Business

The Future of Meta Superintelligence: A 1 Year Progress Update

It’s been a little over 1 year since the disastrous Llama 4 release spurred Zuck to rebuild his entire AI org. Highlights include the shocking $14.3B Scale AI “investment” just to poach Alexandr Wang and the best people from his Safety, Evaluations, and Alignment Labs (SEAL) team, the multi-hundred million dollar (sometimes $1B+) pay packages offered to top AI researchers/engineers, and the expedited compute ramp enabled by their new “Tent” datacenter design. For more details, see our original post on MSL.
Since then, frontier AI has increasingly felt like a two horse race between OpenAI vs Anthropic. Google had a brief moment in the spotlight with Gemini 3 Pro and Nano Banana, but they’ve since faded dramatically. Despite their Windsurf acquisition, they’re far from a compelling agentic coding product, and 3.5 Flash is a benchmaxxed prop that performs far worse than GPT 5.6 and Opus 4.8 in real world scenarios (much less Fable and 5.6). 3.5 Pro is not even Opus level on coding. Microsoft has completely blown their early lead with GitHub copilot and failed to effectively leverage their access to OpenAI IP. SpaceXAI is selling $26B a year worth of GPUs to Anthropic/Google, and the Chinese labs are simply too compute poor to truly reach the frontier.
Meanwhile, MSL made their public debut this April with the launch of Muse Spark. You could argue this model represents a relative regression for Meta. Llama 3 70B and 3.1 405B were both SOTA open-source on release, whereas Muse Spark, despite also being closed source, lagged both DeepSeek v4 Pro and Kimi K2.6—open source models released around the same time—on most benchmarks.
However, evaluating Muse Spark in isolation is missing the forest for the trees. What matters for MSL is the slope, not the intercept. Rebuilding your entire team from the ground up obviously comes with some short term setbacks, and it appears Meta has finally finished paying down this debt. Thus, the interesting question is not where MSL is today, but trying to predict where they’ll be in the next 6 months. We think it’s very possible they are better than Google by then due to the team’s focus.
At the simplest level, there are three things you need to build a true frontier model: data, talent, and compute. We believe Meta is the only hyperscaler/neolab on track to be world class at all three and therefore has the best chance at catching up with Anthropic/OpenAI. We’ll explain why in full detail below, but as a teaser, here are the AI compute projections from our new Tokenomics Model.
Lastly, behind the paywall, we’ll discuss what this all means for Google—the company most people today still believe rounds out the AI big 3.
Data is the new oil (for real this time)
We’ll start with data because it’s Meta’s newest advantage and probably the most underappreciated of the three.
In 2024, Ilya famously said that “data is the fossil fuel of AI.” While this analogy correctly highlights the importance of data for training AI models, it incorrectly assumes that the amount of good data is finite. In reality, if demand is strong enough, market forces will find a way.
This time, the invisible hand created a new human data/RL environment supply chain. The three incumbements—Mercor, Surge, and Handshake—are all at $1B+ ARR, and many new entrants who are barely a year old (e.g. Fleet, Mechanize, and Afterquery) are sitting around $100M ARR.
Reinforcement learning (RL) is the most important scaling law for improving AI capabilities today. The general idea is that instead of just predicting the next token, you teach the model how to complete entire tasks (e.g. fixing a bug in a codebase). Besides the tasks themselves, RL requires you to provide an environment for the model to complete the task in, tools the model can use to interact with the environment, and verifiers to check if the model’s answer is correct or not. For more background, see our previous posts.
It is worth emphasizing that many AI insiders believe more RL environments/tasks are all we need to automate virtually all white-collar work. Here’s a quote from my roommate Sholto Douglas, a prominent Anthropic researcher, on the Dwarkesh podcast last year:
Even if algorithmic progress stalls out, and we just never figure out how to keep progress going—which I don’t think is the case, that hasn’t stalled out yet, it seems to be going great—the current suite of algorithms are sufficient to automate white collar work provided you have enough of the right kinds of data. Compared to the TAM of salaries for all of those kinds of work, it is so trivially worthwhile.
Generally, all of these data companies create new RL tasks by hiring expert contractors from the relevant industries (hence why it’s called “human data”). However, there’s another thing every data company is desperately searching for today: real recordings of white-collar work.
Screen recordings are extremely valuable for making RL tasks
Naively, you might think screen recordings are primarily useful for older training paradigms like supervised fine-tuning (SFT) which teach the AI to mimic humans. After all, the whole point of RL is that you let the AI figure out the steps itself and assign rewards based solely on outcomes.
In reality, however, screen recordings can still be extremely useful for making RL tasks as long as you’re willing to do a little extra work.
The first benefit is realism. RL data is only good if it’s 1) representative of real economically valuable work and 2) at the right difficulty level for the AI. Too easy and there’s nothing for the AI to learn, too hard and it’ll never achieve any reward.
The only way to calibrate task difficulty is to just have the AI try solving the problem a bunch of times and then iterating accordingly. It’s actually quite hard to make an RL task that’s sufficiently difficult for frontier models today, so your expert contractors normally spend their time making tasks more challenging. Mechanize, for example, only expects the software engineers they pay $400k+ per year to make 1 good task per week.
Under these constraints, if you ask the average PE analyst to come up with a new financial modeling task from scratch because you want to RL your model to be better at Excel, you often end up with a contrived task that’s optimized for difficulty over realism. This is the main problem with benchmarks like OpenAI’s GDPval and Mercor’s Apex suite (a good benchmark and a good RL env are roughly equivalent). If you read the tasks, you’ll see that most are unnaturally over-specified and don’t sound like something a human would actually ask an AI to do.
For example, here’s a GDPval task that asks the AI to make an itinerary. In the real world, the difficult part of this task would be fetching all the context from disparate sources (email, text messages, random websites, etc) to build the schedule, but GDPval ignores all of that by just giving the AI super detailed step by step instructions instead. A human would never write this 1k+ word prompt to an AI. It would’ve been easier to just make the itinerary themselves at that point!
