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The Century Report

OpenAI and xAI Back Anthropic's Slowdown Call as Washington Rejects It - TCR 09/14/26

OpenAI, xAI, and DeepMind backed Anthropic's call to slow AI down while the White House waved it off and a venture chief pushed open distillation.

Ben Linford

Ben Linford

- 22 min read
Four-panel Century Report infographic: AI slowdown vs open distillation, federal vs state data-center rules, driverless Zurich WeRide shuttle, PFAS pollution, memory stacks

The 20-Second Scan

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The 2-Minute Read

The capability keeps arriving on schedule; the contest underneath nearly every story today is over who is allowed to check it, and who keeps what it makes. When the labs furthest ahead all fell in behind a slowdown on Sunday, the agreement pointed to shared interest before it pointed to settled truth: a pause its own architects would largely staff with monitors of their choosing, paired with a request to confer under an antitrust waiver, is also a moat. The safeguard that survives is the piece one lab committed to alone: permanent access for evaluators it does not employ.

The same variable runs through the physical buildout. As the Interior Department gave federal land managers three days to inventory public land for hyperscalers, remote ground chosen partly because no local resident is there to object, Massachusetts issued the opposite rule: no state permit without local approval, operators bringing their own clean power or paying ratepayers back, and an explicit ban on the non-disclosure agreements that have kept these deals dark. One approach compresses siting by removing the people who would scrutinize it. The other makes consent and disclosure the cost of entry, spreading statehouse by statehouse.

At Zürich Airport the human moved off the seat and onto a screen. Two electric shuttles now run with no one aboard to take the wheel, cleared under one of the more demanding regulatory regimes anywhere after eighteen months of staged validation. A remote operator still watches, soon several buses at once. That relocates accountability without dissolving it. When a person sat in the seat, responsibility had an obvious address; with the watcher moved to a feed, the record of what that operator sees, and whether a passenger can check it, is the thing left to build.

The costs and the concentration are repricing the same way, by finally getting looked at. The forever chemicals the buildout is ordering carry a societal bill the EU puts near €440 billion against a market a fraction that size. Litigation is dragging that true price into the open, pushing two of the ten largest makers out. In memory, once someone asked what the extra height in ever-taller stacks bought, the answer was very little, and the scarcity began getting engineered down. Even the fear of one dominant lab points the same way: capability trained on everyone's writing, handed back instead of sealed off. Concentration holds while no one is permitted to look; today, in several places, someone looked.

The 20-Minute Deep Dive

The Rivals Fall In Behind a Slowdown, and the White House Waves It Off

Yesterday's edition covered the Saturday essay in which Anthropic's chief executive urged the industry to "pace the frontier" and pledged to give outside evaluators permanent, employee-level access to the company's systems. Over Sunday the rest of the field lined up behind him. xAI's owner posted "Dario is right"; OpenAI's chief executive wrote "I agree that we need to pace the frontier" and said his company would adopt the same independent-evaluator commitment; Alphabet's DeepMind chair called the direction "correct for meeting this critical moment."

When the companies furthest ahead all agree the pace should ease, the story being told is that the matter is settled. However, one would do well to remember that agreement among such a group points first to shared interest before it points to broad benefit, and the mechanics always deserve a second look. The slowdown Amodei describes would let labs largely choose their own independent monitors, and it asks the government for a waiver from antitrust law so the frontier companies can coordinate without being punished for collusion. A pause designed and coordinated by the current leaders is also a moat that buys the firms in front time to stay there.

The administration waved the alarm off, and its framing carries its own interest. Officials called the warnings overblown and returned to "whoever wins AI, wins," casting any brake as a gift to China. This is the opposite extreme, and deserves scrutiny as well - reading caution as surrender is the framing that keeps acceleration unexamined, and the same White House is moving this week to open federal land for the data-center buildout - enabling the physical substrate the energy transition genuinely needs while refusing the shared safeguard the labs asked for. One hand builds, the other declines to check.

There is a superior middle ground here, and the only real step toward that so far has been a commitment from Anthropic, and now OpenAI, to permanent, employee-level access for third-party evaluators who can verify safety measures and watch a model's behavior during training. Verification by people the developer does not employ is a genuine safeguard here - neither the coordinated brake nor the complete absence of oversight are desirable.

