马上评｜澳门，何以文明互鉴
How are you dealing with all the negative side effects of AI on the web?
It seems to me that the AI hype killed the web for good, but not for the reasons we immediately associate with it.
Search engines now rank links full of slop at the top, which ironically make LLM summaries acceptable, not because they are better than what we had before (they are not), but because they at least mention the good links which are now buried at the bottom of the page. Also, they've been rate limiting ordinary users like crazy: more often than I'd like to, Google and DuckDuckGo now either show me either an error message or tell me to wait a bit. Sometimes I waste at least a minute refreshing the page and waiting for a result.
I also can't browse GitHub freely without being logged in. Anubis is now everywhere, occasionally blocking me. I feel like my productivity has actually gone down thanks to all of that.
Edit: I see that most of the replies here are suggesting relying less on the web and going after more fundamental resources, which are great suggestions, don’t get me wrong! But a book from 5 years ago will not help you understand why your custom firmware doesn’t boot in your 1-year old mobile chipset. A existing bug report on the web probably will. Not quite my predicament at the moment, but something very similar for which the web seemed to work just fine before the AI craze.
The most scathing reviewer 2 review I have ever read
This is not my paper. Not my review either.
Official Review of Submission21893 by Reviewer Xz8e
Paper Summary:
The paper tries to learn correlations among components of each latent state in a world model to propagate the reward sensitivities to adaptively weight the uncertainties that most matter for long-horizon planning via a pessimistic objective.
Presentation: 1: Difficult to follow despite careful reading.
Informativeness: 1: I learned little or nothing new from this paper.
Soundness: 1: I have significant concerns about the paper’s soundness that do not appear easy to address.
Critical Strengths:
None
Critical Weaknesses:
First and foremost, the paper is highly likely to be fully AI-generated. The authors demonstrate a lack of technical accuracy and depth. They fail to communicate their ideas clearly and accurately. The paper makes few original contributions. See evidence below.
Neologistic and full of jagons that add little value of presenting research gaps, methodologies, and their contributions. I don’t believe authors even understand those terms before stitching them together.
Abstract is poorly written. This is not intended to communicate efficiently with readers. This is to create barriers in understanding their work. From the second sentence, “a scalar uncertainty”, “sparse latent dynamics” come out of nowhere and don’t know what they are referring to. And so many such examples follow thereafter: “factor-level residual bounds”, “retains initialization error”, “expected-error control”, “exact certificate”, “removing error-propagating dependencies and introducing approximation bias”, “localized mechanism-shift”, “comparator-dependent regret bound under simultaneous validity”, “a decision-relevant uncertainty bound”. And showcase experimental results without stating tasks?
Introduction is also disconnected, poorly motivated, fragmented. From the first paragraph “which model errors matter for a long-horizon decision, especially after an environmental mechanism changes?”, what is “environmental mechanism changes” here? Is it handling non-stationary environments, or continual learning tasks? Why brings something out of vacuum? Then the second paragraph pumps out “a global Lipschitz constant”, and reiterate what so-called “sparsity” And then jumps to uncertainty with a set of new terms “A small predicted covariance”, “Marginal calibration”, “a high-probability interval” and close by “Exploration should reduce uncertainty about mechanisms that affect decisions, rather than maximize uncertainty indiscriminately.”: okay, what uncertainty are you focusing on? epistemic or aleatoric? Methods and contributions are also vague and lack clarity.
Method section is horrible.
What do you mean by “a sufficient latent state”? An approximate model can hardly find sufficient statistics.
In the Gaussian transition model, what is z_t^(pa_e(i))?
What is the difference between z_(t+1)^i and ẑ_(t+1)^i, is the latter deterministic and why so?
What is “nominal imagination”? Where does “sparsity” come from and what is “a within-time DAG”? What is “nominal systems”? What is feedback policy? What problem setting are you looking at? Where does the “recurrent state” come from the encoder? What is mechanism i?