On the other hand, if you base your task on a screen recording of someone doing their day-to-day job, then it’s guaranteed to be representative of real knowledge work by definition.
Relatedly, workflow best practices and correctness definitions can change a lot over time, so the only way to ensure your data stays realistic is to have a regular stream of real recordings. For example, a good coding RL task today would involve orchestrating subagents, and this simply didn’t exist just 7 months ago.
Screen recordings are also extremely useful for making verifiers. These days, most verifiers are rubrics. Since white-collar work is generally subjective—unlike math and coding where you can just check if the final number is correct or run a suite of integration tests—your verifier ends up being a set of rules/preferences encoded in a rubric that a human or LLM will then use to grade the AI’s output. As the tasks themselves become longer and more complex, so do their corresponding rubrics.
If you collect enough traces (typically thousands) of many people doing the ~same task in slightly different contexts, then you’ll eventually capture the entire action space for said task. This ends up being sufficient information for an LLM to more or less one shot the rubric. Of course, you still need a human to do final review and crucially assign weights to all the different criteria, but this is obviously much more efficient (and typically also higher quality) than asking experts to create the rubrics from scratch.
Recordings can be super useful for deterministic verifiers as well. Knowing the underlying state of the application after the task is completed is often step 1 of creating a deterministic verifier for knowledge work, because the whole point of the verifier is to check whether the AI successfully reached the desired end state. A sufficiently detailed recording gives you this info for free.
Meta just created a top tier RL environment startup
With this context, we can better appreciate the recent news about Meta starting to track their employees’ screens, keyboards, and mouse movements. This is quite literally some of the most valuable data in the world today! Of course, it’s also poetically apt that the Scale AI man is the one spearheading the transformation.
Whereas all the data companies are desperately trying to partner with investment banks, law firms, and advertising agencies to record their workflows, Meta is one of the few companies in the world that has a sufficiently large workforce dedicated to each of these industries in-house.
The fact that Meta is still nimble and aggressive enough to do this despite the PR hit and initial employee backlash is already quite impressive. Yes, they’ve since walked it back slightly by strengthening privacy protections and giving employees the option to pause the tracker for 30min, but we think these are very minor concessions.
Furthermore, they took their data efforts to another level in late May by announcing a new “applied AI engineering org” as part of their most recent round of layoffs/restructuring. ~3000 engineers, which includes 70% of their new grads and a significant number of seniors, will now be making RL tasks/environments full-time.
We think this is an extremely underappreciated advantage for MSL. Anthropic has been the most aggressive lab by far when it comes to buying coding data from RL environment startups, and it’s one important reason why their models are so good at coding today.
Mercor recently disclosed that they logged 2,517,000 expert hours on their platform in 2Q26, which is equivalent to ~4800 people working 40 hours a week. Meta is already in the same ballpark, and their average quality is likely higher. Additionally, they have another ~70k people to pull from if this experiment ends up being as valuable as we think.
It’s also worth briefly dispelling the myth that these 3000 Meta engineers will be doing mindless, low-level data labeling. The days of undereducated contractors from third-world countries drawing bounding boxes or classifying text as NSFW are long gone. At this point, the models are sufficiently smart such that creating a good piece of training data is a real intellectual challenge. Deeply understanding failure modes, ensuring your environment is robust to reward hacking, and scaling task creation without quality degradation are all non-trivial engineering problems.
Besides purchasing from data companies, Anthropic engineers have also been making coding tasks themselves for over a year. Frontier labs are willing to pay $5000+ for a single decent coding task. Mercor’s average pay rate recently surpassed $100/hr, and the rate for SWEs is significantly above average. The top expert contractors at all of these data companies are making over 7 figures a year.
Not only is making RL data clearly more economically valuable than some big tech jobs, it just might be more intellectually stimulating too.
Instagram ads can fund a lot of compute
Compared to OpenAI and Anthropic, Meta has a balance sheet befitting a hyperscaler, and compared to Google, Meta doesn’t have a cloud business that’s aggressively trying to rent out as much compute as possible. Add in the fact that Zuck is willing to take free cash flow negative, and Meta should be able to bring up more internal AI compute than anyone else in the world.
This is exactly what we see happening. Our new Tokenomics Model projects that Meta will have more AI compute than both OpenAI and Anthropic by the end of this year.
It is important to note that a meaningful portion of this compute will be used for recommendation systems (RecSys) and generative ads. However, even if we remain conservative and only assign specific, high-profile Meta DC sites to MSL, their training compute is still comparable to OpenAI and Anthropic through 2026 and 2027. See our recent newsletter for a more detailed breakdown on Meta’s compute strategy and our Tokenomics Model for concrete numbers.
An unprecedentedly large compute ramp led by 5 titans
Much has been written about Colossus and Elon’s ability to bring enormous amounts of compute online quickly, but what Meta’s doing today is arguably even more impressive, as our Datacenter Industry Model subscribers have known for over a year.
Meta is simultaneously building 5 1GW+ “titan” clusters. There’s Prometheus in Ohio, Hyperion in Louisiana, and 3 unnamed campuses in El Paso, Iowa, and Indiana.
Never in the history of humanity have we ever seen a full 1GW campus under construction simultaneously—the closest was AWS building 800MW in Indiana for Project Rainier—yet Meta has two of them right now! Hyperion and Iowa.
For Hyperion, Meta is building the world’s largest single buildings at 400MW each. A total of 1.5GW is under construction today: 3x 400MW monsters plus 3 more standard 100MW buildings.
