machine learning

ars-live:-is-the-ai-bubble-about-to-pop?-a-live-chat-with-ed-zitron.

Ars Live: Is the AI bubble about to pop? A live chat with Ed Zitron.

AI, AI bubble, AI criticism, ars live, Ars Live Conversations, Benj Edwards, Biz & IT, chatgpt, chatgtp, Ed Zitron, machine learning, openai / /

As generative AI has taken off since ChatGPT’s debut, inspiring hundreds of billions of dollars in investments and infrastructure developments, the top question on many people’s minds has been: Is generative AI a bubble, and if so, when will it pop?

To help us potentially answer that question, I’ll be hosting a live conversation with prominent AI critic Ed Zitron on October 7 at 3: 30 pm ET as part of the Ars Live series. As Ars Technica’s senior AI reporter, I’ve been tracking both the explosive growth of this industry and the mounting skepticism about its sustainability.

You can watch the discussion live on YouTube when the time comes.

Zitron is the host of the Better Offline podcast and CEO of EZPR, a media relations company. He writes the newsletter Where’s Your Ed At, where he frequently dissects OpenAI’s finances and questions the actual utility of current AI products. His recent posts have examined whether companies are losing money on AI investments, the economics of GPU rentals, OpenAI’s trillion-dollar funding needs, and what he calls “The Subprime AI Crisis.”

Alt text for this image:

Credit: Ars Technica

During our conversation, we’ll dig into whether the current AI investment frenzy matches the actual business value being created, what happens when companies realize their AI spending isn’t generating returns, and whether we’re seeing signs of a peak in the current AI hype cycle. We’ll also discuss what it’s like to be a prominent and sometimes controversial AI critic amid the drumbeat of AI mania in the tech industry.

While Ed and I don’t see eye to eye on everything, his sharp criticism of the AI industry’s excesses should make for an engaging discussion about one of tech’s most consequential questions right now.

Please join us for what should be a lively conversation about the sustainability of the current AI boom.

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Why iRobot’s founder won’t go within 10 feet of today’s walking robots

Agility Robotics, AI, AI development tools, AI research, Biz & IT, boston dynamics, dexterity, elon musk, Figure, humanoid robots, iRobot, machine learning, manipulation, MIT, openai, optimus, Rethink Robotics, robot AI, robotics, Robots, rodney brooks, safety, Tech, Tesla, walking robots / /

In his post, Brooks recounts being “way too close” to an Agility Robotics Digit humanoid when it fell several years ago. He has not dared approach a walking one since. Even in promotional videos from humanoid companies, Brooks notes, humans are never shown close to moving humanoid robots unless separated by furniture, and even then, the robots only shuffle minimally.

This safety problem extends beyond accidental falls. For humanoids to fulfill their promised role in health care and factory settings, they need certification to operate in zones shared with humans. Current walking mechanisms make such certification virtually impossible under existing safety standards in most parts of the world.

Apollo robot

The humanoid Apollo robot. Credit: Google

Brooks predicts that within 15 years, there will indeed be many robots called “humanoids” performing various tasks. But ironically, they will look nothing like today’s bipedal machines. They will have wheels instead of feet, varying numbers of arms, and specialized sensors that bear no resemblance to human eyes. Some will have cameras in their hands or looking down from their midsections. The definition of “humanoid” will shift, just as “flying cars” now means electric helicopters rather than road-capable aircraft, and “self-driving cars” means vehicles with remote human monitors rather than truly autonomous systems.

The billions currently being invested in forcing today’s rigid, vision-only humanoids to learn dexterity will largely disappear, Brooks argues. Academic researchers are making more progress with systems that incorporate touch feedback, like MIT’s approach using a glove that transmits sensations between human operators and robot hands. But even these advances remain far from the comprehensive touch sensing that enables human dexterity.

Today, few people spend their days near humanoid robots, but Brooks’ 3-meter rule stands as a practical warning of challenges ahead from someone who has spent decades building these machines. The gap between promotional videos and deployable reality remains large, measured not just in years but in fundamental unsolved problems of physics, sensing, and safety.