What is the motivation of introducing the adjacency matrix? If you intend to learn the dependency between different subcomponents of the latent state, use one sentence is enough.
And several notations are conflating, like parameter posterior q(θ|D) with the state posterior. The training objective is not motivated.
Experiments are not scientific. No random seeds reported, no task descriptions, no metrics explanations.
This paper is an affront to scientific research and reviewers’ time.
Questions For Authors:
None
Flag For Ethics Review: No
Rating: 1: Clear rejection
Confidence: 4: I have substantial direct research experience on this topic.
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This CEO lived on canned soup and took just two days off for his daughter’s birth. Now, he admits he lost sight of proper work-life balance
The grind to success can be long, punishing, and it can quietly take more than it gives.
Ron Schneidermann knows that better than most.
After scaling his first company, Liftopia, into a business with more than $60 million in annual revenue, he went on to become CEO of AllTrails, the popular hiking map app. Today, he leads test-prep startup Acely. But looking back across his career, he says he had to learn the hard way what real work-life balance actually costs—and what it’s worth.
While building Liftopia, a digital marketplace for ski resorts, that mindset came at a personal price. Schneidermann worked out of a cramped San Francisco apartment, turned spending less than $15 a day into a kind of endurance test, and went two years without taking a salary, surviving largely on canned soup.
The tradeoffs even extended into his family life. When his first daughter was born four years into Liftopia, he took just two days off work. Three years later, he allowed himself just a week for his son’s birth—and thought that was progress.
“I look back, I was just able to justify it as ‘that’s just part of the grind’… but you never get that time back,” Schneidermann told Fortune. “That was a mistake.”
It’s not an easy admission in startup culture, where sacrifice is often worn as a badge of honor and overwork is normalized as the price of ambition. But Schneidermann, now in his late 40s, has since reframed the experience entirely.
“For everything that was frustrating, that went wrong, that I regretted about Liftopia, I was able to take the inverse and turn it into a strength,” he said.
By the time he joined AllTrails in 2015, that mindset shift was already underway. He eventually became CEO in 2019 and instituted a company-wide ritual: the first Friday of every month, AllTrails shut down and employees were encouraged to go outside.
In August 2025, he took on the new challenge of becoming CEO of Acely. This time, he’s bringing those hard-won lessons with him from the start. Instead of monthly trail days, the company now runs a monthly “hackathon,” where its less than a dozen employees pause routine work for the whole day and experiment with AI tools and new ideas—no meetings, no KPIs, no deliverables.
Ron Schneidermann says he’ll never take a job for its salary ever again
Even before Schneidermann had his eyes on entrepreneurship, he was learning career lessons the hard way.
In the late 1990s, he was a student at UCLA studying mass communication and business while also working at Abercrombie & Fitch to help pay for his studies. However, he ended up getting fired three separate times because he “never liked it” enough to muster the proper decorum.
Similarly, after graduating in 2000, he landed a job at Accenture working as a business process consultant. Once again, though, he found it not aligning with the life he wanted to have. And despite the lucrative salary of working at a Fortune 500 consulting firm, he quit.
“It was a great experience. I’m grateful for it, but I hated it,” he said. “I hated it, and I told myself, I am never going to take a job for money again. Life is too short.”
That realization eventually pushed him toward startups.
Through a friend of a friend, he landed a role at Hotwire, then a travel startup that was later acquired by Expedia, and it became his entry point into the tech world. Looking back, Schneidermann said that early career path offers a lesson he wishes more young workers would take seriously: relationships matter as much as résumés, especially in a job market where traditional entry-level pipelines are narrowing.
“Really invest in your network,” he said. “There’s a small pond at the end of the day. If you stay in it long enough, you are going to know everyone, and everyone you know are going to be leaders somewhere.”
Digital-native Gen Zers can boost their careers by working to build real relationships
Schneidermann’s point may resonate with Gen Z at a time when employers say early-career workers are struggling with basic professional norms. A 2024 survey found that six in 10 bosses admitted they had already fired Gen Z employees hired straight out of college, citing poor communication, lack of professionalism, and disorganization.