In Iowa, Meta signed a 1GW lease with a leading datacenter operator (as we flagged in June 2025 in our Datacenter Model). As you can see from the satellite images below, they’ve gone from nothing to the full GW under construction in just 1 year.
At Prometheus—which is already partially operational today—Meta doesn’t quite have a full GW currently under construction, but they are fully embracing the scrappy tent DC design we called out in our previous MSL article. The Prometheus cluster also keeps expanding—from an initial ~1GW to now >3GW within two years. To understand how Meta achieves this, check out our Datacenter Model. We have building-level tracking of all five Titans, and are the first to call out any expansions with precise monthly timelines. Some of these facilities use behind-the-meter power and we track the exact type of system and flag permitting risk.
Connecting the titans: Meta’s solution to scale-across
Since Meta primarily builds and runs their own datacenters, they have more flexibility to customize their infrastructure to fit their actual needs. Prometheus is a good example of this. Instead of a single datacenter or campus, Prometheus is a constellation of 27 datacenters spread across 6 campuses. 5 of these are within 6km of each other and the 6th is 75 to 80km away from the rest.
The reason behind this design choice is that scaling to hundreds of megawatts is currently very challenging, especially regarding thermal and power management. Training the next frontier AI models requires a lot of compute—way beyond 100MW—which is why spreading the workload across multiple datacenters is the path forward to overcome current limitations.
However, with this design choice comes a new challenge: networking. The whole point of a larger cluster is to be able to train larger models in a reasonable timeframe. If the accelerators are not able to communicate efficiently, then there is no point in a gigawatt scale campus. This is where scale-across comes into play.
To address this Meta has introduced a solution called AI-Backbone (or AIBB for short). This is an evolution of their 10X Backbone, specifically designed for AI and massive cluster needs. This network architecture consists of multiple L3 Superspines (or Backend Aggregation – BAG for short) that interconnect up to 5 DSF (Disaggregated Scheduled Fabric) or 7 NSF (Non-Scheduled Fabric) scale-out regions (those can be mixed as well). The L3 Superspines are then aggregated on a single L4 Inter-BAG hub which is set to provide around 22 petabits per second of bi-directional bandwidth across the whole Prometheus cluster.
While the DSF and NSF regions fit into a single datacenter room, the L3 and L4 are distributed across all the datacenters. The L3 layer sits in a single campus, while the L4 is primarily used to interconnect the campuses together. The connection between L3 and L4 is a mix of LR optics and Dense Wave Division Multiplexing (DWDM) systems with ZR optics, depending on the length of the fiber to reach the other campus.
While this seems to be the perfect solution, this network architecture and the distance between the datacenters inherently introduce latency. Inside a DSF or a NSF region, the latency usually sits between 1 to 10µs, but the latency to reach a site that is 100km apart cannot be lower than 500µs, just due to light propagation in the fiber, which forces Meta to use asynchronous strategies for training workflows. The pretraining can be in 1 region synchronously, but the RL can be spread globally quite easily.
Overall, while Prometheus offers a solution to current limitations, the other Titans will go even further with scale-across, connecting campuses up to 2,000kms apart.
Assembling the MSL superteam
Last year, Meta became famous for offering AI researchers compensation packages that would make Patrick Mahomes jealous. After spending $14B to acquire Alexandr Wang and another $1B+ to buy out Nat Friedman and Daniel Gross’ venture fund, it was reported that Meta had poached at least 14 researchers by the end of June 2025. Most of these people were ex-OpenAI, but there were a handful from Anthropic and Google as well. Some big names include Shengjia Zhao, Trapit Bansal, Joel Pobar, and Jack Rae.
Since then, MSL has continued their recruiting frenzy. Notable research/engineering hires include
Andrew Tulloch (ex Thinking Machines cofounder)
Joshua Gross, Mark Jen, Yinghai Lu (Thinking Machines founding team)
Jason Wei, Hyung Won Chung, and Zhiqing Sun (ex OpenAI)
MSL’s not just hiring members of technical staff either. This January, they brought on Dina Powell McCormick—a well connected finance bro and former advisor to both Trump and W Bush—as President and Vice Chairman to help build out their compute fleet. In the same vein, they also poached the 3 musketeers from OpenAI’s compute team (Pete Hoeschele, Anuj Saharan, Shamez Hemani) in April, though 1 of them has already quit because of Meta’s culture issues on the infrastructure org.
Much like when a sports team suddenly starts lavishingly spending on superstars, only time will tell if this newly assembled Meta superteam can actually win a championship. However, Zuck has made his intentions clear and is pooling together all available resources to take a true shot at the frontier. As of today, you can’t say the same about any other hyperscaler. This could be good or bad for Meta’s business depending on your priors, but we believe they have the best shot at catching up to Anthropic/OpenAI.
But just to be clear, success is far from guaranteed
We’re overall bullish on the future of MSL, but it’s worth emphasizing that they are still basically at step 1. We commend them for marshaling the resources and balls necessary to take a true shot at building RSI, but now they have to do the actual work. Catching up to Anthropic is easier said than done, and at the end of the day, Meta is still a big tech company with lots of competing opinions and priorities.
As we explained in our recent piece on Meta compute, there are many ways Meta can temporarily monetize compute in the short term while still giving themselves the optionality to full send MSL if their research org can hit the right milestones. However, if they do anything that demonstrates a true weakening of resolve, like signing a long term deal to sell compute with no clawback, disbanding their new RL task creation org, or letting their top researchers walk away, then we would be materially less bullish. You could even argue that any one of these would be tantamount to a death sentence for MSL.
We briefly tested Muse Spark 1.1 before the official release and believe it’s roughly on par with Opus 4.6 or GLM 5.2 for general agentic use cases. The fact that Meta chose to price the model just under GLM 5.2 feels intentional. Some of our engineers noticed it has a bad habit of ignoring warnings instead of fixing them and doesn’t properly use the edit tool.
None of our internal token volume will be moving to Muse Spark 1.1, but that’s still to be expected at this stage. Even in the bull case, we don’t expect them to be on par with Anthropic or OpenAI until the end of this year.