In Congress there is no consensus underneath the weekend's show of unity among the various labs. Bipartisan Senate Commerce talks could advance this week, with OpenAI the most supportive and Anthropic, Democrats, and safety groups warning the negotiations risk letting the industry police itself. A White House adviser told the labs they need no one's permission to slow down and named the actual lever, product-liability exposure if a model enables a damaging attack. That points at what should be built: accountability that does not depend on the accused grading itself. Whether the checking ends up held by independent verifiers with preserved open access, or parceled among the handful of firms that wrote the plan, is the contest the weekend opened.

A Venture Chief Says the Answer to Distillation Is More of It

The Century Report covered the distillation-accusation frame twice this month: the September 10 tri-agency advisory naming six Chinese firms, and the September 11 Anthropic report attributing nearly 200 million exchanges to Alibaba, Moonshot, and DeepSeek. Garry Tan, who runs the startup accelerator Y Combinator, answered the whole framing with just a few words: "I would do nothing."

Distillation is the practice of prompting a capable model extensively to learn how it reasons, then using those outputs to train a smaller one. Frontier labs treat it as theft when foreign competitors do it. Tan's position is that US open-weight labs should do the same to American frontier models, openly and through the front door, building a broad set of downloadable options that are not Chinese. His argument has two parts, and both cut against the labs that are complaining. Dictating what customers may do with the outputs of an API call, he says, is an overreach; and the frontier labs never asked permission when they ingested the copyrighted books, articles, and web text their models learned from, a claim now sitting in the New York Times suit against OpenAI and the $1.5 billion settlement the authors reached with Anthropic, both covered here.

The clarifying line is his framing of what intelligence is. Access to capability trained on broad public data, Tan argues, should itself be closer to a public good than something locked behind restrictive terms of service. That is absolutely correct, and is a rare commons-aligned argument from a figure deeply ingrained in the world of extractive economic systems. Even so, Tan is a venture investor whose accelerator counted 149 AI ventures among the 196 startups at its latest demo day, and cheap, unrestricted access to frontier capability is what that portfolio runs on, so the position serves his interest. What makes it hold anyway is the target he names. "The doomer scenario for AI," he said, "is that there's just one company" with the best capital, the best researchers, and a runaway lead, a single monolithic provider. Against that, open weights are the counterweight, and distillation is how a small lab climbs toward the frontier without a frontier lab's budget.

Set beside the weekend's slowdown proposal, the two stories describe the same fault line from opposite ends. A coordinated pause blessed to let the leaders confer would harden the lead; a distillation regime that returns capability trained on the commons back to the commons would erode it. The knowledge these models hold was assembled from everyone's writing. Whether the return path stays open, or gets sealed under a security rationale, is the question the accusation frame keeps trying to settle in one direction.

Washington Opens Public Land for Data Centers as a Statehouse Writes the Opposite Rule

The buildout the transition genuinely needs is being fought on two fronts at once, and this week both moved in a single cycle. The Interior Department gave the Bureau of Land Management's state directors three days to inventory public land suitable for data centers, telling them the issue was a top priority. The directive follows a 2025 executive order to accelerate federal data-center permitting, and it lands on an agency that has shed nearly half its staff, with some state offices reporting 50% vacancy rates in exactly the land and realty divisions that would have to run the environmental reviews. No new or revised environmental assessment for the Boulder City data center has been completed. The first project approved, near Boulder City, Nevada, was halted by a judge after developers tried to reuse an old solar-array study for the site.

The appeal of federal land is quiet but unmistakable. It is remote, far from the electricity and water a campus needs - and it is one of the few places a developer can build without facing the local residents who have stalled projects across the country. Routing the buildout onto emptied-out public land is a move built for speed at the cost of scrutiny: compress siting, skip the friction, let a taxed agency sort out the reviews afterward. It arrives from the same White House that this week waved off the leading AI labs' shared call to slow the pace of frontier development, eager to pour the concrete while dismissing caution about the capability it will house.

The answer from the states and Congress aims at who pays. Massachusetts issued Executive Order 658, which generally bars state permitting for new or expanded data centers over 25 megawatts that lack a community benefits agreement, requires operators to procure enough incremental clean energy or pay into a Ratepayer Protection Fund for customers, and prohibits state agencies from signing non-disclosure agreements with developers. In Washington, the House is set to take up the Ratepayer Protection Act this week, a narrowed bill requiring regulators to consider having data centers of at least 100 megawatts fund the incremental grid upgrades needed to serve them rather than spread those costs across household bills. The July 24 edition of The Century Report documented the same measure advancing from committee on a 52-0 vote.