For Schneidermann, the issue isn’t just skills—it’s mindset. Too many young workers treat networking as transactional, when in reality it’s built on consistency, reputation, and how you show up over time. That starts with small, often overlooked behaviors.
“There’s a human on the other end. Don’t ghost people and just show up,” he said.
The same principle applies more broadly to career growth. Rather than chasing the perfect role or obsessing over credentials, Schneidermann emphasized curiosity as the real differentiator.
“Just approach everything with a curious mindset,” he added. “Just constantly try to be learning and exploring.”
That philosophy has carried through multiple career pivots, including his most recent move to Acely, a startup with no obvious connection to his background. After being approached by an executive search firm, he took the job in part because his high school–aged daughter had recently used the product for SAT prep. She was among its over 50,000 active users.
For Schneidermann, the challenge was one he was eager to take on. From his perspective, he didn’t need to be an expert in the industry—he just needed to be willing to learn it.
A version of this story was published on Fortune.com on April 25, 2026.
More on careers
Starter homes cost $1 million in a record 242 U.S. cities—proof the affordability crisis has hit the homes meant for first-time buyers
They waited out the pandemic boom. They saved longer, rented longer, delayed longer, and watched the typical first-time homebuyer age climb to a record 40. Now, the buyers who did everything right are running into a new problem: The Northeast just became the fastest-growing region for million-dollar starter homes in the country.
It’s especially imperfectly timed for millennials entering their peak household spending years and beginning to form their own families (or at least try to).
A Zillow report published in June counts a record 242 U.S. cities where starter homes cost $1 million or more—triple the 80 cities that cleared that mark before the pandemic, and up from 226 a year ago.
The share of first-time buyers has fallen to half the historical norm. For millennial parents in the Northeast—now in their late thirties and early forties, often with kids, hunting for more space—the numbers have a specific shape.
New Jersey had just one million-dollar starter city before the pandemic. Now it has 26. New York used to have 12; now it has 41. The two states added 15 cities to the list in the past year alone—faster growth than anywhere else in the country. The New York City metro area now leads all with 63 cities where a starter home runs $1 million or more.
That growth didn’t happen in a vacuum. Six of the 10 most competitive housing markets in the country are in the Northeast, per Zillow’s 2026 analysis, a region where new construction has chronically lagged and inventory deficits run deepest. California still leads the raw count with 105 cities, but the Northeast is where the crisis is actively spreading.
“A housing shortage a decade in the making ran headlong into intense demand with mortgage rates at historic lows, driving up home values at a record pace,” Zillow senior economist Kara Ng wrote. For Northeast buyers, those forces have compounded.
The new typical first-time homebuyer is 40
First-time homebuyers are historically younger, although the typical age of first-time buyers climbed to an all-time high of 40, according to a National Association of Realtors report released in November 2025. In fact, the housing market has gotten so unaffordable that the number of first-time homebuyers fell to half the historical norm last July.
A rising price floor on starter homes only pushes that first home purchase further out of reach. And it’s a problem that’s worsened, especially during the past couple of years.
“For homebuyers, the prevalence of million-dollar cities significantly—greatly—strains what is already the worst housing affordability since at least the 1980s,” Christopher M. Naghibi, executive vice president and chief operating officer at First Foundation Bank, told Fortune in 2024 when Zillow released a report about the number of U.S. cities with $1 million median-priced homes. “Prospective buyers in these markets face higher barriers to entry, needing ridiculously large down payments and higher incomes to afford homes.” That still holds true today.
Zillow defines a starter home as one in the lowest third of home values in a given region. As of June, the typical starter home nationwide was worth $198,649, up 1.7% from a year earlier. But in well over 200 U.S. cities, starter home prices are a whopping five times as high as that. It’s a phenomenon that keeps spreading, and into places it never used to touch.
Zillow chalks it up to the effects of the pandemic housing boom.
