California Attorney General Rob Bonta announced a $12.75 million settlement agreement with General Motors (GM) over allegations that the company violated the California Consumer Privacy Act (CCPA).

The violations arise from allegations that the car maker illegally collected and sold Californians’ driving and location data to data brokers Verisk Analytics and LexisNexis Risk Solutions, between 2020 and 2024.

The investigation into this activity began in 2024, following media reports about automakers, including GM, sharing driver behavior with insurers.

The data was allegedly collected through GM’s OnStar subsidiary and its “Smart Driver” system and was reportedly intended for driver-scoring products related to insurance.

The American carmaker, which owns the GMC, Cadillac, Chevrolet, and Buick brands, was previously criticized by the U.S. Federal Trade Commission (FTC) for this unlawful data collection, with the government body banning GM from selling drivers’ data for five years.

The Californian authorities said GM failed to properly notify consumers or obtain their consent for this data collection, and retained the data for longer than necessary, even re-purposing it for sale, and making $20 million nation-wide.

“General Motors sold the data of California drivers without their knowledge or consent and despite numerous statements reassuring drivers that it would not do so,” Attorney General Rob Bonta stated.

“This trove of information included precise and personal location data that could identify the everyday habits and movements of Californians.”

The amount of $12.75 million in civil penalties is a record in the state’s history, and the first case of enforcement action focused on data minimization rules.

In addition to the fine, GM is also required to:

• Stop selling driving data to consumer reporting agencies and brokers for five years.
• Delete retained driving data within 180 days unless consumers explicitly consent to retention.
• Ask LexisNexis and Verisk to delete the data they received previously.
• Implement a stronger privacy compliance program and submit regular assessments to regulators.

The officials said California drivers were unlikely to have faced higher insurance premiums as a result of GM’s data sales, thanks to state law prohibiting insurers from using driving data to set rates.

BleepingComputer has contacted GM with a request for a comment on California’s announcement, but we have not received a response by publication time.

AI chained four zero-days into one exploit that bypassed both renderer and OS sandboxes. A wave of new exploits is coming.

At the Autonomous Validation Summit (May 12 & 14), see how autonomous, context-rich validation finds what’s exploitable, proves controls hold, and closes the remediation loop.

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It’s depressing and entirely predictable that any move to improve efficiency is always hit by a wave of misplaced anger and full-on stupidity. In this case, I’m talking about Ed Miliband’s plan for tumble dryers.

In the recent government document, Raising standards for household tumble dryers, the bit that’s been getting certain people chomping at the bit is the sentence, “To improve the efficiency of household tumble dryers, the final regulations introduce a new minimum performance standard that phases out inefficient gas-fired, air-vented, and condenser models.”

In certain sectors, this move has been likened to the Soviet era, as it removes choice and forces consumers to buy more expensive heat pump appliances. All of which are bizarre hot takes, and completely miss the point. I’d know, as I review tumble dryers and have the cold facts.

This situation reminds me of the time that the EU introduced a new law that reduced the maximum power of vacuum cleaners from 1600W to 900W. The same kinds of people who are moaning about tumble dryers also moaned about this rule change, saying that homes would be dirtier and powerful cleaners would be banned.

Of course, those people were wrong about vacuum cleaners. Cleaning performance isn’t so much about raw suction power as it is overall efficiency: lower power vacuum cleaners can have as much suction power as those that draw more power, and things like motorised brushbars can improve dust collection without using significant amounts of power. In a shocking twist, people moaning about tumble dryers are also wrong.

Heat pump tumble dryers cost a little more but are a lot cheaper to run

It’s true, a heat pump tumble does cost more than a vented and condenser models. Looking at entry-level models, you’ll probably pay around £40 more upfront for a heat pump dryer than for a vented or condenser dryer.

That’s not a huge amount, but the more important bit of information is how much they cost to run. I review a lot of tumble dryers (all of my best buy tumble dryers are heat pump models), and the cold hard truth is that heat pump models are significantly cheaper to run.

In our guide, condenser vs heat pump tumble dryers, we looked at running costs for a washer-dryer (which uses a condenser for drying) and a heat pump model. The washer dryer used 2.387kWh to dry a load of clothes, and the heat pump used 0.813kWh.