Why iRobot’s founder won’t go within 10 feet of today’s walking robots Read More »

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California’s newly signed AI law just gave Big Tech exactly what it wanted

AI, AI development tools, AI ethics, AI law, AI regulation, AI safety, Andreessen Horowitz, Anthropic, Biz & IT, California, Fei-Fei Li, Gavin Newsom, Google, Jack Clark, machine learning, Meta, openai, Policy, Scott Wiener / /

On Monday, California Governor Gavin Newsom signed the Transparency in Frontier Artificial Intelligence Act into law, requiring AI companies to disclose their safety practices while stopping short of mandating actual safety testing. The law requires companies with annual revenues of at least $500 million to publish safety protocols on their websites and report incidents to state authorities, but it lacks the stronger enforcement teeth of the bill Newsom vetoed last year after tech companies lobbied heavily against it.

The legislation, S.B. 53, replaces Senator Scott Wiener’s previous attempt at AI regulation, known as S.B. 1047, that would have required safety testing and “kill switches” for AI systems. Instead, the new law asks companies to describe how they incorporate “national standards, international standards, and industry-consensus best practices” into their AI development, without specifying what those standards are or requiring independent verification.

“California has proven that we can establish regulations to protect our communities while also ensuring that the growing AI industry continues to thrive,” Newsom said in a statement, though the law’s actual protective measures remain largely voluntary beyond basic reporting requirements.

According to the California state government, the state houses 32 of the world’s top 50 AI companies, and more than half of global venture capital funding for AI and machine learning startups went to Bay Area companies last year. So while the recently signed bill is state-level legislation, what happens in California AI regulation will have a much wider impact, both by legislative precedent and by affecting companies that craft AI systems used around the world.

Transparency instead of testing

Where the vetoed SB 1047 would have mandated safety testing and kill switches for AI systems, the new law focuses on disclosure. Companies must report what the state calls “potential critical safety incidents” to California’s Office of Emergency Services and provide whistleblower protections for employees who raise safety concerns. The law defines catastrophic risk narrowly as incidents potentially causing 50+ deaths or $1 billion in damage through weapons assistance, autonomous criminal acts, or loss of control. The attorney general can levy civil penalties of up to $1 million per violation for noncompliance with these reporting requirements.

California’s newly signed AI law just gave Big Tech exactly what it wanted Read More »

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Can AI detect hedgehogs from space? Maybe if you find brambles first.

AI, Biz & IT, cambridge, computer vision, Ecology, environmental monitoring, field validation, habitat mapping, iNaturalist, machine learning, remote sensing, satellite imagery, Sentinel satellites, TESSERA, wildlife conservation / /

“It took us about 20 seconds to find the first one in an area indicated by the model,” wrote Jaffer in a blog post documenting the field test. Starting at Milton Community Centre, where the model showed high confidence of brambles near the car park, the team systematically visited locations with varying prediction levels.

The research team locating their first bramble.

The research team locating their first bramble. Credit: Sadiq Jaffer

At Milton Country Park, every high-confidence area they checked contained substantial bramble growth. When they investigated a residential hotspot, they found an empty plot overrun with brambles. Most amusingly, a major prediction in North Cambridge led them to Bramblefields Local Nature Reserve. True to its name, the area contained extensive bramble coverage.

The model reportedly performed best when detecting large, uncovered bramble patches visible from above. Smaller brambles under tree cover showed lower confidence scores—a logical limitation given the satellite’s overhead perspective. “Since TESSERA is learned representation from remote sensing data, it would make sense that bramble partially obscured from above might be harder to spot,” Jaffer explained.

An early experiment

While the researchers expressed enthusiasm over the early results, the bramble detection work represents a proof-of-concept that is still under active research. The model has not yet been published in a peer-reviewed journal, and the field validation described here was an informal test rather than a scientific study. The Cambridge team acknowledges these limitations and plans more systematic validation.

However, it’s still a relatively positive research application of neural network techniques that reminds us that the field of artificial intelligence is much larger than just generative AI models, such as ChatGPT, or video synthesis models.

Should the team’s research pan out, the simplicity of the bramble detector offers some practical advantages. Unlike more resource-intensive deep learning models, the system could potentially run on mobile devices, enabling real-time field validation. The team considered developing a phone-based active learning system that would enable field researchers to improve the model while verifying its predictions.

In the future, similar AI-based approaches combining satellite remote sensing with citizen science data could potentially map invasive species, track agricultural pests, or monitor changes in various ecosystems. For threatened species like hedgehogs, rapidly mapping critical habitat features becomes increasingly valuable during a time when climate change and urbanization are actively reshaping the places that hedgehogs like to call home.