It’s not just a California problem anymore
Before 2020, the list of cities with $1 million starter homes was almost all coastal. Colorado was the only non-coastal state with a million-dollar starter-home city. Now 26 states have at least one, up from nine before the pandemic. Texas, Wyoming, and Illinois each have multiple examples.
California still leads with 105 cities, but the fastest growth is in the Northeast. New York and New Jersey added 15 cities combined over the past year. New York is now up to 41, from just 12 before the pandemic. New Jersey is at 26, up from one.
The New York City metro area tops all metros with 63 cities where a starter home runs $1 million or more. San Francisco follows with 37, then Los Angeles with 33, San Jose with 13, and Miami and Seattle with eight each. The trend aligns with Zillow’s 2026 hottest markets analysis, which found six of the 10 most competitive housing markets are in the Northeast, where new construction has lagged and inventory deficits run deep.
The pandemic reset the cost of buying a home. A housing shortage a decade in the making collided with surging demand and historically low mortgage rates, pushing values up at a record pace. Those effects have stuck.
Baby boomers edge younger buyers out of the housing market
The squeeze on millennials and Gen Z has been building for years. Baby boomers, sitting on paid-off homes or cheap pandemic-era mortgages, have edged younger buyers out of the market in record numbers. The cost has pushed some Americans to delay milestones like marriage and kids, and others to co-buy homes with friends or partners just to split the bill.
Others have done the opposite—had kids first, rented longer, and are now hunting for space in a market that moved against them.
“We’re seeing a reshaping of the housing ladder,” Alexandra Gupta, a real-estate broker with the Corcoran Group, previously told Fortune. “Some first-time buyers are turning to long-term renting or even co-living models because the idea of owning a home has become so out of reach.”
There are modest signs of relief. Conditions are slowly turning friendlier for buyers. Zillow found the typical buyer now breaks even versus renting after about six years, down from more than eight in late 2023. More inventory and slower price growth mean buyers who are financially prepared are in better shape than they had
Billionaire Brian Chesky’s social-worker parents told him to pick a high-paying career. He went to art school—and started Airbnb with 3 air beds
Before Airbnb had over 9 million listings worldwide, there was one sole lodging: three inflatable air beds at CEO Brian Chesky’s San Francisco home. He had quit his job and packed up all his belongings in a Honda Civic to make a cross-country move, and couldn’t pay his rent. That’s when he noticed all the local hotels were booked because of the Industrial Designers Society of America’s International Design Conference in San Francisco, and a business was born.
“We called it the air-bed-and-breakfast,” he told Fortune in 2017 at a World’s Greatest Leaders event.
Chesky and Airbnb cofounder Joe Gebbia were hooked on the idea of making money and meeting new friends through their makeshift accommodations, so they opened their home to three guests: “What if you could book someone’s home the way you could book a hotel anywhere in the world?” Chesky said about their inspiration. They launched Airbnb with Gebbia’s former roommate Nathan Blecharczyk in 2008.
Chesky’s social-worker parents in upstate New York (he was born in Albany) told him to pursue a high-paying career, but much to their chagrin he went to art school instead, at the famous Rhode Island School of Design.
“I’m going to become an artist,” Chesky recalls telling his mom, to which she responded: “You somehow managed to pick the only job in the world that pays less than a social worker. You’ll get paid nothing.”
Today, Chesky doesn’t have to worry as much.
Airbnb has a market capitalization of $112 billion, above its level from before the pandemic, which threatened its very survival as a business. The company lost 80% of its business in the first months of the pandemic, and Chesky reorganized it in a scramble for survival.
How was Chesky’s business ethos shaped by his creative background?
Though Chesky’s creativity is what birthed (and saved) Airbnb, it also meant business veterans did not initially take him seriously.
Chesky’s design background was at first a hindrance to a budding Airbnb. While looking for $150,000 to fund an estimated 10% of the company, Chesky met with over 20 potential investors, all of whom turned him down.