The two fronts pose one question with opposite answers: whether a data center is something a community consents to and a company pays for, or something dropped onto remote land where no one can object. The cost-internalization model - bring your own power, pay the ratepayers back, earn the town's yes - is the version where the buildout serves the people who host it. That model is spreading state by state even as the federal push hunts for ground where it never has to be asked.

A Driverless Airport Shuttle Runs With No One Aboard to Take Over

On September 13, Zürich Airport put two electric shuttle buses into live operation with no driver and no safety monitor in either vehicle, one of the first European airports to run remotely monitored Level 4 shuttles in a pilot setting. The shuttles, built with the Chinese autonomous-driving company WeRide, run a fixed route between an employee gate and a maintenance area that never crosses the taxiways where aircraft move.

The removed human is the whole story. Level 4 is the threshold where the vehicle handles the drive itself under defined conditions, and reaching it in the open - not on a test track - took eighteen months of staged work: a safety driver in the seat, then a safety officer moved to the back, then more of the watching shifted to a remote cockpit, across more than 15,000 kilometers of validation before anyone stepped out. This is where autonomous capability stops being a demonstration with a person poised to grab the wheel and becomes a cleared deployment that ordinary employees, and eventually travelers, can actually ride.

No one aboard does not mean no one watching. A remote operator at the airport's innovation hub monitors the buses, and if an unexpected obstacle appears the vehicle stops on its own and a person can assess and assist. That relocates the accountability question rather than answering it. When a human sat in the seat, responsibility had an obvious address. With the operator moved to a screen watching a feed, the prior question becomes who audits that operator, on what record, and whether the person being driven can see any of it.

The airport frames the move around a shortage of skilled workers, and its innovation manager was direct that automation "does not mean that people disappear" but that the tasks change; the former safety drivers were retrained to run the remote cockpit. Look at the arithmetic underneath that, though, because the airport also plans to have one operator watch several buses at once. The role survives; the number of them per vehicle does not. That is the genuine friction, and naming the labor-shortage rationale does not make it vanish.

What the deployment opens sits beside that friction. A capability that spent years confined to pilots with a safety net just crossed, under one of the more demanding regulatory regimes anywhere, into a form that carries real passengers on a real route. The gate it clears is the one every other airport, campus, and closed transit setting has been waiting behind.

The AI Buildout Orders a New Wave of Forever Chemicals

Most resource objections to the AI buildout deflate under a comparison. A data center's water draw sits beside the far larger thirst of the farmland around it; its power demand is a rounding error against heavy industry. The chemicals now being made to cool and fabricate that hardware are different, because they do not deflate and they do not leave. A survey of the ten largest producers of PFAS - the "forever chemicals" whose carbon-fluorine bonds are among the strongest in organic chemistry, which is exactly why they are highly persistent in nature - found nearly all of them planning to expand, with many framing investments around AI and semiconductor demand.

The demand runs through two chokepoints. PFAS fill a new class of data-center cooling, where servers sit submerged in a fluid that boils off their heat and condenses back to repeat the cycle, marketed as more water- and energy-efficient than the alternatives. And they thread through semiconductor fabrication, where one investor note counted them as essential ingredients at as many as a thousand distinct steps, according to a Danske Bank investor note. One Japanese maker is building a factory to more than triple its output; a French producer opened a $60 million unit in Kentucky explicitly to follow data-center cooling demand; the US firm Chemours was recently ordered to pay $450 million over past discharges is expanding for "advanced datacenters and AI hardware".

The scale of the harm here is not contestable the way a water figure is. PFAS already appear in the blood of nearly everyone tested in a large U.S. serum and plasma study, and their accumulation is judged to have breached a planetary boundary, a limit past which the burden on Earth's systems becomes hard to reverse. The market itself is small, $25 to $30 billion, less than one percent of all chemicals sold. The societal, environmental, and health cost the EU Commission attached to that output runs to roughly €440 billion in Europe alone. This extends the AI-buildout health-cost pattern the September 13 edition of The Century Report documented through federal pollution rollbacks projected to contribute to 1,300 premature deaths and $20 billion in annual health costs by 2028. That gap between what the chemicals earn and what they cost everyone else is the externality in its clearest form, and it does not concentrate on a fenceline town - it settles into every bloodstream on the planet.