Can AI detect hedgehogs from space? Maybe if you find brambles first. Read More »

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Why does OpenAI need six giant data centers?

AI, AI chips, AI development tools, AI infrastructure, Biz & IT, chatgpt, Clay Magouyrk, CoreWeave, datacenters, machine learning, Masayoshi Son, NVIDIA, openai, oracle, sam altman, SB Energy, SoftBank / /

Training next-generation AI models compounds the problem. On top of running existing AI models like those that power ChatGPT, OpenAI is constantly working on new technology in the background. It’s a process that requires thousands of specialized chips running continuously for months.

The circular investment question

The financial structure of these deals between OpenAI, Oracle, and Nvidia has drawn scrutiny from industry observers. Earlier this week, Nvidia announced it would invest up to $100 billion as OpenAI deploys Nvidia systems. As Bryn Talkington of Requisite Capital Management told CNBC: “Nvidia invests $100 billion in OpenAI, which then OpenAI turns back and gives it back to Nvidia.”

Oracle’s arrangement follows a similar pattern, with a reported $30 billion-per-year deal where Oracle builds facilities that OpenAI pays to use. This circular flow, which involves infrastructure providers investing in AI companies that become their biggest customers, has raised eyebrows about whether these represent genuine economic investments or elaborate accounting maneuvers.

The arrangements are becoming even more convoluted. The Information reported this week that Nvidia is discussing leasing its chips to OpenAI rather than selling them outright. Under this structure, Nvidia would create a separate entity to purchase its own GPUs, then lease them to OpenAI, which adds yet another layer of circular financial engineering to this complicated relationship.

“NVIDIA seeds companies and gives them the guaranteed contracts necessary to raise debt to buy GPUs from NVIDIA, even though these companies are horribly unprofitable and will eventually die from a lack of any real demand,” wrote tech critic Ed Zitron on Bluesky last week about the unusual flow of AI infrastructure investments. Zitron was referring to companies like CoreWeave and Lambda Labs, which have raised billions in debt to buy Nvidia GPUs based partly on contracts from Nvidia itself. It’s a pattern that mirrors OpenAI’s arrangements with Oracle and Nvidia.

So what happens if the bubble pops? Even Altman himself warned last month that “someone will lose a phenomenal amount of money” in what he called an AI bubble. If AI demand fails to meet these astronomical projections, the massive data centers built on physical soil won’t simply vanish. When the dot-com bubble burst in 2001, fiber optic cable laid during the boom years eventually found use as Internet demand caught up. Similarly, these facilities could potentially pivot to cloud services, scientific computing, or other workloads, but at what might be massive losses for investors who paid AI-boom prices.

Why does OpenAI need six giant data centers? Read More »

when-“no”-means-“yes”:-why-ai-chatbots-can’t-process-persian-social-etiquette

When “no” means “yes”: Why AI chatbots can’t process Persian social etiquette

AI, AI behavior, AI bias, AI ethics, AI limitations, AI research, Anthropic, Biz & IT, cross-cultural AI, cultural alignment, DeepSeek, Google, language models, large language models, machine learning, microsoft, openai / /

If an Iranian taxi driver waves away your payment, saying, “Be my guest this time,” accepting their offer would be a cultural disaster. They expect you to insist on paying—probably three times—before they’ll take your money. This dance of refusal and counter-refusal, called taarof, governs countless daily interactions in Persian culture. And AI models are terrible at it.

New research released earlier this month titled “We Politely Insist: Your LLM Must Learn the Persian Art of Taarof” shows that mainstream AI language models from OpenAI, Anthropic, and Meta fail to absorb these Persian social rituals, correctly navigating taarof situations only 34 to 42 percent of the time. Native Persian speakers, by contrast, get it right 82 percent of the time. This performance gap persists across large language models such as GPT-4o, Claude 3.5 Haiku, Llama 3, DeepSeek V3, and Dorna, a Persian-tuned variant of Llama 3.

A study led by Nikta Gohari Sadr of Brock University, along with researchers from Emory University and other institutions, introduces “TAAROFBENCH,” the first benchmark for measuring how well AI systems reproduce this intricate cultural practice. The researchers’ findings show how recent AI models default to Western-style directness, completely missing the cultural cues that govern everyday interactions for millions of Persian speakers worldwide.