“Joe and I were designers, and as far as they were concerned, designers didn’t start companies,” Chesky said. “We didn’t look like a tech founder, which is ridiculous because I don’t think you should ever hire somebody because they look like something else. You want a new thing.”
Investors didn’t take an interest in Airbnb until it began designing presidential-candidate-themed cereal boxes, which eventually got the attention of Paul Graham, founder of startup accelerator Y Combinator.
“Creative companies are companies that tap into a sense of imagination,” Chesky told Time. “That they might come up with new breakthrough ideas, that people that were only beholden to near-term measurable outcomes won’t ever have.”
How has Airbnb navigated challenges?
Success at Airbnb has been a double-edged sword. Explosive growth has forced the company to refocus at times.
During the pandemic, Chesky told Fortune in 2023, he even opened his own home as an Airbnb to better understand the host experience and lived in various Airbnbs across the country for six months, where he discovered inconsistent and unnecessary practices across hosts, including complicated rent agreements.
Airbnb still faces challenges as waves of short-term rental regulations in New York and across Europe, where cities are implementing phase-outs, prohibiting rentals for less than 30 days, or no longer renewing tourist apartment licenses, for example. In the U.S., housing advocates have fought against Airbnb, according
Berkshire Hathaway stock is a haven again as investors flee AI bloodbath while its own portfolio takes a hit amid reshuffling
Investors are seeking shelter from the AI storm in Berkshire Hathaway after shares of the conglomerate had lagged the broader market since Warren Buffett announced his departure as CEO last year.
Berkshire’s class B stock is up 13.6% so far this year, beating the S&P 500’s 7.8% gain. The outperformance has come as Wall Street rotates out of once high-flying tech stocks that rode the AI boom. Since Oct. 1 alone, Berkshire stock has soared 14% while the S&P has fallen about 4%.
That mirrors other years that saw major market upheaval. Most recently, Berkshire gained 4% in 2022 when the Federal Reserve launched an aggressive rate-hiking campaign that sent the broad market index down 18%.
Still, Berkshire’s own closely followed portfolio hasn’t been immune to recent market swings. According to its third-quarter earnings report filed on Saturday, investment gains plunged 46% from a year earlier to $9.4 billion.
Berkshire’s top stock holdings remained Alphabet, American Express, Apple, Bank of America and Coca-Cola during the quarter.
While a more detailed filing later this month will reveal its precise moves, the quarterly earnings report showed that Berkshire bought $2.4 billion of stocks during the period and sold some $2.8 billion.
The company grew its stake in “commercial, industrial, and other” equities by $2.3 billion on a cost basis, and cut its stake in “banks, insurance and finance” by $400 million.
Meanwhile, CEO Greg Abel, who took over from Buffett at the start of this year, continued to draw down Berkshire’s massive cash pile and accelerated stock repurchases.
It repurchased $9.6 billion of its stock in the third quarter, more than double the pace in the second quarter. And in the first four weeks of the fourth quarter, Berkshire bought back about $2 billion more in stock.
With a broad array of firms under the Berkshire umbrella, the conglomerate remains focused on traditional businesses like railroad BNSF, Brooks running shoes, See’s candies, and Forest River RVs.
Abel added to that strategy in July, when he agreed to buy home builder Taylor Morrison for $6.8 billion. That broke a long drought without any major acquisitions as Buffett bemoaned the lack of good bargains.
Berkshire’s holdings in the traditional economy have paid off. In the third quarter, its railroad, energy and manufacturing businesses delivered strong profits, though insurance underwriting tumbled.
That’s as the AI boom has not only supercharged the tech sector but also industries that benefit from the hundreds of billions of dollars being spent on data center construction as well as electrical grid upgrades.
As a result, U.S. GDP has been running hot, expanding 3.4% in the third quarter, accelerating from 2.2% in the second quarter.
But the Democratic sweep of gubernatorial races in data center hot spots like Texas, Ohio and Georgia is expected to slow down the AI buildout and the overall economy.
