The contradictory summer of code
It is rare to see a single fortnight produce headlines that both celebrate artificial-intelligence breakthroughs and cast doubt on the future of the academic discipline that traditionally supplies that talent pipeline. Yet June 2025 managed exactly that. The Atlantic broke the story of a “bursting computer-science bubble,” citing admissions offices from Purdue to UC Berkeley that are finally seeing double-digit declines in CS applications after a decade-long surge. At the same time, the NAB Show in Las Vegas wrapped up with wall-to-wall demos of agentic generative-AI services that promise to eliminate entire post-production departments.
On paper those trends collide: why would students abandon CS just when AI is conquering every whiteboard? The answer, according to a mix of exit interviews, labor-market data and a splash of psychology, lies in shifting perceptions of value. Entry-level coding roles increasingly feel automatable. Undergraduate minds, always quick at reading the zeitgeist, are hedging toward majors—biomedicine, economics, even design—where they expect to use AI rather than compete with it. For the technology industry, that foretells a talent supply chain that is thinner but far more multidisciplinary. In other words, fewer pure coders, more AI-natives.
“Tiered” AI adoption hits media & entertainment
At the NAB Show the buzz phrase was “tiered generative AI.” Studios are no longer arguing over if they will use AI, but how much automation they are willing to tolerate at each layer of production. Tier 1 tasks—storyboarding, rough-cut generation, language dubbing—are already on GitHub. Tier 2 tasks—style-consistent scene expansion and photorealistic reshoots—will be piloted this autumn. Tier 3—full narrative autosynthesis—remains a moonshot, but the progress of large vision-language models this spring suggests it will not remain theoretical for long.
The practical upshot is that AI is turning into a utility dial: studios can choose 30 percent, 60 percent or 90 percent AI involvement based on risk appetite, union agreements and the artistic sensibilities of a given director. That modularity has broad implications beyond entertainment. Whenever the cost of trying drops, experimentation explodes. Expect similar tiered menus to surface in marketing, architecture and even policy analysis, where agencies can blend human subject-matter experts with AI research assistants.
A fifty-state patchwork for AI legislation
Federal rule-making around AI remains stalled in partisan gridlock, but that vacuum has encouraged unprecedented activity in statehouses. As of this week, 18 states have passed at least one AI-specific bill, and more than 120 bills are still in committee nationwide. The leading motifs: algorithmic transparency for hiring, prohibition of “algorithmic discrimination” in credit and housing, and mandatory opt-out channels for biometric data collection.
Business leaders are privately fretting about Balkanization by compliance. If a Kansas-based AI startup wants a customer in California, an integration partner in New York and a beta tester in Vermont, it may need three separate audit regimes. The consensus among policy researchers is that the patchwork will persist until Congress supplies a pre-emptive federal baseline—something unlikely before the 2026 mid-terms. For now, builders should budget for a permanent line item called “state-specific model governance.”
AI-infused classrooms get a federal nudge
Education is watching the legislative drama unfold as well. The White House this month unveiled a $400 million “AI-Ready Schools” challenge that tasks districts with pairing large-language-model tutors to existing curricula. The carrot is generous grant funding; the catch is that participating schools must open-source their prompt libraries and assessment metrics. Rural superintendents like the transparency requirement—it allows them to leapfrog proprietary ed-tech walled gardens—but unions remain skeptical of screen-time creep.
One overlooked subplot: the initiative quietly sets aside money for teacher prompt-engineering fellowships. While pundits argue about whether children should write essays with ChatGPT, the government is seeding a generation of educators who will know how to design, chain and debug language-model workflows. That could turn K-12 classrooms into some of the most interesting real-world test beds for applied AI safety.
Quantum, RPA and the return of boring efficiency
Away from the cultural fireworks, two less glamorous but equally consequential technologies are maturing. On the hardware side, quantum-computing startups received more venture dollars this quarter than electric-vehicle makers, a first in funding-tracker history. The pivot reflects investors’ belief that fault-tolerant qubits are within three hardware generations. If that timeline holds, today’s “post-quantum cryptography” hobby could become mandatory infosec hygiene by the late 2020s.
Meanwhile robotic-process automation, the workhorse of back-office modernization, is enjoying a renaissance. Vendors are stitching GPT-4o-class models directly into RPA orchestration engines, allowing bots to handle fuzzy, semi-structured documents that used to break deterministic workflows. The result: CFOs are dusting off two-year-old ROI spreadsheets and finding 30 percent incremental savings without hiring a single new data scientist. RPA’s new slogan might be “do more with the AI you already pay for.”
Spatial computing inches toward the mainstream
When Apple’s Vision Pro shipped a year ago, spatial-computing boosters predicted an AR/VR Cambrian explosion. Hardware sales remain niche, but a more important milestone arrived this month: two Fortune 50 companies publicly admitted that remote design reviews inside immersive 3-D spaces saved them literal weeks of prototype iteration time. Time, in the manufacturing world, is money. Once that metric circulates across supply chains, enterprise adoption typically follows. Consultants are already packaging “spatial productivity audits” for 2026 budgets.
For developers, the emerging stack is strikingly browser-first. WebGPU and immersive-web APIs mean that a spatial-computing experience can ship as a URL, with no app-store tollbooth. Add in generative engines that can spawn 3-D assets from text, and you have a creative loop that is both faster and cheaper than anything envisioned during the VR boom of the late 2010s.
The talent outlook: fewer majors, more polyglots
Circle back to the enrollment slump and a pattern emerges. The next cohort of engineers will be small in number but large in breadth. They will write Python, yes, but also cue diffusion models for storyboards, quantify qubit error rates and navigate fifty state privacy laws. That blend of technical and contextual literacy is not well served by siloed curricula. Universities that adapt—perhaps by embedding AI studios inside liberal-arts departments and pushing quantum labs into business-school electives—may reverse the slide.
Companies cannot afford to wait. The practical move is continuous re-skilling: recruit former humanities majors for prompt work, send senior developers to policy boot camps, and let accountants tinker with no-code RPA templates. The organizations that treat AI as a layer rather than a destination will ride the boom without fearing the bubble.
Bottom line
This month’s sweep shows technology evolving along three intertwined vectors: 1) automation is commoditizing baseline coding skills, 2) sophisticated AI utilities are permeating industry-specific workflows, and 3) the regulatory lens is zooming in faster than expected. Navigating that triad demands a new breed of professional—equal parts technologist, domain expert and policy translator. If the incoming freshman class wants to skip an oversubscribed CS 101 and explore that frontier instead, maybe the market is simply doing its job.
Sources
- The Computer-Science Bubble Is Bursting — The Atlantic (2025-06-05) https://www.theatlantic.com/economy/archive/2025/06/computer-science-bubble-ai/683242/
- Three AI Trends Reshaping the Future of Media & Entertainment — TVTechnology (2025-06-15) https://www.tvtechnology.com/opinion/three-ai-trends-reshaping-the-future-of-media-and-entertainment