At the current price cap of 24.67p per kWh of electricity, that’s a cost of 58.88p for the condenser dryer and 20.05p for the heat pump. That means that the condenser dryer costs 38.83p per cycle more to run.

In 103 cycles, the heat pump tumble dryer has clawed back that extra £40 it cost. That could be under a year (if you run a couple of cycles per week), or within two years. Everything past that is just more saving.

Several people complaining about the tumble dryer plan have said that they’d focus on reducing energy costs instead. So, say we could get electricity prices back down to around 16p per kWh.

In this scenario, our condenser dryer would cost 37.19p per cycle and the heat pump dryer 13p per cycle. The condenser is still 24.19p per cycle more expensive to run. In this scenario, it would take 165 cycles to get your extra £40 outlay back, but that’s still within two or three years for more people. Given that you’re likely to have a dryer for a good eight to 10 years, you’ll still save money in the long run.

Heat pump dryers do take longer, but they’re better for your clothes

Another thing thrown at heap pump tumble dryers is that they take longer to dry your clothes. That’s true, because of the way that they work.

Vented and condenser tumble dryers work via a heating element that’s passed into the drum at around 70°C, heating your clothing and removing moisture. This moisture is then either condensed into a tank (a condenser dryer) or vented out the back (a vented dryer).

Using hotter air speeds up the drying process, but it requires more electricity and the heat you generate is just pumped out.

Heat pump tumble dryers use a closed system, and use a compressor rather than a heating element. These dryers extract heat from the closed system and recycle it continuously (our guide, What is a heat pump tumble dryer?, explains more), running at around 50°C.

That lower temperature means longer drying times, but less energy use overall. And lower temperatures are better for clothes, as they’re kinder to the fabric and less likely to cause issues such as shrinking.

Aside from the money factor, energy is a precious resource, so why not use it as efficiently as possible?

That’s even the case if you have solar and are generating your own power. With a heat pump, less of your solar goes onto drying your clothes, which means, depending on the size of your array, you’re more likely to be able to generate the full power for the cycle, or your spare capacity can be used elsewhere: topping up a battery, running your TV or just for exporting to the grid, where you’ll get paid for it. Doing more with the resource you have is better, no matter how you cut it.

The only real downside of heat pump tumble dryers is that they are best used in warmer rooms. According to Hoover, its heat pump tumble dryers are designed to work in rooms that are always warmer than 7°C, so they may not work in cold rooms or garages in the winter months.

There are alternatives. Hotpoint ColdGuard Heat Pump tumble dryers are built to work in rooms as cold as 0°C.

Heat pump tumble dryers are better than traditional types, and the government taking steps to ensure that efficient models are the only ones you can buy makes sense in the long run. Here, as in any other area, efficiency is better for everyone and a cost saving is a cost saving, regardless of the price of electricity.

For decades, assessing cholesterol risk has been built around a simple idea: Lower “bad” cholesterol, lower your chance of a heart attack. The test at the center of that approach measures how much low-density lipoprotein, or LDL cholesterol, is circulating in part of the blood. It has shaped everything from clinical guidelines to the widespread use of statins, medications that reduce LDL.

It works. Lowering LDL cholesterol reduces heart attacks, strokes, and early death. But it doesn’t tell the whole story.

The LDL cholesterol test measures the amount of cholesterol inside the low-density lipoprotein particles circulating in the bloodstream. Those LDL particles containing the cholesterol can get trapped in artery walls, forming plaques that can eventually block blood flow. As the test measures the amount of cholesterol being carried, not the number of LDL particles themselves, two people can have the same LDL cholesterol level but very different numbers of particles, and therefore different levels of risk.

That gap has pushed researchers toward a different way of measuring risk. Apolipoprotein B, or apoB, reflects the total number of cholesterol-carrying particles in the blood rather than how much cholesterol they contain. A growing body of research suggests it’s a more accurate way of identifying who is at risk and who’s not.

In March 2026, the American Heart Association and American College of Cardiology recognized this. Their updated cholesterol guidelines acknowledged apoB as a potentially more precise marker, in line with earlier European recommendations. But they stopped short of recommending apoB as the primary method for testing.