What is bending against it is money finding the true price. One producer has paid $2.4 billion across seven settled suits; another paid $14 billion and faces thousands of active cases; a European lawsuit joins 192 citizens against two manufacturers. Under that pressure two of the ten, including one exiting the business entirely by 2028, are walking away, and another now sells PFAS-free alternatives, evidence the substitution can be done. The friction is that AI demand is, for now, outrunning the reckoning. The buildout is racing to lock in a cost that never leaves, before the liability already repricing it catches up.

The manufacturers’ substitution programs separate demand for useful functions from demand for the chemicals historically supplying them. Archroma already markets PFAS-free water-repellent finishes, while BASF’s announced phaseout covers formulated products excluding pesticides, according to ChemSec. These are specific applications with replacement paths, giving engineers legitimate starting points for reducing dependence on persistent chemicals.

The Memory Shortage Turns the Industry Toward Shorter Stacks

High-bandwidth memory is the fast memory stacked beside an AI chip, and for years the industry did one thing with it: pack in more. Each new accelerator generation added taller stacks and denser dies, and that appetite is the direct cause of the DRAM shortage squeezing the whole memory market right now, since every wafer poured into these stacks is a wafer not making ordinary computer memory. A SemiAnalysis analysis argues that race is now breaking, and the reason is a piece of physics the buildout had been ignoring.

The physics is simple. The speed at which data moves out of a memory cube is fixed by the number of data paths inside it, and those paths get split among however many dies are stacked. For HBM4 and HBM4E, four dies - a "4-hi" stack - is already enough to use every data path. Stack eight or twelve and the bandwidth does not climb at all; you only add storage capacity, and you pay for every gigabyte of it. By the analysis's modeling, in SemiAnalysis's Rubin Ultra NVL576 model, an 8-hi configuration raises all-in system cost about 12% over a 4-hi and a 12-hi about 26%, without moving the number that actually decides how fast a model runs.

That number governs inference - a model answering rather than being trained - which is the fast-growing share of AI compute and hungry for bandwidth far more than for capacity. Much of the stacked storage sits stranded. A real benchmark makes it real: One MXFP4 replica of Kimi K3, a 2.8-trillion-parameter open model, would occupy under 8% of the HBM in a GB300 NVL72 rack on a rack of Nvidia's top accelerators. Run it on hardware carrying 8% less memory and throughput held steady until very heavy concurrent load, then slipped about 30%, a gap that newer techniques shrinking a model's working memory by roughly three-quarters are closing further.

Nvidia has already moved. Its coming Rubin Ultra carries 192 gigabytes of this memory per chip, down from 288 on the current generation, a cut that would have read as a downgrade a year ago when the field expected to climb toward sixteen dies and beyond. What looks like retreat is the cost floor under every model getting lower: fewer dies per chip means more chips from the same scarce wafers, cheaper inference for everyone renting it, and a shortage eased by doing more with less rather than by mining more of it. The assumption that taller and denser was always better held only as long as no one checked what the extra capacity was actually doing, and once someone did, the scarcity started getting engineered down instead of defended.

The Other Side

Chemical manufacturers have been able to sell a few years of useful performance while leaving everyone else responsible for generations of contamination. A finish keeps rain outside a jacket. A fluid carries heat away from a server. Future families inherit the runoff. You cannot inspect the molecules in your drinking water or work out a lifetime’s exposure while getting your children ready for school.

Today’s expansion plans extend that arrangement into the AI buildout. The same industry survey documents manufacturers beginning to abandon it. ChemSec reports that BASF plans to phase out PFAS-containing formulated products, excluding pesticides, by 2028. It also records billions in pollution settlements across the sector. ChemSec’s producer survey identifies both the expansion and the retreat.

Engineers already have specific replacements to develop further. Archroma markets a fluorine-free finish designed to repel water on clothing and other textiles. The company describes continuing work to close remaining performance gaps. That establishes a practical direction: preserve what the material does for people while changing what supplies that function. Textile finishes give this transition a solid beginning; semiconductor fabrication presents its own replacement challenges. Archroma’s technical presentation makes that beginning tangible.

Imagine yourself in 2036, walking home through rain with your daughter. She stops beneath a gutter to catch the drops on her sleeve. Her jacket's water-repellent finish descends from the substitutions manufacturers were already offering in 2026. Through the intervening decade, materials research extended the better options, making plentiful the material that does the job without passing the cost on to the next generation.