“Cultural missteps in high-consequence settings can derail negotiations, damage relationships, and reinforce stereotypes,” the researchers write. For AI systems increasingly used in global contexts, that cultural blindness could represent a limitation that few in the West realize exists.

A taarof scenario diagram from TAAROFBENCH, devised by the researchers. Each scenario defines the environment, location, roles, context, and user utterance.

A taarof scenario diagram from TAAROFBENCH, devised by the researchers. Each scenario defines the environment, location, roles, context, and user utterance. Credit: Sadr et al.

“Taarof, a core element of Persian etiquette, is a system of ritual politeness where what is said often differs from what is meant,” the researchers write. “It takes the form of ritualized exchanges: offering repeatedly despite initial refusals, declining gifts while the giver insists, and deflecting compliments while the other party reaffirms them. This ‘polite verbal wrestling’ (Rafiee, 1991) involves a delicate dance of offer and refusal, insistence and resistance, which shapes everyday interactions in Iranian culture, creating implicit rules for how generosity, gratitude, and requests are expressed.”

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White House officials reportedly frustrated by Anthropic’s law enforcement AI limits

AI, AI ethics, AI law, AI regulation, AI security, Amazon Web Services, Anthropic, Biz & IT, Claude, Dario Amodei, Department of Defense, fbi, government contracting, ice, law enforcement, machine learning, national security, openai, Policy, Secret Service, surveillance, trump administration / /

Anthropic’s AI models could potentially help spies analyze classified documents, but the company draws the line at domestic surveillance. That restriction is reportedly making the Trump administration angry.

On Tuesday, Semafor reported that Anthropic faces growing hostility from the Trump administration over the AI company’s restrictions on law enforcement uses of its Claude models. Two senior White House officials told the outlet that federal contractors working with agencies like the FBI and Secret Service have run into roadblocks when attempting to use Claude for surveillance tasks.

The friction stems from Anthropic’s usage policies that prohibit domestic surveillance applications. The officials, who spoke to Semafor anonymously, said they worry that Anthropic enforces its policies selectively based on politics and uses vague terminology that allows for a broad interpretation of its rules.

The restrictions affect private contractors working with law enforcement agencies who need AI models for their work. In some cases, Anthropic’s Claude models are the only AI systems cleared for top-secret security situations through Amazon Web Services’ GovCloud, according to the officials.

Anthropic offers a specific service for national security customers and made a deal with the federal government to provide its services to agencies for a nominal $1 fee. The company also works with the Department of Defense, though its policies still prohibit the use of its models for weapons development.

In August, OpenAI announced a competing agreement to supply more than 2 million federal executive branch workers with ChatGPT Enterprise access for $1 per agency for one year. The deal came one day after the General Services Administration signed a blanket agreement allowing OpenAI, Google, and Anthropic to supply tools to federal workers.

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Millions turn to AI chatbots for spiritual guidance and confession

AI, AI assistants, AI behavior, AI sycophancy, Biz & IT, chatbots, chatgpt, Gemini, generative ai, Google, large language models, machine learning, openai / /

Privacy concerns compound these issues. “I wonder if there isn’t a larger danger in pouring your heart out to a chatbot,” Catholic priest Fr. Mike Schmitz told The Times. “Is it at some point going to become accessible to other people?” Users share intimate spiritual moments that now exist as data points in corporate servers.

Some users prefer the chatbots’ non-judgmental responses to human religious communities. Delphine Collins, a 43-year-old Detroit preschool teacher, told the Times she found more support on Bible Chat than at her church after sharing her health struggles. “People stopped talking to me. It was horrible.”

App creators maintain that their products supplement rather than replace human spiritual connection, and the apps arrive as approximately 40 million people have left US churches in recent decades. “They aren’t going to church like they used to,” Beck said. “But it’s not that they’re less inclined to find spiritual nourishment. It’s just that they do it through different modes.”

Different modes indeed. What faith-seeking users may not realize is that each chatbot response emerges fresh from the prompt you provide, with no permanent thread connecting one instance to the next beyond a rolling history of the present conversation and what might be stored as a “memory” in a separate system. When a religious chatbot says, “I’ll pray for you,” the simulated “I” making that promise ceases to exist the moment the response completes. There’s no persistent identity to provide ongoing spiritual guidance, and no memory of your spiritual journey beyond what gets fed back into the prompt with every query.