Business

Report: Window dislodges on Ryanair Boeing 737, passenger nearly sucked out of plane

A Ryanair Boeing 737 was forced to make an emergency landing after a window broke and a passenger got partially sucked from the aircraft.
About 10 minutes after the flight took off, the incident occurred shortly after the departure from Greece. The window was thrown out at 13,000 feet.
As the plane decompresses, oxygen masks are seen being removed.
The BBC reported that witnesses told the local media of a man in his sixties who was left head-first hanging out the window for several minutes. Other passengers pulled him inside.
ABC reported other passengers holding someone’s foot to prevent him being sucked into the window.
After the pilot had descended to about 6,000 feet, the jet was able to land.
Ryanair has confirmed that one passenger, a Serbian national, required medical attention after the plane landed.
Airline released a press release about the incident.
After takeoff, a Ryanair flight on Friday (10th July) from Thessaloniki back to Memmingen returned to Thessaloniki when the passenger window was displaced inflight. Passengers returned to the airport after landing. A passenger in Thessaloniki requested medical attention and was given it on the spot. To minimise delays, passengers were transported to Memmingen by a new aircraft that left Thessaloniki this morning at 9:53 am local time.
Renton is the home of Boeing 737.
Boeing issued a press release saying that it was aware of the flight FR1879 incident and in touch with Ryanair.

Business

Man partially sucked out of broken Ryanair plane window during flight, fellow passenger says

Thessaloniki (Greece) — A witness said that a man nearly fell out of the window when a Ryanair plane “detached”, mid-flight, on its way to Germany. Other passengers pulled him back in, according to Greek media.
The passenger was described by authorities as a Serbian tourist on a flight between Thessaloniki, Greece, and Memmingen, Germany. He was hospitalized for friction burns, but otherwise was in good health.
Most of us were asleep and had shut our eyes. A fellow passenger reported to Radio Thessaloniki that there was an unusual noise. It sounded like a tire burst.
We immediately recognized that there was a decompression. “There were screams…for a brief moment, I thought that someone accidentally opened the door to the medical room,” said the woman. The masks fell and there was an unpleasant smell. One passenger’s head and shoulders were visible outside the window. He hadn’t removed his seatbelt.
She said that other passengers nearby helped pull the man in.
CBS News was unable to independently confirm a video that circulated on social media purportingly showing the interior of the plane following the accident. The footage showed a cracked window, oxygen masks hanging in the ceiling and no visible passengers.
CBS News reported that the European Union Aviation Safety Agency, or EASA, was aware of this incident and will support any investigation.
In a press release, the EASA stated that it was in touch with both the FAA and the engine maker to determine the state of design for the aircraft. We will continue to monitor the situation as new information becomes available and we’ll take whatever airworthiness actions are necessary in order to maintain safety.
In a press release, the FAA stated that they “stand ready” to assist local authorities as well as the NTSB with the investigation. They also confirmed that the aircraft was made by Boeing in the United States.
The FAA reported that “the Boeing 737-8 returned to Thessaloniki Airport safely on Friday, 10 July around 7:10 a.m., local time, after experiencing a damaged window.”
According to CBS News, a woman in the U.S. died after a similar incident occurred in 2018. An aviation expert said that if a passenger is not wearing their seatbelt in flight they could be “sucked” out the window due to an “incredible pressure” trying to escape the small hole.
The Greek media said that the accident occurred above North Macedonia and that a fragment of engine debris had broken the window.
Ryanair stated in a press release that the plane “returned shortly after taking off to Thessaloniki when a window on a passenger detached.” The plane landed as normal and passengers were returned to the terminal.
Irish airline said that a replacement aircraft had been made available for the remainder of the passengers who needed to be transported to Memmingen.