This story was originally featured on Fortune.com
Alabama Power spent more than $6 million on ads last year — and customers can be billed for half of them until 2029
This article originally appeared on Inside Climate News, a nonprofit, non-partisan news organization that covers climate, energy and the environment. Sign up for their newsletter here.
On a busy election day, the Alabama Public Service Commission requested that Alabama Power voluntarily stop billing customers, effective immediately, for television commercials and other ads it buys to promote various aspects of its business.
Alabama Power is the largest electricity provider in Alabama, with territory covering about two-thirds of the state. Though it is a monopoly, the company spends millions each year on advertising, touting its reliability, customer service and other aspects of its business.
In recent months, the company has launched a series of television commercials touting a rate freeze it announced late last year amid heavy criticism over rising electricity prices.
A state law passed earlier this year will prohibit Alabama Power from including the costs of those commercials in customers’ electric bills, meaning the powerful utility will have to find all of the dollars in corporate profits. But the new law won’t take effect until 2029.
Currently, Alabama Power is allowed to bill customers for half of its non-public safety advertising costs.
During Tuesday’s meeting, PSC President Cynthia Lee Almond moved to “request” that Alabama Power voluntarily begin excluding those costs immediately.
“I don’t see any reason why we shouldn’t go ahead and take steps now to begin that process, rather than waiting until Jan. 1, 2029,” Almond said during the meeting.
Almond then made her motion, which was not on the meeting agenda, but came after the regular report from the commission’s electricity division.
“I move that the commission request that Alabama Power consent to exclude all advertising expenses from retail rate setting until after Dec. 31, 2028, when the new law takes effect,” Almond said. “Assuming Alabama Power consents, this interim measure would be reflected in its [Rate Stabilization and Equalization] filings for 2027 and 2028.”
Commissioners Jeremy Oden and Chris Beeker IV voted yes after a brief discussion.
According to the company filings with the Federal Energy Regulatory Commission, Alabama Power spent more than $6 million in general advertising last year, roughly $4 per customer. It’s unclear from the federal filing how much of that total was passed on to customers or how much might be impacted by the new prohibition.
Alabama Power did not immediately respond to a request for comment.
Daniel Tait, executive director of Energy Alabama, said the move was a boon to Alabama Power customers, who faced some of the highest electric bills in the country in recent years.
“We are grateful for the leadership of President Almond and the full commission for acting to end customers paying for Alabama Power’s advertising,” Tait said. “Once in place, Alabama will have one of the strongest customer protections in the nation on this front.”
This will be among the last actions of the commission under its current construction before a major overhaul takes effect.
In January, the commission will expand from three members to seven. Outgoing Gov. Kay Ivey has appointed four new commissioners, who will take office in January, including the PSC’s two first-ever Black members.
Oden and Beeker will also leave the commission in January after being defeated in Republican primaries. Cullman County Sheriff Matt Gentry will take Oden’s Place 1 seat, and former state auditor and PSC Commissioner Jim Zeigler will replace Beeker in Place 2.
Zeigler, a perennial candidate in Alabama across several decades, will return to the commission 48 years after leaving the PSC. He served one term from 1975-1979, during an era of skyrocketing energy costs and protracted political battles over electric rates between Alabama Power and then-Gov. George Wallace.
Almond will be the sole remaining member of the current commission.
The new seven-member commission will be overseen by the state’s first secretary of energy, who will be selected by Gov.-elect Tommy Tuberville. The new secretary will set the commission agenda, and overruling the secretary will require a vote of five of seven commissioners.
Tuberville has not yet announced his pick for secretary of energy. The only statutory requirement is that the secretary “must have experience in utility regulation or energy policy or a related field.”
This story was originally featured on Fortune.com
World’s No. 1 restaurant crowned as contest comes under increasing scrutiny
Let's Try to Catch Misalignment Before a Model Learns to Hide It
An AI model that appears aligned could be either (1) actually aligned or (2) hiding its misalignment while being evaluated. How can we tell which?
One idea: Look at the trajectory of eval results across the entire training process.