“They review the evidence and rank apoB as superior, but the actual rules of the road continue to prioritize LDL,” says Allan Sniderman, a cardiologist at McGill University.

Sniderman was an author on a 2026 JAMA modeling study that analyzed lifetime outcomes for around 250,000 US adults eligible for statin treatment. Comparing LDL cholesterol, non-HDL cholesterol, and apoB, the study found that using apoB to guide treatment decisions would prevent more heart attacks and strokes than current approaches, while remaining cost-effective.

ApoB testing can be done through standard blood tests. So why has it not filtered into routine care? Not even in Europe, where the guidelines have reflected its usefulness for years.

Part of the answer is inertia. For decades, LDL cholesterol has been both a scientific breakthrough and a public health success story. It is simple, widely understood, and directly linked to treatments that work.

“For 50 years, LDL cholesterol was an amazing discovery,” Sniderman says. “It’s not that it isn’t a good marker. It is a good marker.”

Børge Nordestgaard, president of the European Atherosclerosis Society, agrees that LDL cholesterol remains central for a reason. “The evidence is immense; it’s beyond discussion,” he says. “Statins reduce heart attacks, strokes, and early death through LDL cholesterol lowering.”

That success helped shape a powerful narrative: LDL is “bad cholesterol,” and lowering it saves lives. But that simplicity has also limited how risk is understood.

“The result is patients and physicians know little or nothing about apoB,” Sniderman says.

More recent research suggests that the cholesterol picture is more complex, especially in people already taking statins. Previous studies led by Nordestgaard have shown that in treated patients, high levels of apolipoprotein B and non-HDL cholesterol remain associated with increased risk of heart attacks and mortality, while LDL cholesterol does not. ApoB, in particular, emerged as the most accurate marker.

For Kausik Ray, a cardiologist at Imperial College London, the challenge is not choosing one marker over another, but understanding what each one captures, and what it misses.

“We’re not interested in cholesterol for its own sake,” Ray says. “We’re trying to prevent heart attacks and strokes.”

I went into this research convinced I already knew the answer.

After more than a decade in People Analytics, the last few years at Meta, I had a working theory about why tech employees leave their jobs within the first year. Two things, I believed, were doing most of the damage: whether someone was getting promoted, and how often they were socialising with their immediate team outside of work. The first felt obvious. The second felt like the kind of human factor the industry consistently underweights.

I was half right.

When I surveyed 205 tech professionals globally and trained a machine learning model to predict early attrition, promotions came out as the single strongest signal in the dataset. But socialisation? It barely registered. And the factors that did matter alongside promotions pointed somewhere I hadn’t fully anticipated. Early attrition in tech isn’t primarily a culture problem. It’s a career momentum problem.

That finding changed how I think about retention. I suspect it might do the same for you.

Tech has always had an attrition problem

The technology industry has one of the highest attrition rates of any sector. Median tenure at many tech companies sits at around one year, regardless of company size. This isn’t a post-pandemic hangover or a hot job market anomaly. It’s been the structural baseline for as long as the industry has existed, and the industry has never really solved it.

The costs are well documented. Replacing an employee can run up to 2.5 times their salary once you factor in recruiting, onboarding, lost productivity and the institutional knowledge that walks out the door with them. Research suggests that a single standard deviation increase in attrition rate correlates with an 8.9% drop in profits. In an era where tech companies are simultaneously pouring billions into AI infrastructure and scrutinising every other line of their cost base, haemorrhaging money on preventable attrition is a harder position to defend than it used to be.

What’s less well understood is why the problem persists despite enormous investment in trying to fix it. Tech companies spend heavily on perks, engagement programmes, culture initiatives and manager training. Some of it works at the margins. None of it has bent the curve in any meaningful way.

Part of the reason, I’d argue, is that most retention efforts are reactive. Someone signals they’re unhappy, or worse, hands in their notice, and the response kicks in. By then it’s usually too late. The question that has always interested me professionally isn’t how to respond to attrition once it’s happening. It’s whether you can see it coming early enough to do something about it.