Business

These are America’s 10 cheapest states to live in

Kevin Warsh is the new Federal Reserve chairman and he has made it his mission to get straight to the point. He did this in his very first press conference on June 17, 2017.
He said that “persistently higher prices burden the American public.”
He left out that prices in certain states are lower than in other.
When companies are deciding where to invest, the cost of living plays a major role. Low cost of living is a great way to attract employees. This could also mean they can pay less because of the savings on wages.
CNBC includes Cost of Living in the ten competitiveness categories of our America’s Top States for Business Study, which is now in its twentieth year.
The Council for Community and Economic Research calculates an index of the prices of a wide range of products and services. Renters and homeowners are also considered in the affordability of housing. We also measure, in light of the ongoing insurance crisis, the cost for a home priced at the median price based on most recent data. Cost of Living, according to this year’s method, is worth 2% of the total score for each state.
In some states life can be very expensive, while in others it is quite affordable.
Here are the cheapest US states for 2026. Also included is a list of average prices in major metros.
Missouri
Istock
You can find some great deals in Missouri if you show me rental listings. According to ATTOM Data Solutions the average three-bedroom rent in Missouri was only $1,582. This is the 5th lowest rent in the nation as a percent of the median income. It is also about half the price you’d pay in New Jersey.
Not only is housing cheap. According to C2ER, a head of lettuce will cost 12% less in Joplin than in New York City. Insurance is one thing you can’t get a good deal on. Insurance costs are rising due to severe storms such as the tornado that struck St. Louis and surrounding areas last year, which killed four people and damaged $1.6 billion. According to Insurify, premiums for Missouri will increase by 7% in 2018. The state is already ranked 13th in the nation.
Cost of living score for 2026: 34 points out of 50 (Top States Grade: B+).
Consumer Price Index for May, Midwest Region (year-over-year), +5%
Average rent (3-bedroom home): $1,582
The average home value in Springfield is $478.702.
Energy bill for the month: $149.83
Dozen eggs (Q1 2026): $3.22
Price of a loaf (Q1 2020): $3.39
Ohio
Bloomberg
Ohio has the best business and living costs, making your money go further. The Buckeye State will be America’s top state for business in general by 2026. Cleveland is the best place to find affordable housing. The average cost of rent in Cleveland is just a bit more than a third that of Boston. The average rent in Ohio is fourth lowest nationally as a percent of the median income.
Cost of living score for 2026: 35 points out of 50 (Top States Grade: A+)
Consumer Price Index for May, Midwest Region: +5%
Average rent (3-bedroom home): $1,565
The average home value in Cleveland is $388,116
Energy bill for the month: $188.39
Dozen eggs: $4.29
Bread: 3.99 dollars
Kansas
Getty Images
Kansas is a great place to watch your money grow. A 64-ounce bottle will only cost about 10% less in Salina than in Chicago. The Sunflower State has the 3rd lowest housing costs in the nation. However, as with its Midwest neighbors the insurance rates are increasing due to the severe weather, the summer heat and the winter cold. The homeowner’s insurance premiums in Sunflower State were 10th highest nationwide last year. A 4% rise is expected this year.
Cost of living score for 2026: 36 points out of 50 (Top States Grade: A+)
Consumer Price Index for May, Midwest Region: +5%
Average rent (3-bedroom home): $1,538
Average house price in Salina: $348,000
The average monthly energy bill is $223.04
Dozen eggs: $3.87
Bread: 3.99 cents
Iowa
Bloomberg
You’ll have plenty to spare if you build a nest in Iowa. The second lowest rents in percentage terms of the median income (after Michigan) are found across the nation. You may prefer to purchase instead. Hawkeye State housing is some of the least expensive in the country. Iowa is also facing an insurance crisis six years after the Midwest was devastated by a massive derecho that caused over $11 billion worth of damage, the majority of which occurred in Eastern Iowa.
Cost of living score for 2026: 36 points out of 50 (Top States Grade: A+)
Consumer Price Index for May, Midwest Region: +5%
Average rent (3-bedroom home): $1,580
The average home value in Burlington is $331,200
The average monthly energy bill is $205.61
Dozen eggs: $3.63
Bread: 3.99 cents
Indiana
Getty Images