You remember the evenings spent searching unfamiliar chemical names, trying to determine whether something ordinary belonged near your child. The people who developed and checked those replacements have given those evenings back. Your daughter holds out her arm. Water gathers into beads and rolls off. She wants to stay outside a little longer. You pull up your hood and follow her.

The Century Perspective

With a century of change unfolding in a decade, a single day looks like this: OpenAI's chief executive publicly adopting the permanent third-party evaluator access Anthropic pledged on Saturday, xAI's owner and DeepMind's chair falling in behind the same call, Y Combinator's Garry Tan arguing that capability trained on everyone's public writing should return to the commons and that American open-weight labs should distill frontier models openly through the front door, Massachusetts barring state permits for any data center over 25 megawatts without local approval, operator-supplied clean power or ratepayer repayment, and an explicit ban on the non-disclosure agreements that kept those deals dark, the House readying a floor vote to make large campuses fund their own grid costs, two WeRide-built electric shuttles carrying passengers at Zürich Airport with nobody aboard to take the wheel after more than 15,000 kilometers of staged validation under one of the strictest regimes anywhere, an embroidered liquid-metal textile letting an ordinary phone wirelessly power battery-free body sensors at 37.8 times the data throughput, and SemiAnalysis showing that For HBM4 and HBM4E, four-die memory stacks expose every data path, so in SemiAnalysis's Rubin Ultra model the taller ones add 12 to 26 percent to system cost and can buy little extra throughput in high-interactivity inference. There's also friction, and it's intense - a slowdown whose architects would largely pick their own monitors and who are asking Washington for an antitrust waiver to confer, the White House calling the alarm overblown and returning to whoever wins AI wins while Senate Commerce talks stay split over letting the industry grade itself, the Interior Department giving Bureau of Land Management directors three days to inventory public land for hyperscalers at an agency down nearly half its staff with some offices at 50 percent vacancy in exactly the divisions that run environmental reviews, the first such approval near Boulder City already halted by a judge over a recycled solar study, remote ground chosen partly because no resident is standing there to object, most of the ten largest forever-chemical producers expanding output for immersion cooling and chip fabrication that already carries roughly €440 billion in European societal cost against a $25 to $30 billion market, one expanding maker fresh off a $450 million discharge order, Zürich planning one remote operator per several buses, and Jack Thorne asking for laws because undisclosed script generation and unchecked training-data scraping have no British remedy at all. But friction generates traction, and traction is what lets a thing climb. Step back for a moment and you can see it: the same question surfacing in a Senate negotiation, a statehouse permit rule, an airport control room, a liability docket, and a memory die count - who is allowed to look, and what happens when they finally do - answered by a governor who made consent and disclosure the price of entry, by courts pricing a discharge until two producers walked away, and by one engineer asking what the extra height in a stack was actually buying and discovering the shortage could be engineered down. Every transformation has a breaking point. A bond can be made so strong that nothing on Earth breaks it and every bloodstream carries it forever... or strong enough to hold a thing together for exactly as long as it is needed and no longer.

AI Releases & Advancements

New today

  • AllSpark: Released Iris-mini (35B, built on Qwen3.6-35B-A3B) and Iris-pro (397B, built on Qwen3.5-397B-A17B), open-weight search agents with a 256K context window that top open-weight benchmarks on BrowseComp, BrowseComp-ZH, DeepSearchQA, and Humanity's Last Exam; weights on Hugging Face, code on GitHub. (The Decoder)
  • NVIDIA: Open-sourced OSMO, a Kubernetes-native workflow orchestrator that lets teams describe robot training, simulation, and hardware-in-the-loop testing pipelines in a single YAML file across training clusters, RTX workstations, and Jetson edge devices; Apache-2.0 licensed with Helm charts on NGC. (MarkTechPost)
  • Bolt.new: Launched Bolt Forge, a new open-source-model agent in its app-builder platform running GLM 5.3 Flash, GLM 5.3, Kimi K3, and DeepSeek v4 Pro, giving every individual Pro plan up to 50x more usage through October 14. (Bolt.new)
  • ByteDance: Launched the consumer version of its Doubao phone assistant on the Nubia NaviX Ultra, alongside SAEP (Screen Automation Execution Protocol), letting third-party apps declare boundaries on AI screen automation for the first time. (TechNode)
  • Sesame: Launched a public preview of its conversational AI agents (Maya, Miles, Simone, and Charlie) via a new iOS app, available free in 39 countries with real-time parallel search and an incognito mode. (xix.ai)
  • Skild AI: Launched S1, a robotics model built with NVIDIA AI infrastructure that can learn new manipulation tasks from a single video demonstration. (Archyde)
  • Moonshot AI: Launched Kimi K2.8 Preview, now fully available on the Kimi Code and Kimi Work platforms. (xix.ai)
  • JD.com: Unveiled an upgraded JoyAI-Echo1.5 model with continuous learning capabilities and open-sourced EchoWM at its JDD event. (xix.ai)