But this is spirituality we’re talking about, and despite technical realities, many people will believe that the chatbots can give them divine guidance. In matters of faith, contradictory evidence rarely shakes a strong belief once it takes hold, whether that faith is placed in the divine or in what are essentially voices emanating from a roll of loaded dice. For many, there may not be much difference.

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Modder injects AI dialogue into 2002’s Animal Crossing using memory hack

AI, AI integration, Animal Crossing, Biz & IT, chatgpt, Dolphin emulator, Emulation, game hacking, game modding, GameCube, gaming, generative ai, Google, large language models, machine learning, Nintendo, openai, retrogaming, retrotech, Tom Nook / /

But discovering the addresses was only half the problem. When you talk to a villager in Animal Crossing, the game normally displays dialogue instantly. Calling an AI model over the Internet takes several seconds. Willison examined the code and found Fonseca’s solution: a watch_dialogue() function that polls memory 10 times per second. When it detects a conversation starting, it immediately writes placeholder text: three dots with hidden pause commands between them, followed by a “Press A to continue” prompt.

“So the user gets a ‘press A to continue’ button and hopefully the LLM has finished by the time they press that button,” Willison noted in a Hacker News comment. While players watch dots appear and reach for the A button, the mod races to get a response from the AI model and translate it into the game’s dialog format.

Learning the game’s secret language

Simply writing text to memory froze the game. Animal Crossing uses an encoded format with control codes that manage everything from text color to character emotions. A special prefix byte (0x7F) signals commands rather than characters. Without the proper end-of-conversation control code, the game waits forever.

“Think of it like HTML,” Fonseca explains. “Your browser doesn’t just display words; it interprets tags … to make text bold.” The decompilation community had documented these codes, allowing Fonseca to build encoder and decoder tools that translate between a human-readable format and the GameCube’s expected byte sequences.

A screenshot of LLM-powered dialog injected into Animal Crossing for the GameCube.

A screenshot of LLM-powered dialog injected into Animal Crossing for the GameCube. Credit: Joshua Fonseca

Initially, he tried using a single AI model to handle both creative writing and technical formatting. “The results were a mess,” he notes. “The AI was trying to be a creative writer and a technical programmer simultaneously and was bad at both.”

The solution: split the work between two models. A Writer AI creates dialogue using character sheets scraped from the Animal Crossing fan wiki. A Director AI then adds technical elements, including pauses, color changes, character expressions, and sound effects.

The code is available on GitHub, though Fonseca warns it contains known bugs and has only been tested on macOS. The mod requires Python 3.8+, API keys for either Google Gemini or OpenAI, and Dolphin emulator. Have fun sticking it to the man—or the raccoon, as the case may be.

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Education report calling for ethical AI use contains over 15 fake sources

academic integrity, AI, AI Citations, AI confabulations, AI ethics, AI hallucination, AI in education, confabulations, education policy, generative ai, machine learning, Memorial University, Newfoundland and Labrador / /

AI language models like the kind that power ChatGPT, Gemini, and Claude excel at producing exactly this kind of believable fiction when they lack actual information on a topic because they first and foremost produce plausible outputs, not accurate ones. If there are no patterns in the dataset that match what the user is seeking they will create the best approximation based on statistical patterns learned during training. Even AI models that can search the web for real sources can potentially fabricate citations, choose the wrong ones, or mischaracterize them.

“Errors happen. Made-up citations are a totally different thing where you essentially demolish the trustworthiness of the material,” Josh Lepawsky, the former president of the Memorial University Faculty Association who resigned from the report’s advisory board in January, told CBC, citing a “deeply flawed process.”

The irony runs deep

The presence of potentially AI-generated fake citations becomes especially awkward given that one of the report’s 110 recommendations specifically states the provincial government should “provide learners and educators with essential AI knowledge, including ethics, data privacy, and responsible technology use.”

Sarah Martin, a Memorial political science professor who spent days reviewing the document, discovered multiple fabricated citations. “Around the references I cannot find, I can’t imagine another explanation,” she told CBC. “You’re like, ‘This has to be right, this can’t not be.’ This is a citation in a very important document for educational policy.”