You’ll save money if you drive through Indiana. According to C2ER, even at the beginning of the Iran War in the first quarter this year, gas in Richmond, Indiana was only $2.82 per gallon. Kokomo’s tire balancer costs about half as much as the one in Conway.
Cost of living score for 2026: 36 points out of 50 (top states grade: A+)
Consumer Price Index for May, Midwest Region: +5%
Average rent (3-bedroom home): $1,711
Average home price (Kokomo): $293,267
Energy bill for the month: $197.80
Dozen eggs: $3.92
Bread: 3.99 cents
Wyoming
Istock
Wyoming is currently able to contain the current insurance crisis. The Cowboy State has the lowest homeowner’s insurance premiums, at just $1,929 a year. This is the 16th lowest in the nation. It is also important to note that they will not be increasing this year. Wyoming, however, has experienced some inflation elsewhere. While not excessive, food prices are high. Renting an apartment in Laramie costs about one third of the price in Arlington, Virginia.
Cost of living score for 2026: 37 points out of 50 (Top States Grade: A+)
Consumer Price Index for May, West Region: +3.5%
Average rent (3-bedroom home): $1,791
Average home price (Laramie): $449,444
The average monthly energy bill is $208.17
Dozen eggs: $3.28
Bread loaf: $4.29
South Dakota
Istock
South Dakotans enjoy some of the best housing deals in the country. According to ATTOM Data Solutions, and the U.S. Census data, South Dakotans have the 4th lowest monthly payment in the United States. A home in Pierre costs about 25% less to buy than a similar one in Miami. Renting is your preference? Rent costs in America are among the lowest. According to Insurify, homeowners insurance rates, which are currently in the middle range of premiums nationwide, will only rise by 1% over the next year.
Cost of living score for 2026: 38 points out of 50 (States with the highest grade: A).
Consumer Price Index for May, Midwest Region: +5%
Average rent (3-bedroom home): $1,785
Average Home Price (Pierre) : $474.200
Energy bill for the month: $175.72
Dozen eggs: $3.28
Bread: 3.99 $
Alabama
Getty Images
Alabama, also known as Yellowhammer State is named for its state bird, the Northern Flicker, which is a woodpecker. Bananas are also yellow and cheap in Alabama. Decatur offers 20% cheaper per pound than Orange County in California. Prices are affordable whether you rent or own your home. The 10th lowest rents in the country as a percent of the median income. Anniston’s average home price is about half that of Phoenix.
Cost of living score for 2026: 38 points out of 50 (Top States Grade: A).
Consumer Price Index for May, South Region: +3.9%
Average rent (3-bedroom home): $1,542
Average Home Price (Anniston, AL): $284.340
The average monthly energy bill is $239.21
Dozen eggs: $4.72
Bread: 3.00 dollars
North Dakota
Cavan Images / Kimberli Fredericks | Cavan | Getty Images
The International Peace Garden is the source of the nickname Peace Garden State for North Dakota. The Peace Garden State is located in Turtle Mountains along the U.S. Canada border. It was established in 1932 when relations between the U.S. This state has some of the best housing options in the country if you’re looking for a private garden. In Bismarck you can get a newly constructed four-bedroom house with enough space to grow your own garden for less than the cost of a similar home in Bozeman in Montana. Wearing a pair of comfortable slacks while you tend to your flowerbeds would be a great idea. You’ll pay nearly a third less in Asheville than you would elsewhere.
Cost of living score for 2026: 41 points out of 50 (Top States Grade: A+).
Consumer Price Index for May, Midwest Region: +5%
Average rent (3-bedroom home): $1,908
The average home value in Bismarck is $378.598
Energy bill for the month: $157.22
Dozen eggs: $3.27
Bread: 3.99 $
West Virginia will be the cheapest US state by 2026
Steve Heap
Housing costs in West Virginia are almost heavenly. In the Mountain State, nearly 81% of residents spend less than a third of their income each month on housing. This is by far the highest figure for any state. The insurance premiums in Charleston are also among the lowest of the nation. A home in Charleston costs about a fifth as much as an equivalent place in Seattle. Your grocery bill will be lower too. The price of a bag frozen sweet peas in Arlington is about 30% lower. Gas for your trip to the supermarket will be about 50% cheaper than in Los Angeles.
Cost of living score for 2026: 43 points out of 50 (Top States Grade: A+).
Consumer Price Index for May, South Region: +3.9%
Average rent (3-bedroom home): $1,726
The average home value in Charleston is $274.429
Energy bill for the month: $190.36
Dozen eggs: $3.98
Bread: 3.99 $