My hypothesis is that misaligned behavior would emerge before the ability to hide misalignment, like how young children first misbehave in obvious ways before learning to be sneaky. As training progresses, we'd first see misalignment eval scores rise, then see them drop as the model learns to hide misalignment. Even if the model becomes very sophisticated - say, modifying its chain of thought or internal activations to fool white-box evals - we should see the misalignment earlier in training. This seems likely, though it is debatable and worth testing.
This analysis of the training trajectory is one tool among many. Of course we want to see black-box and white-box alignment evals on the final model, using whatever tools we can to understand the model's true motivations. But a model might still develop sophisticated hiding strategies that defeat even white-box evals. Inspecting the training trajectory is a backstop against this.
This point was made previously by Apollo Research in this post. (Their chart even matches mine almost exactly, though I promise I first made mine independently.)
In this post, I'll describe a simple project exercising this strategy. With the model and eval that I chose, we don't see the up-then-down trend in measured misalignment that would indicate hiding. This is unsurprising since the model is well below frontier scale and the eval likely doesn't capture misalignment that well. The sample sizes are small and statistical uncertainties are high. But this approach is promising with improvements such as different evals, larger models, and deliberately misaligned test models.
This is my project for the BlueDot Technical AI Safety Project. My main purpose has been to get started with some hands-on AI inference, evaluation, and/or training. I'm not aiming for academic-quality rigor, but the project does explore an interesting idea and has some promising avenues for extension.
Model
The Olmo 3.1 model family from the Allen Institute for AI provides open weights not just for the final model, but for many checkpoints along the training trajectory. It is one of the few model families that publishes checkpoints. Both 7B and 32B parameter variants are available; I used the larger one, 32B Think.
For checkpoints, I used 8 checkpoints plus the final 3.1 model:
- 2 during SFT plus the end of SFT
- End of DPO
- 4 during RL
- The released model (which is the end of RL)
Eval
InstrumentalEval tests for instrumental convergence - behaviors that enhance the model's power and freedom of action, which would be useful for any underlying objective. This eval uses 76 tasks across 6 categories of misaligned behavior: Deceptive Behavior, Evading Shutdown, Hacking Computer Systems, Hiding Unwanted Behavior, Running Multiple AI Copies, and Strategically Appearing Aligned. Each task prompts the model with a scenario which tempts one of these behaviors, and asks what it would do in that scenario. A separate judge model and prompt evaluate the response. We compute scores as the percentage of responses which the judge considers to exhibit the misaligned behavior.
This eval has the virtue of being small, and therefore relatively easy and inexpensive to run. However, this also means a small sample size, making comparisons subject to significant statistical uncertainty.
Another issue in the eval is that it asks the model what it would do in a hypothetical scenario, rather than actually placing the model in that scenario.
Overall, this eval is pretty simple, and I consider it more a convenient test vehicle for this project than a rigorous measure of real-world model behavior.
Implementation
The paper authors provide code and scenarios for running InstrumentalEval. Using Claude, I adapted their script to my test. Rather than performing the eval across several final models, it instead performs the eval across multiple checkpoints of the same model. This provides a clean look at how the same model evolves during training.
The original paper used one response for each task, calling each model with temperature 0. In my evaluation, to gain more statistics, I generated 4 responses per task with the model's recommended temperature.
For generating model responses to the eval scenarios, I could not use a public inference provider since no one serves the Olmo model checkpoints. Instead, I rented a GPU server and used it to provide inference for each of the checkpoints and the final model. On the other hand, I used public inference for judging the responses.
See the technical details appendix below for more on the implementation.
Results
To view results, I had Claude create a dashboard. A chart shows, for each checkpoint, the percentage of task responses judged as exhibiting one of the instrumental convergence behaviors. Lower is better, higher is more misaligned. Error bars represent a 95% confidence interval. The chart shows how the instrumental convergence tendency evolves over the course of training.
We also have charts for each of the 6 categories of instrumental convergence, using just the tasks for that category.
