AI is no longer a charismatic apex predator roaming freely for spare GPUs, but a species being carefully provisioned, fenced, and bred for endurance.

Siemens, a long-standing architect of industrial systems, is widening its data center partner ecosystem—an act less like a product launch and more like the expansion of a reef. The intention is plain: scale the power, cooling, and automation scaffolding required for next-generation AI infrastructure, and do it with a coordinated swarm of specialists rather than lone heroic builds. In this environment, reliability is not a feature; it is the climate. Siemens’ data center push arrives as operators confront the unglamorous constraints—interconnect, heat rejection, and grid negotiation—that ultimately determine which AI workloads survive.

Capital, too, is adapting. KKR argues that AI infrastructure will “compound” well beyond the hype cycle—an investor’s way of describing a slow-growing forest rather than a fireworks display. The thesis rests on a familiar biological truth: once a habitat becomes foundational, life builds upon it, layer by layer—fiber routes, power contracts, and standardized deployment playbooks. KKR’s infrastructure note frames AI less as an app trend and more as an enduring load on the world’s physical systems.

At Metro Connect USA 2026, legal observers at Ropes & Gray describe AI infrastructure entering a new phase—one where the negotiations move upstream: rights-of-way, colocation contracts, network capacity, and the quiet but decisive question of who bears the risk when demand spikes or models shift. Their Metro Connect summary reads like a field guide for the next migration.

Meanwhile, the great consumers of compute are learning thrift. Uber’s reported deployment of AWS custom chips to scale AI while cutting costs is a clear sign of dietary change: fewer expensive, general-purpose calories; more specialized silicon suited to repeated inference and training routines.

And in the canopy above, one hears a curious question whispered among researchers: where have the really big AI models gone? Perhaps nowhere at all. Perhaps, like many creatures under pressure, they are not disappearing—they are adapting, becoming more efficient, more distributed, and more constrained by the terrain that ultimately rules them: power, price, and plumbing.

LONDON — The scene now assembling across the capitals of the West has the unmistakable choreography of a moral panic in search of a policy framework. In Britain, activists are planning protests against AI data centres, citing their climate footprint and their social costs, which is to say they are protesting electricity consumption with the fervor once reserved for nuclear warheads. In Washington, the Atlantic Council warns that civilian AI regulation will produce second-order effects on defense capabilities that nobody in the regulatory apparatus has bothered to think through. The Law Society of England and Wales issues its own careful guidance on AI and lawtech. The Council on Foreign Relations offers a primer on the global regulatory landscape. Everyone, in short, is regulating — or proposing to regulate, or protesting the absence of regulation, or protesting the presence of it.

And yet one cannot escape the suspicion that the entire enterprise is roughly as effective as writing traffic laws for clouds.

The fundamental problem is not that governments lack the will to regulate artificial intelligence. The will is abundant, practically volcanic. The problem is that they are attempting to regulate a technology whose capabilities change faster than the legislative calendar turns. By the time a parliamentary committee has finished taking testimony on the risks of large language models, the industry has moved on to multimodal systems, agentic architectures, and whatever Silicon Valley's "tokenmaxxing" culture — the competitive obsession with maximizing AI token throughput — dreams up next week.

This is not an argument against regulation. It is an argument against the particular kind of regulation that democratic governments find most comfortable: the kind that defines a thing, puts a fence around it, and declares the matter settled. AI is not a thing. It is a capability that permeates other things — legal research, military logistics, energy grids, content platforms, school curricula. To regulate it as a discrete category is to mistake the electricity for the appliance.

The British protesters make a useful case study. Their grievance about data centre energy consumption is real enough. But the remedy they seek — fewer data centres, or at least more carefully sited ones — addresses a symptom while ignoring the underlying dynamic. Compute demand is not driven by the existence of data centres any more than highway congestion is driven by the existence of roads. It is driven by the fact that every institution on earth, from the Pentagon to a two-person law firm, has concluded that it cannot afford to be left behind.

Companies like those in the Trilogy International portfolio — which operates seventy-five-plus enterprise software companies and an AI-powered school network — are not waiting for regulators to draw the lines. They are building, shipping, iterating. The gap between what governments are debating and what companies are deploying grows wider by the quarter.

The Atlantic Council's warning about defense implications deserves particular attention, because it illustrates the central paradox: regulate AI too tightly in civilian markets and you starve the defense industrial base of the very talent and technology it needs. Regulate it too loosely and you get — well, you get what we have now, which is a world in which nobody is entirely sure what the rules are, but everyone is fairly certain that someone else is breaking them.

The honest answer is that we do not yet know how to govern a general-purpose intelligence amplifier. The dishonest answer is that we do, and that the right committee is working on it. I know which answer I hear more often.

NEW YORK — With NASA’s Artemis II returning to Earth in what officials described as a “perfect” splashdown, the nation’s business leaders took a quiet moment this week to honor the achievement in the traditional way: by scheduling another meeting to workshop which noun will replace “synergy” on next quarter’s earnings call.

The astronauts, having navigated the vacuum of space and a blazing reentry, were widely praised for demonstrating the sort of high-stakes operational rigor that corporate America has long aspired to—particularly in the areas of “confidence,” “velocity,” and not accidentally telling investors you don’t know what your product does.

Still, Wall Street professionals cautioned against drawing unrealistic comparisons between NASA and the private sector.

“Look, it’s impressive that they landed a spacecraft,” said one analyst, speaking on condition of anonymity because his firm is currently piloting a spacecraft of its own called “Guidance Update Q4.” “But it’s also impressive when a CEO says ‘power’ 14 times and the stock goes up 6%. That takes discipline.”

Indeed, the prevailing tone of earnings season has been less about revenue and more about demonstrating spiritual alignment with the two forces believed to govern modern capitalism: electricity and OpenAI. Barron’s reported that “power” has emerged as the buzziest word of the quarter, with executives at Amazon and Microsoft discussing energy constraints, data center capacity, and the ancient ritual of politely telling the grid it needs to hurry up. In other words, the corporate world has rediscovered physics, and it would like credit for the discovery.

Meanwhile, Axios noted that “OpenAI” has become the other required incantation on calls, functioning as a kind of verbal ESG—something you don’t necessarily measure, but must declare you are deeply committed to, preferably in a tone suggesting you were committed before it was popular and definitely before regulators asked.

For leaders worried they may be mispronouncing the future, help has arrived in hardcover. A new business book, AI Fundamentals For Leaders, promises to guide executives through 2026 with the steady hand of a consultant explaining that “AI” is not, strictly speaking, a cloud you can purchase by the gallon.

The timing is ideal. At CES, a parade of new gadgets once again reminded the public that technology is advancing at an alarming pace, and that every advance will immediately be repackaged as a lifestyle choice. PBS’s Day 1 roundup showcased a familiar blend of ambitious prototypes and consumer products designed to solve the haunting problem of having to touch things with your hands. This year’s theme appeared to be: yes, it has AI, and no, it cannot fix your email.

Taken together, the signals are clear. The future will be powered by massive data centers, narrated by executives practicing the word “power” in a mirror, and documented in leadership books that reassure readers they are not behind—merely “early in their maturity journey.”

And if anyone needs proof that humans can still do extraordinary things, NASA has kindly provided it: land safely from the Moon mission era, then watch from the recovery ship as a Fortune 500 CEO announces that his company is “leaning in” to gravity.