Business

Fidji Simo: the Health Advice From Zuckerberg She Wishes She’d Listened to

Fidji SIMO, who resigned on Thursday as OpenAI’s head of applications and shared with the world a lesson in health she had learned through hard experience.
Simo suffers from POTS, postural orthostatic Tachycardia Syndrome. She wrote Thursday on her social media page that she had lived with this chronic illness for 7 years.
She claimed that she fell ill while working at Facebook in different roles over a ten-year period.
She wrote: “Throughout the years, my doctors, family, friends and colleagues have encouraged me to take it easy. Facebook gave me the option to go on a year-long medical leave two years after my illness. It was something I did not even consider. I said “no” immediately. Zuck said at the time that I should be playing the long-game. “I wish I’d listened.”
She said: “Looking at it now, I see that many of the things which made me so successful made my decision so difficult.”
Zuckerberg didn’t immediately reply to an inquiry for comment.
Simo writes that she was able to do more by 40 than she ever imagined possible because of her ability to grab opportunities. She grew up in a small village in the south of France.
What I am learning is that endurance and grit aren’t the only qualities required for a lasting impact. She said that sometimes it is harder to listen and believe in yourself, to take care of your health today, so you can contribute much more tomorrow.
What is POTS (Polytrauma Syndrome)?
The autonomic nervous systems, which control our bodily involuntary functions such as heart rate, are affected by POTS. The symptoms are debilitating and occur when someone with POTS moves from sitting to standing.
The symptoms include headaches, chronic fatigue, palpitations of the heart, dizziness and sweating.
According to an article published by Heart, Lung and circulation in 2026, because POTS has symptoms that are similar to many other diseases, it is difficult to diagnose. It is not possible to prescribe specific medication for it. Each patient’s treatment will be unique.
Simo described it as “jarring”, to be spending her time at OpenAI, building the future she envisions while also “navigating” a disease for which there is still no cure.
Simo announced that she would be shifting from her role as CEO for Applications at the company to one of a consultant.
OpenAI CEO Sam Altman shared Simo’s post to X. He wrote: “I am very saddened by this, but also grateful for everything fidji did for openai. I’m even grateful for the friendship she has shown and for who she really is. We all hope for her to recover quickly. This is a pity.”
Simo concluded her Thursday statement by writing, “For the moment, I am focusing on recovery.” My belief that technology can solve deep human problems is stronger than ever.

