The New York Times: 21 Ways People Are Using A.I. at Work
The New York Times: 21 Ways People Are Using A.I. at Work
According to The New York Times, experimentation after ChatGPT’s debut has matured into everyday practice across offices, labs, clinics, studios and government. The publication reports that recent surveys indicate roughly one in five U.S. workers now use A.I. at least semi-regularly, with measurable time savings alongside persistent errors that require human skepticism. Drawing on its own reporting, The New York Times describes how hospitality, science, education, health care, law, government and the arts are folding new models into routine work.
In hospitality, the report states that Cleveland restaurateur Sam McNulty feeds distributor portfolios into ChatGPT under constraints (price bands, regions, grape varieties) and receives viable lists such as Herdade do Esporão Monte Velho Branco from Portugal (Antão Vaz, Roupeiro and Perrum; wholesale estimate $7–$9 per 750 ml), trimming hours of meetings even if tastings remain irreplaceable. In science, The New York Times details Missouri Botanical Garden’s effort, led by curator Jordan Teisher, to identify herbarium specimens—among eight million dried plants—via spectral “reflectance” signatures so that confident matches are auto-labeled while edge cases are routed to taxonomists. The publication notes that cheaper GPUs and adequate funding enable processing hundreds of thousands of samples and sharing compatible data for biodiversity and climate research.
Designers are streamlining production photography: the report cites Dan Frazier’s use of Adobe Photoshop Generative Fill to remove glare, extend clothing in portraits and tidy product shots in seconds; for more ambitious concepts, he still blends A.I. drafts with traditional techniques. In classrooms, The New York Times states E.S.L. teacher Manuel Soto halves prep time by generating five-day lesson plans aligned to Puerto Rico Core standards (objectives, differentiation, closings and exit tickets), while also planning to teach responsible A.I. use. Academia’s citation grind is eased too: the publication reports that French literature professor Karen de Bruin now offloads MLA/APA/Chicago formatting to A.I., challenging guesses (like paywalled “Doe, Jane” placeholders) and keeping a skeptic’s eye.
Clinical workflows feature prominently. According to the report, psychotherapist Alissa Swank converts free-form notes into SOAP summaries (Subjective, Objective, Assessment, Plan), reclaiming hours weekly. Primary-care physician Matteo Valenti uses Abridge inside the EHR to capture conversations and produce organized notes, saving about an hour a day and preserving incidental details; he also worries about impacts on human scribes but sees relief for paperwork-burdened clinicians. Medical imaging scientist Michael Boss, The New York Times adds, accelerates literature triage with tools like ChatGPT, Perplexity and Undermind—using summaries to seed deeper reading while guarding against misattribution.
Infrastructure and code are getting help too. The publication reports that Tim J. Sutherns’s Digital Water Solutions installs acoustic sensors in hydrants and relies on autonomous machine learning tuned on the fly to each network’s pipes, sizes and pressures, surfacing possible leaks within weeks at small-utility price points thanks to cheaper compute and storage. In software development, DraftPilot co-founder and CTO Chris O’Sullivan uses Anthropic’s Claude Code to generate working code from high-level tasks, a pattern mirrored across engineering teams.
Creative workers are using models as collaborators rather than replacements. According to The New York Times, visual artist Marya Triandafellos conditions a model on her own portfolio, inspects grids of abstract images like inkblots, groups themes and refines them into final works while also requesting critique and titles—yet avoids A.I. for the last mile. Fiber artist Nicole Goldman taps Claude for material choices such as stabilizers, glues, and needle-thread combinations, finding organized, succinct guidance faster than manual searching. Music educator Deb Schaaf, the publication notes, discovered that asking for “more Gen X” yielded firm, empathetic audition feedback without saccharine tone.
Public-sector teams are applying A.I. to reduce friction rather than replace staff. The New York Times reports that Chris Handley at the Harris County District Attorney’s office built a custom L.L.M. to flag filing pitfalls—typos, missing arrest details, slightly incorrect charges, or full names where initials belong—after police reports arrive, while discarding an overconfident legal-corpus model that hallucinated case facts. California’s Department of Tax and Fee Administration, led at the call center by Thor Dunn, is testing a Claude-based assistant that reads live transcripts, links authoritative pages across 16,000+ entries, and has already trimmed call times by 1.5 percent in early use as agents learn the system.
Civic and cultural work round out the picture. According to the publication, animal-welfare consultant Kristen Hassen uses A.I. to brainstorm adoption campaigns—such as “Lifetime of Love” with then-and-now photo pairings—for senior pets. Baroque specialist Richard Stone uses A.I. as a tutor to cross-check translations of 17th- and 18th-century lyrics, ultimately trusting informed intuition on ambiguous forms (e.g., resolving “pramo” to “bramo”). Lawyer Deyana Alaguli runs plain-English checks on legalese with Google Gemini to surface how a layperson might interpret a paragraph, while also using models to stress-test arguments. And in schools, English teacher Matthew Moore uses A.I. to generate classroom materials and, when needed, consults multiple detectors (such as GPTZero and QuillBot) while anticipating a near-term future where typed essays may need to give way to supervised writing.
Finally, research into minds and machines is converging. The New York Times reports that neuroscientist Adam Morgan examines how large language models encode syntax and meaning as a proxy for hard-to-measure human processes during neurosurgical experiments, using internal model layers to generate hypotheses for limited operating-room time. Across these cases, the publication emphasizes a consistent refrain from practitioners: A.I. accelerates grunt work, sparks ideas, and widens access to expertise—yet still hallucinates, still misses nuance, and still demands a reserve of skepticism.
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