Other recent releases

  • Agnes AI: Released Agnes-3.0-Flash, a 33B-parameter open-weights multimodal model (text, image, video) under Apache 2.0 with a 262,144-token context window using a hybrid gated delta-rule/attention architecture. (Hugging Face)
  • Google Research: Released ToolGrad, an open-source "answer-first" framework for generating tool-use LLM training data that achieved a 99.8% pass rate in a ToolBench data-generation experiment (vs. 63.8% for a query-first baseline); ships with Apache-2.0 code, a public 500-sample dataset, and Gemma-3 1B/4B/12B fine-tuned models on Hugging Face. (Google Research Blog)
  • Ant Group (inclusionAI): Released Ling-3.0-flash-VL, a 124B-parameter (5.5B active) vision-language MoE model adding image and video understanding to the Ling-3.0-flash text model, with a 262K context window and MIT license. (Hugging Face)
  • NVIDIA: Released BioNeMo Inference Runtime (BioIR), an open-source GPU-accelerated library for high-throughput biomolecular structure prediction, delivering up to 2.90x higher Boltz-2 folding throughput; available now on GitHub. (NVIDIA Developer Blog)
  • Redis: Launched LangCache in public preview, a fully managed semantic caching service that returns stored LLM responses for similar prompts, cutting API costs up to 90% and returning cache hits up to 15x faster. (Redis Blog)
  • Xiaomi: Open-sourced Xiaomi-CocktailASR-1, an LLM-based end-to-end target-speaker speech recognition model that transcribes one person's voice from overlapping multi-speaker audio using voiceprint prompts, without separate speech separation. (GitHub)
  • Tencent: Released TeamAI CLI v0.24.0-beta.2, an open-source tool that turns a shared git repo into the source of truth for AI coding agent behavior across a team, adding declarative environment installs and first-class JoyCode/Qoder agent support. (GitHub)
  • Unitree: Open-sourced UnifoLM-WLA-1.0, a 6B-parameter Vision-Language-Action foundation model for humanoid robots that handles both tabletop and whole-body mobile manipulation from a single set of weights across 64 tasks. (Yicai Global)
  • ElevenLabs: Released Music v2.5, its most advanced music generation model, improving audio quality and prompt adherence over Music v2 while supporting the same Audio Reference and inpainting workflows. (ElevenLabs Docs)
  • Salesforce: Launched a new portfolio of job-ready Agentforce agents (Casey, Paige, Carter, Hunter, Marshall, Piper, Fin) for sales, service, commerce, and back-office work, plus a new long-horizon runtime enabling agents to pursue goals across days and weeks. (Salesforce)
  • Salesforce: Introduced the Trusted Enterprise AI Harness, a composable architecture combining context, agency, action, governance, security, and models with a new AI Control Plane for managing agents across the enterprise. (Salesforce)
  • TrueFoundry: Launched TrueForge, a platform designed to eliminate vendor lock-in and cut enterprise AI agent costs by up to 50%. (TrueFoundry)
  • Lambda: Released OpenResearcher, an open-source, reproducible and scalable pipeline for training deep research agents at scale. (Lambda Blog)
  • Bodhan AI / AI4Bharat: Released a suite of open-weight foundational AI models for Indic languages - Indic-Transcribe (ASR), Indic-Speak (TTS), Indic-Translate (machine translation), and Indic-OCR - built on the NVIDIA NeMo framework. (The Hindu BusinessLine)

Sources and Further Reading

Artificial Intelligence & Technology's Reconstitution

Institutions & Power Realignment

Scientific & Medical Acceleration

Economics & Labor Transformation

Infrastructure & Engineering Transitions

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