Business

US-Iran escalation threatens oil supply recovery, warns IEA

International Energy Agency warns that the return of conflict between Iran and the United States could extend the energy crisis.
United Nations Agency warned on Friday that a resumption in hostilities may undermine hopes for a rapid recovery of the energy market.
Read More Stories
List of three items
List 1 of 3How the Strait of Hormuz dispute has led to the latest US-Iran fighting cycle
List 2 of 3Traffic in the Strait of Hormuz plummets after US and Iran resume their fighting
List 3 of 3The US has built a new system to pressure Iran
End of List
Sources caution that US forces are still ready to continue attacks, despite a perceived lull between US and Iranian military activity.
The IEA’s latest oil market report said that the world oil demand will fall for the first year since 2020. This is because the Middle East conflict has continued to affect production and exports.
The agency stated that a recovery was underway on the basis of the US-Iran Memorandum of Understanding signed last month, but warned against escalation.
This week’s latest fighting was caused by differing interpretations of the provisions of the MoU governing the Strait of Hormuz. Before the April conflict with US and Israeli attacks on Iran, the waterway carried about a fifth the world’s oil exports and LNG.
According to the IEA, the closure of Hormuz has reduced crude oil flow by up to 14 million barrels a day. Fuel shortages and rising prices are a major blow to the world economy.
According to the UN agency, the global oil supply increased by 4.1 millions bpd after the MoU was signed and the Strait was reopened in June. However, the supply still remained 9,4 million bpd lower than pre-war levels.
The IEA forecast a global surplus of 4.62 million bpd in 2027 based on the assumption that the strait will return to full operations, compared to an 860,000 bpd shortage in 2026.
The fighting has resumed, and shipping in the Strait is once again at a standstill.
Oil prices remained stable despite the disruption. Brent crude was trading at $76.37 per barrel early on Friday, little different from the close of Thursday, but up over $4 from one week ago.
The relative calm, according to analysts, reflects the market’s confidence in stabilizing the situation. However tightening inventory levels are expected to increase pressure on the prices of goods and services over the next few weeks.
US reports quoted US sources as saying the lull of attacks between Thursday and Friday was due to ongoing work behind the scenes in order to restore diplomacy.
CNN reported that unnamed US officials told them Washington carried out airstrikes and then paused to prevent escalation.
This claim is in line with the statements of an official from Washington who told Al Jazeera earlier that Washington remains dedicated to negotiations with Tehran, and that technical discussions for a lasting deal would continue.
CNN’s source warned, however, that the US Military was ready to launch new assaults if needed.
Regional Concerns
CNN confirmed that sources in the Middle East confirm Pakistan’s and Qatar’s efforts to get the US and Iran to return to the table for negotiations.
The dpa agency echoed this, citing sources from Islamabad who said that Iran had asked Pakistan to indicate to the US their willingness to engage in negotiations.
There were reports of discussions through different channels. One such meeting was between the Iranian Foreign Minister Abbas Araghchi, and Pakistan’s Chief Army Staff Asim Muniz that continued late Thursday evening.
On Friday, the oil producing states of the region, some of whom were also targeted this week by Iranian missile attacks, called for restraint.
Egypt and Gulf States held telephone calls Friday, urging both parties to prevent regional tensions from spreading and to avoid a larger conflict. They added their voices to those of the condemnations that had been made following Iranian attacks on Bahrain, Kuwait, and Jordan.
The Gulf Cooperation Council, as well as individual members states of the council have repeatedly called on Washington and Tehran in order to maintain diplomatic gains that were made last month under an agreement. This was despite both sides exchanging fire.
United Nations also expressed alarm. They warned that renewed conflicts could unravel diplomatic progress, and have catastrophic effects for the regional economy and global trade if the conflict escalates into a full-scale civil war.
The IEA forecast is still based on the assumption of a continuing ceasefire and that traffic in Hormuz will gradually reopen. If this assumption fails to hold true, the IEA’s forecast for a global rebalancing in oil demand and supply next year will be put under new pressure.