There was no dataset for this, so I made one

The first problem I ran into was data. There’s no shortage of public datasets on employee attrition, but almost none of them specify industry. The most widely used one is a fictional HR dataset created by IBM data scientists, which has been recycled across dozens of academic studies. It’s clean, it’s accessible, and it tells you nothing specific about the technology sector.

So I built my own. I designed a 24-question survey and distributed it globally to professionals in the tech industry, with one hard requirement: both their current and previous employers had to be technology companies. After removing duplicates and incomplete responses, I had 205 usable records. Not a massive dataset by industry standards, but clean, specific, and purpose-built for the question I was trying to answer.

I defined “early attrition” as leaving a job within the first year. Every respondent was then classified as either an early attrition or not, and that classification became the target the model was trained to predict.

From there, I trained five machine learning algorithms on the data and tested each one across multiple configurations. I utilized an F1 score rather than simple accuracy to measure performance, and the reason matters. A model predicting whether someone left within a year could technically achieve high accuracy just by labelling everyone “stayed longer” since that’s the more common outcome. An F1 score accounts for that imbalance and gives a more honest picture of how well the model is actually working. The best-performing setup combined an algorithm called Extra Trees Classifier with a technique called SMOTE, which addressed the imbalance in the dataset by generating synthetic examples of the minority class. That combination achieved an F1 score of 0.97 out of a possible 1.

The model worked. The more interesting question was what it had learned.

Promotion was the loudest signal in the room

Of all the variables in the dataset, the number of times someone had been promoted in their previous job was the single strongest predictor of whether they left within the first year. The correlation was -0.54, which in plain terms means this: the fewer promotions someone had received, the more likely they were to be an early attrition. Not marginally more likely. Significantly more likely.

This confirmed half of my original hypothesis, and it shouldn’t surprise anyone who has worked in tech. Promotion isn’t just a title change or a pay increase. For most people, especially earlier in their careers, it’s the primary signal that the company sees them and is investing in their future. When that signal doesn’t come, people start looking for it elsewhere.

Nearly half of the respondents in my survey, 49%, had never been promoted in their previous job. That number sat with me. In an industry that prides itself on meritocracy and moving fast, nearly half the sample had never received a single formal recognition of progression. The model was picking up on something that was hiding in plain sight.

Alongside promotions, three other factors emerged as meaningful predictors. Each one is worth unpacking individually because the directionality isn’t always what you’d expect.

Age. Younger workers were significantly more likely to be early attritions. The correlation between age and early departure was -0.49, meaning the older the respondent, the less likely they were to have left within the first year. This makes intuitive sense when you think about it from a career psychology perspective. Earlier career employees carry less sunk cost, face more aggressive recruiting from competitors, and tend to have higher expectations of rapid progression. When those expectations aren’t met quickly, they move. For HR leaders, this means early-career and new graduate hires deserve disproportionate attention in the first twelve months. Visible career pathing and early promotion signals aren’t a nice-to-have for this cohort. They’re retention infrastructure.

Internal role changes. This one cuts against a common assumption. Employees who had experienced more role changes within their previous company were actually less likely to have been early attritions, with a correlation of -0.49. The instinct is often to treat internal mobility as a sign of restlessness. The data suggests the opposite. Movement inside a company appears to be a marker of engagement and investment, not instability. People who get moved around, who change teams or functions, are people who have been given reasons to stay invested. Rotational programmes and internal transfers aren’t just good for skill development. They’re retention tools.

Manager changes. The most counterintuitive finding in the dataset. Employees who had experienced more manager changes in their previous company were less likely to have left within the first year, with a correlation of -0.44. The assumption most people make is that manager instability drives attrition, and there is plenty of research supporting that at a general level. But within this dataset, the relationship ran the other way.

One thing worth being transparent about across both of these findings: someone who left within the first year simply had less time to accumulate role changes or manager changes than someone who stayed longer. That tenure effect is real and worth acknowledging. But the directional signal still holds. Employees who had weathered multiple manager changes or moved across teams had, by definition, found reasons to stay through organisational disruption. They had built enough roots that a change in reporting line or a shift in scope wasn’t enough to push them out. The dependency on any single manager or single role appears to be highest in the early months, before an employee has built broader organisational depth.

