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Step 0: Know Thy Enemy

The key thing to understand about all residential landlords, whether they’re corporate conglomerates or Just Some Asshole, is that their asset—the property—is a Cinderella carriage that magically turns back into an expensive ass pumpkin of a liability any time it’s sitting empty. The property taxes, insurance, mortgage, HOA fees, and maintenance costs all still come due every month/quarter/year whether they have a tenant to cover it all and then some, or not. 

Because of this, at the end of the day, their ultimate goal is to fill every unit at all times with someone who will reliably pay the rent on time and in full. And because everything else is secondary to that goal—and because with the exception of Just Some Asshole landlords, the person responding to your emails and writing up your lease paperwork is several degrees of separation removed from the shareholders who profit off your rent money—they’re almost always willing to negotiate with you. As long as it gets the liability converted into an asset faster or keeps the carriage from turning back into a pumpkin for longer, then in the long run, it’s actually in their best interest to give you a better price. 

Step 1: Identify Your Leverage

If you understand how supply and demand works, you can figure out how much leverage you have pretty easily.  High supply and low demand = you have more leverage, and vice versa. Do they have an “AVAILABLE NOW - MOVE IN TODAY” sandwich board on the sidewalk or a web banner that says “First month free”? Does their website and/or Apartments.com show a bunch of currently open listings? Do you already live there and know at least two families on your floor have moved out in the last several months with no one new moving in to replace them? These are all indications that they have more than one unit currently sitting empty, meaning higher supply and lower demand. No sandwich board and a website that just says “call for availability”? They might just suck at marketing, but more likely, supply is lower and demand is higher. 

You have the least leverage if you’re a prospective tenant looking to move in somewhere that has a waitlist. They have no reason to offer you a discount if six other people are already in line to pay full price for apartments that aren’t even vacant yet (but you can still ask!). You also have no leverage to negotiate if you’ve already signed a lease and you’re in the middle of the lease period; you legally agreed to pay $X/month for Y months, so you’re stuck with that until the lease is up.

At the other end of the spectrum, you have the most leverage if you’re a current tenant who has always paid your rent on time and you’re being offered a renewal on your existing lease with higher rent than you're currently paying, especially if they already have some units that have been empty for a while. If you move out, not only is your unit going to sit vacant for at least part of a month, they’re also probably going to have to put in some work to “turn” the unit (repainting, professional cleaning, etc) to get it in move-in condition for the next tenant.

All of this means that if you move out, even if they can fleece you out of your security deposit and find a new tenant the very next month, it’s still gonna cost them at least a few thousand dollars to turn that pumpkin back into a carriage again. They’re probably willing to come down by $100-$200/month or so on the renewal offer rent if you ask, because they know it’ll actually save them money in the long run. Similar situation if you’re a prospective new tenant—if they can’t get you or anyone else to sign a lease and move in this month, that’s $[whatever the monthly rent is] down the drain, and they’ll never get it back. It’s a perishable item about to spoil. 

Step 2: Get Their Opening Offer

This is the first number they’ll quote you for the rent—the sticker price that you’ve always just accepted as set in stone. The truth is, they’ve built some buffer into that number. There’s almost always some room for them to come down, and depending on your leverage, they will if you ask nicely. But for reasons that baffle me, most people don’t!

Step 3: Wait, Research, & Counter

Don’t reply to their initial offer right away—unless there’s a waitlist (in which case, you have little haggling power anyway), wait a few days. It makes them sweat a bit, and it shows you aren’t desperate. The person who is rushing to reply is not the one who has more leverage in the negotiation, and making them wait reminds them of that. In the meantime, use Apartments.com or Zillow to get an idea of what similar units in the same area are currently going for. Then you come up with your counteroffer.

As a general rule, anything more than about 20-25% below their opening offer (or below market rates) will probably just piss them off or make them take you less seriously. But when we’re talking about your monthly rent over the course of a year or two, even a 10% discount adds up to a lot of money!

When I negotiated our original lease for my current place, I also asked for and got a two year lease term instead of the standard one year. But whatever automated calendar event system they use to remind their leasing office staff when it’s time to send out renewal offers didn’t get the memo about that, so they mistakenly sent me a renewal offer the following year, meaning I got to see how much they would have jacked up the rent if they could’ve. For that second year of the lease alone, my negotiating saved us $3,000! 

Step 4: BDE (Big Dick Emailing) 

Here’s the tricky part. You need to write an email—always negotiate over email if you can, it’s too easy for a salesperson to bowl you over on the phone and anything they say that isn’t in writing means nothing—which simultaneously makes it sound like you would sign a lease with them in a heartbeat and like you are actively flirting with five other apartment complexes right now who all want you so bad it makes them look stupid, because you are just so sexy and fun and your credit score is eight inches flaccid. You need to make them believe you are both highly motivated and ready to sign on the dotted line and willing to just walk away from the table at any second, but if they could just come down a little bit on that number, you’d delete those other hoes’ numbers forever! Here’s the rough script I use every time:

“ Thank you for [your email/the tour/sending over the offer letter/etc]. I have had a chance to review and consider it. I think [name of apartment complex] would be the perfect fit for me, but I am also exploring and touring other options in the area, including a comparable unit nearby at $[a little below your counteroffer number]/month.

If we could come down to $[your counteroffer number]/month on the rent, I would be prepared to sign the lease today. Let me know your thoughts. Thanks! "

Step 6: You Win Either Way

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.