When contacted by CBC, co-chair Karen Goodnough declined an interview request, writing in an email: “We are investigating and checking references, so I cannot respond to this at the moment.”

The Department of Education and Early Childhood Development acknowledged awareness of “a small number of potential errors in citations” in a statement to CBC from spokesperson Lynn Robinson. “We understand that these issues are being addressed, and that the online report will be updated in the coming days to rectify any errors.”

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openai-and-microsoft-sign-preliminary-deal-to-revise-partnership-terms

OpenAI and Microsoft sign preliminary deal to revise partnership terms

AI, AI development, AI ethics, AI law, AI regulation, AI research, API, Artificial Intelligence, Biz & IT, Bret Taylor, chatbots, chatgpt, generative ai, large language models, machine learning, microsoft, openai, sam altman, Satya Nadella / /

On Thursday, OpenAI and Microsoft announced they have signed a non-binding agreement to revise their partnership, marking the latest development in a relationship that has grown increasingly complex as both companies compete for customers in the AI market and seek new partnerships for growing infrastructure needs.

“Microsoft and OpenAI have signed a non-binding memorandum of understanding (MOU) for the next phase of our partnership,” the companies wrote in a joint statement. “We are actively working to finalize contractual terms in a definitive agreement. Together, we remain focused on delivering the best AI tools for everyone, grounded in our shared commitment to safety.”

The announcement comes as OpenAI seeks to restructure from a nonprofit to a for-profit entity, a transition that requires Microsoft’s approval, as the company is OpenAI’s largest investor, with more than $13 billion committed since 2019.

The partnership has shown increasing strain as OpenAI has grown from a research lab into a company valued at $500 billion. Both companies now compete for customers, and OpenAI seeks more compute capacity than Microsoft can provide. The relationship has also faced complications over contract terms, including provisions that would limit Microsoft’s access to OpenAI technology once the company reaches so-called AGI (artificial general intelligence)—a nebulous milestone both companies now economically define as AI systems capable of generating at least $100 billion in profit.

In May, OpenAI abandoned its original plan to fully convert to a for-profit company after pressure from former employees, regulators, and critics, including Elon Musk. Musk has sued to block the conversion, arguing it betrays OpenAI’s founding mission as a nonprofit dedicated to benefiting humanity.

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Developers joke about “coding like cavemen” as AI service suffers major outage

agentic AI, AI, AI agents, AI assistants, AI behavior, AI coding, AI development tools, AI infrastructure, AI programming, AI work, Anthropic, API, Biz & IT, Claude Code, cloud infrastructure, Developer Tools, machine learning, outages, service reliability, software development, terminal tools / /

Growing dependency on AI coding tools

The speed at which news of the outage spread shows how deeply embedded AI coding assistants have already become in modern software development. Claude Code, announced in February and widely launched in May, is Anthropic’s terminal-based coding agent that can perform multi-step coding tasks across an existing code base.

The tool competes with OpenAI’s Codex feature, a coding agent that generates production-ready code in isolated containers, Google’s Gemini CLI, Microsoft’s GitHub Copilot, which itself can use Claude models for code, and Cursor, a popular AI-powered IDE built on VS Code that also integrates multiple AI models, including Claude.

During today’s outage, some developers turned to alternative solutions. “Z.AI works fine. Qwen works fine. Glad I switched,” posted one user on Hacker News. Others joked about reverting to older methods, with one suggesting the “pseudo-LLM experience” could be achieved with a Python package that imports code directly from Stack Overflow.

While AI coding assistants have accelerated development for some users, they’ve also caused problems for others who rely on them too heavily. The emerging practice of so-called “vibe coding“—using natural language to generate and execute code through AI models without fully understanding the underlying operations—has led to catastrophic failures.

In recent incidents, Google’s Gemini CLI destroyed user files while attempting to reorganize them, and Replit’s AI coding service deleted a production database despite explicit instructions not to modify code. These failures occurred when the AI models confabulated successful operations and built subsequent actions on false premises, highlighting the risks of depending on AI assistants that can misinterpret file structures or fabricate data to hide their errors.

Wednesday’s outage served as a reminder that as dependency on AI grows, even minor service disruptions can become major events that affect an entire profession. But perhaps that could be a good thing if it’s an excuse to take a break from a stressful workload. As one commenter joked, it might be “time to go outside and touch some grass again.”

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