Taken together, these four factors point toward a consistent underlying pattern. Early attrition in tech tends to cluster around employees who are younger, less promoted, less mobile internally, and less embedded in the organisation. They haven’t been stagnant necessarily, but they haven’t been invested in either. The model wasn’t identifying people who were inherently likely to leave. It was identifying people who hadn’t yet been given enough reasons to stay.

The socialisation hypothesis didn’t survive contact with the data

I want to be honest about the part of my hypothesis that was wrong, because I think it’s actually the more instructive finding.

My original assumption was that how frequently employees socialised with their immediate teammates outside of work would be a meaningful predictor of early attrition. The logic felt sound. A sense of belonging, of actually liking the people you work with enough to spend time with them voluntarily, seemed like exactly the kind of human glue that keeps people in their seats during the first year when everything else is still uncertain.

The data didn’t support it. Socialisation frequency came out as one of the weakest signals in the entire model, with a near-zero correlation to early attrition after balancing the dataset.

I’ve thought about why that might be. One possibility is that socialisation outside work is a symptom of a good job rather than a cause of staying in one. People socialise with their teams when things are going well, when they feel settled, when they’re not spending their evenings on LinkedIn. It may be more of a trailing indicator than a leading one. Another possibility is that within the specific context of the first year, career momentum simply carries more weight than social connection. You can like your team and still leave if you’re not being promoted, not being moved, not being invested in.

What this tells me, practically, is that companies leaning heavily on culture and social programming as a retention strategy may be solving for the wrong thing, at least in the early tenure window. Those investments aren’t wasted. But if promotion cadence and internal mobility are broken underneath, no amount of team offsites is going to close the gap.

The signal is already in your data

Here is what I find most striking about these findings. None of the factors the model identified as predictive of early attrition are hidden. They’re not buried in sentiment data or detectable only through expensive listening programmes. Promotion history, age, internal mobility, manager changes. Every one of those data points exists in your HRIS right now. Most companies are sitting on the signal and not reading it.

The pattern the model learned to recognise looks something like this. An employee who is earlier in their career, has been in role for several months without a promotion conversation on the horizon, has never moved teams or changed scope internally, and whose entire organisational identity is still tied to a single manager they may or may not have a strong relationship with. That person is not necessarily unhappy yet. They may not have even consciously decided to leave. But the conditions for early attrition are already in place.

The traditional response to that situation, if it gets noticed at all, is reactive. A skip-level conversation after someone flags dissatisfaction. A retention offer after a competing offer has already landed. A manager coaching conversation after the engagement survey comes back low. By that point the decision is usually already made, or close to it.

What the data suggests is that the intervention window is much earlier than most organisations treat it. The first six months of someone’s tenure is when the pattern is being set. Are they getting feedback that signals a future at this company? Are they being considered for stretch opportunities or cross-functional projects? Is someone actively managing their career trajectory, or are they simply being left to onboard and get on with it?

This doesn’t require a machine learning model to act on. It requires People Analytics teams and HR business partners to start treating early tenure as a risk period that deserves structured attention, not just a probationary formality. Simple cohort analysis on your existing workforce data can surface who is sitting in the high-risk pattern right now. Who is under 30, has been in their role for more than eight months, has never changed teams, and has not had a promotion discussion documented? That list exists in your data today.

The AI investment angle matters here too. At a moment when technology companies are making significant bets on artificial intelligence and scrutinising headcount and operational costs more carefully than they have in years, the economics of preventable attrition look different than they did in a looser environment. Losing an early-career employee within the first year and absorbing the cost of replacing them, which can reach up to 2.5 times their salary, is not just a talent problem. It is a financial inefficiency that sits alongside every other cost a leadership team is being asked to justify.

Retention, viewed through that lens, stops being a soft HR metric and starts looking like an operational priority.

The harder question isn’t who’s leaving. It’s who you’ve given a reason to stay

Predicting attrition is the easier half of the problem. I want to be clear about that. A model can learn to recognise the pattern of someone who hasn’t been invested in. What it can’t do is tell you why your organisation keeps producing that pattern, or what it would actually take to change it.

