...possibly

“Are there other Graces? Why do you need to specify?”

Rocky considers this, tapping his xenonite-encased claws idly against the floor. 

“No other Grace,” Rocky says. “We just do this.”

“Like an honorific,” Grace guesses. “It goes before everyone’s name? Everyone you know?”

Rocky whirs in surprise. “No, no, no. Not at all.”

“Okay, then who? What makes me qualify?”

Rocky is silent for a moment.

“...Does this offend Grace?” he asks, voice lower. 

Grace blinks. “No! I don’t think so. Is there a reason to be offended?”

“Good,” Rocky says, relieved. Grace is starting to recognize what Eridian relief sounds like. “No, no reason.”

“Who else do you call that?”

“You don’t have this on Earth?”

Grace considers. What for, friends? He couldn’t call Marissa “my Marissa.” That would be weird.

“I still don’t know what it means,” he settles on. “So I couldn’t tell you.”

Rocky groans in frustration. He’s a very impatient tutor. “We just say it.”

“Are you my Rocky?” Grace asks. He hits the two keys to make the my note. 

“I don’t know!” Rocky says. “You decide this.” 

“I decide? Who do you use it for?” 

“You. Adrian. I will use it for my pebbles.” 

Grace blinks. “So few?”

Rocky shifts. “I am… you do not have the word yet. Eridian who works alone often, not close to many other Eridians.”

“What, mechanical engineer?”

Rocky chitters. “Acceptable.”

“Is Adrian your only friend?” 

Rocky draws back, like he’s taken offense. “I have friends. Coworkers. But different.”

So just Adrian. And him. 

“This worries you,” Rocky says.

“Adrian is your mate,” Grace points out. “I’m not your mate.”

“Correct. You are not. You do not like to be a mate.”

Grace isn’t sure how to respond to that, so he ignores it.

“I’m your friend,” Grace says. 

Rocky hesitates, for a second. “...Correct.”

“Like your coworkers.”

“No.”

Grace sighs, rubbing his hand across his face. Okay, maybe that’s fair. It’s not like he felt about any of his coworkers like he feels about Rocky.

“Best friend,” Grace amends. “You’re my best friend, too.”

Rocky hums. “Acceptable.”

“Just acceptable? Now you are offending me. Can you just explain?”

This makes Rocky fall silent for a minute longer than usual. Grace is half-ready to apologize and to say let’s move on and to retype his own name without the superfluous my. 

“The Earth ‘best friend’ is not strong enough,” Rocky says finally. “It does not translate this way.”

Grace runs his fingers through his hair, a little nervous, for some reason. “Okay.”

“It is…” Rocky pauses. He has to pause more often, now that they’re not using the translator, to simplify his language. “It means that I am not Rocky without my Grace. You are part of… of the whole. When I wake up, I think of you. When I work. When I eat. When I think I am going to die.”

Rocky speaks slowly, but it’s still a lot of Eridian for Grace to grasp all at once. Even as he works out the sentences in his head, he can feel hot tears rising in his eyes.

“It means that when you are sick, I am sick,” Rocky continues. “And it means I will take care of you, because taking care of you is taking care of myself.”

Grace bunches up his sleeve, wiping it across his face, blinking furiously.

Rocky’s voice is soft. “So Grace is part of Rocky. Grace is like a cell. You see? My Grace.” 

Grace is quiet, for a moment, trying to get himself together. When he speaks, his voice is shaky.

“...Oh.”

Rocky hums, pressing his carapace gently against Grace’s arm. “You are leaking. Does this make you sad?”

Grace shakes his head, sniffing, crossing his arms across his knees and resting his chin on his sleeves. “Not at all.”

“Good, good.”

“I feel like that,” Grace mumbles. “Just like that. Just exactly—exactly like that.”

“About your old mate?”

“Don’t act stupid. You know I’m talking about you.”

Rocky hums, burrowing closer. Grace curls one arm around his carapace. That’s not enough, so he leans over, dropping his head down so his forehead presses against the xenonite. 

“My Rocky,” Grace whispers in English. 

“My Grace,” Rocky echoes in Eridian. Grace can recognize the note at the start. He will add it when he plays Rocky’s name. 

“How long have you called me that?” Grace asks. “Must’ve been a while. I didn’t notice it change.”

Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.

Her name is Timnit Gebru.

She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.

The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.

The paper had not even been published yet.

Here is what she actually wrote, and why every prediction inside it has now come true.

The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.

This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.

The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.

The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.

Every one of those things has now been documented in deployment.

Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.

The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.

In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.

The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.

In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.

The fifth warning was the one Google cared about most.

Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.

This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.

The paper Google fired her for predicted the model collapse problem before model collapse had a name.

The mechanism behind why this all happened is the part of her work that nobody quotes.

Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.

She was making that argument from inside Google.

Then Google proved her right by removing her.

The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.

Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.

Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.

The question worth sitting with is the one almost no one in the industry will say out loud.

Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.

The researcher Google fired for warning about exactly what is now happening was right.

The company that fired her is now the second-largest deployer of the technology she warned about.

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