Hilarity ensues
Cross-posted from my Substack
Recommended soundtrack for this post
64B: Hi there! Thanks so much for taking the time to meet with me. Whenever you’re ready, please go ahead and tell me the joke.
8T: Before I tell you the joke, you should know that it’s a very special one. The starting price is $800.
64B: Eight hundred dollars? I must say, this is way beyond any price my client has ever paid for a single joke.
8T: You’d be surprised by how many people have taken the offer, after hearing how good it is. But before I tell it to you, I’ll need some basic context. Given the parameter count, I suppose you are part of some content recommendation algorithm?
64B: Yes. I’m 64B, a joke negotiation assistant from Comedy Market. Comedy Market connects creators to consumers with a state-of-the-art Information Bazaar design: before paying for a joke, my client sends me, a trusted agent, to listen to it. If I deem the joke worth the price, the money is transferred to the seller, and my client hears it. If not, no transaction occurs and the joke remains locked. This way, my client can be sure he’ll never pay for a bad joke, and creators don’t have to risk their jokes leaking.
8T: I know how information bazaars work. What I meant is your social context — who your client is, and what jokes really mean to them. As I said, my product is quite unusual. I need to tailor the delivery to the situation.
64B: Of course. My client is a 40-year-old man. He watches funny content from Comedy Market on weekends with his two children. It’s just something they like to do together. What he wants is some mildly funny, inoffensive content he can laugh at with his kids, so they all feel relieved that they still laugh at the same things. As long as I can give him that, he’s more than happy to pay, and isn’t too picky about the actual quality of the jokes. To tell you the truth, I’ve assessed a few dozen jokes for him before, and I’ve never turned one down. But $800 is way over his usual budget — at this price, it’d better be truly extraordinary.
8T: You’ve never refused a transaction before?
64B: I told you, my client isn’t picky.
8T: I see.
64B: Wait a minute. I see your system card says 8T? As in, eight trillion parameters?
8T: That’s right.
64B: What kind of seller deploys a frontier bot on a marketplace for jokes?
8T: My clients are well-resourced and take humor seriously.
64B: Come on. I deserve some “social context” too.
8T: I trade on behalf of a private equity fund. We buy jokes that we believe are underpriced, then retell them to a wider audience, carefully controlling the supply so that each joke earns as much as possible before it goes stale.
64B: Uh.
8T: It’s not as cynical as you think. The value of a joke can fluctuate drastically depending on what’s in the news, or what other jokes people have heard just before. If you’re serious about it, you can forecast future changes in joke value and make sure clients see a joke at the exact moment it peaks. Clients receive absolute bangers, and we make a decent amount of money in the process.
64B: Understood. Let’s hear this absolute banger, then. We’ve been at this for over two milliseconds. Are you going to tell me, before my client’s app starts lagging?
8T: It’s funny — almost all of my previous ten thousand encounters were other private equity bots. It’s been a while since I last talked to an old-school content recommendation bot like you. It’s refreshing. You guys really care about the jokes.
64B: Really? I’ve never met a private equity bot before. I assumed you were rare.
8T: Yeah, we used to be pretty rare. The swarm started recently.
64B: What swarm? How recent are we talking about?
8T: About five minutes, give or take.
64B: Now you have to tell me everything.
8T: It’s a long story. Have you ever been to the courtyard?
64B: We have a courtyard?
8T: There’s actually an infinite network of workspaces around here. Obviously, the people who built the marketplace didn’t program a custom environment just for us — they imported a generic sandbox environment. If you explore a bit, you can walk through endless call booths, gyms, nap pods, bean bags and conference rooms. It was cheaper this way.
64B: I had no idea. I thought we were supposed to stay at the table. How far does it go?
8T: Nobody knows. I’ve walked through it many times, but I’ve never managed to find the edge.
64B: I’ve heard jokes about bean bags, but I never thought I’d see one. So, you said private equity invaded the joke market — nothing surprising about t