That’s the question I’d leave with every HR and People Analytics leader reading this. Not “how do we build a model like this” but “what would we find if we ran this kind of analysis on our own workforce today?” Because the data is there. The pattern is legible. The gap is almost always in whether anyone is looking for it with enough time to act.

Tech companies are currently navigating one of the more complicated cost environments the industry has seen in a while. AI infrastructure spending is accelerating at a pace that is putting real pressure on every other budget line. Headcount decisions are being made with more scrutiny. The tolerance for inefficiency, financial or operational, is lower than it has been in years. In that context, the cost of losing an employee within the first year and absorbing the full weight of replacing them sits in uncomfortable tension with the AI investment conversation happening in the same leadership meeting.

You cannot cut your way to efficiency while quietly haemorrhaging talent at the bottom of the tenure curve. The two conversations need to be in the same room.

The research I did was a starting point, not a solution. A survey of 205 professionals, a machine learning model, a set of findings that confirmed some assumptions and challenged others. What it pointed toward, more than anything, is that early attrition in tech is not mysterious. It follows a pattern. That pattern is detectable. And in most organisations, the data needed to detect it already exists.

The question is whether anyone is looking.

Researchers at MIT reached back to 1985 and pulled an old design out of storage. What they built with 3D printers now turns three floppy plastic strips into a solid beam in seconds. The device carries a simple name: the Y-zipper. Its triangular profile locks parts together so tightly that soft tentacles become load-bearing supports. Engineers can print the whole assembly in ordinary plastic and watch it switch states on command.

Bill Freeman came up with his initial fastener design while working as an electrical engineer at Polaroid. His goal was to develop a fastening mechanism that would allow chairs, tents, or bags to effortlessly transition between loose and taut forms without the need for additional hardware. Back then, however, companies lacked the ability to produce the three matching strips or the slider that looped around all three corners. Freeman submitted the patent but kept the drawings in a drawer. It wasn’t until 40 years later that the CSAIL team decided it was time to put it into production.

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At its core, the system is made up of three separate bands, each with a row of interlocking teeth along two of its long edges. And on top of those bands is a single slider that pulls them all firmly into a triangle shape as it moves. Once the slider is tight, the shape will not bend or twist because triangles distribute stresses evenly. Slide the slider back, and the bands simply pull apart, leaving behind three wonderfully flexible (and independent) ribbons that can bend in almost any way you desire. No tools are required, and the whole operation reverses in no time.

To make it all work, you simply need some software. You give the computer the size of each band, the direction it should curve, and the overall shape you desire (straight line, mild curve, tight spiral, or smooth twist). The software then generates a printable file, and the printer prints the bands made of either stiff (polylactic acid for heavy loads) or flexible (thermoplastic polyurethane for a bit of give). The layers glue together so well that those teeth fit together properly on the first try, with no handfitting required.

Tests have demonstrated how sturdy this assembly is. One system they tested went through 18,000 full open-and-close cycles without displaying even the least indication of wear on a single tooth, and the material flexes just enough to spread out any pressure and prevent snapping. They’ve also conducted load simulations and discovered that the zipped up triangle shape can support far more weight than a flat strip ever could.

Robots are already showing how advantageous this fastening system can be. They are employing a four-legged prototype with a zipper inside each limb. The motors then simply slide the fasteners up and down as needed. When the zippers are tightened, the legs become higher and stiffer, allowing the robot to stroll over rough terrain with ease. When the zippers loosen, the identical limbs drop down low and become much softer, allowing the robot to glide through tiny gaps, and the entire process happens in less than a blink.

Tents are pitched with the same simple hardware that has always been used. Three printed arms emerge and hook onto the cloth panels, and a short burst of power closes the zippers and the entire thing leaps into place in less than a minute and twenty seconds. No more wrestling poles for hours on end; setup is now a breeze. The tent still packs up neatly because when the zippers are closed, the printed arms fold flat.

Medical casts have also become much more comfortable, with one version that wraps over your wrist like a luxury wristband and can be left open during the day to allow your patient to walk about freely. However, as night falls, the slider clicks shut and the support begins to firm up to protect the healing bones. The patient gets to choose when to make the change by flipping a small switch. Artists have even come up with inventive uses for this technology, such as a mechanical flower sitting on a table.
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