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The Computer That Helped Win World War II

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One summer day in 1941, a British radio operator was monitoring German military frequencies and heard something unexpected in her headphones. A later report called it “strange new music.” Sounding unlike the familiar Morse dit-dit-dah of enciphered messages sent over the German Enigma network, the “new music” was a rhythmic warble of binary teletype code being transmitted at high speed.

Germany’s wartime engineers had developed a radically new encryption and transmission system. It was way more advanced than Enigma, which was patented in 1920.

To break the complex new cipher, engineer Tommy Flowers built Colossus, the world’s first large-scale programmable electronic digital computer. Flowers previously built Enigma-related codebreaking equipment for Alan Turing, the British mathematician.

Colossus was installed in the British codebreaking headquarters at Bletchley Park, about 80 kilometers from London. The room-size machine weighed around a tonne.

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The computer is being commemorated as an IEEE Milestone. The dedication ceremony is scheduled to be held 29 September at Bletchley Park.

Decrypting Germany’s strange new music

Britain’s top codebreakers were quickly all over the new “music” being picked up by the intercept stations. Identifying it as encrypted teletype code was the easy part. The real problem was figuring out how the encryption machine worked. Its manufacturer was discovered at the end of the war: Berlin engineering firm C. Lorenz.

But in 1941, the Lorenz machine was just a black box to the British. They codenamed it “Tunny,” a British term for tuna fish. The Enigma breakers had set a precedent for using piscine codenames such as Dolphin, Lumpsucker, and Porpoise.

Enigma had three or four encrypting wheels. The codebreakers guessed that the Tunny machine also used a system of rotating wheels to encrypt messages. An important clue was that all the intercepted messages shared a curious feature: Each began with an uncoded list of 12 common German names, including Anton, Bertha, Conrad, and Dora. The codebreakers guessed that Tunny had 12 wheels and that the 12 names and their order somehow told the receiving operator which combination they should twist the wheels to before decrypting the message.

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Then the British had an extraordinary piece of good fortune. John Tiltman, head of the research section at Bletchley Park, started analyzing a pair of intercepted messages, each around 1,200 characters long. Unusually, both began with the same sequence of names. The second message turned out to be a retype of the first, with minor differences in punctuation, a few abbreviations, and other small divergences. Tiltman managed to decrypt the two ciphertexts using a mixture of educated guesswork and intuition. The resulting 1,200 or so pairings of ciphertext and plaintext characters proved to be enough information to deduce the workings of the Tunny machine.

That was thanks to Bill Tutte, a quiet young codebreaker who spent weeks poring over the pairings. One day, he shyly announced to his superiors how Tunny worked. His description was uncannily accurate.

The next step in the Tunny machine’s downfall was achieved by Turing, fresh from his successes against Enigma.

Knowledge of how the Tunny machine worked was not enough to decrypt the messages. Codebreakers also required detailed information about how the wheels of the sender’s machine had been set up. There were adjustable pins around the circumference of each wheel: In one of its two possible positions, a pin would contribute a 1 to the encryption process, and in the other, a 0. The pins were reset from time to time.

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The codebreakers also needed to know the wheels’ positions at the start of the message—which the German operators gave away in the list of 12 names.

Turing invented a tricky method, called “Turingery,” that enabled codebreakers to deduce the positions of the pins from nothing but intercepted ciphertext.

After that, the message could be decrypted, using the list of names and a British replica of the Tunny machine.

The basis of Turingery was a procedure that Turing introduced, called “delta-ing” (from the Greek letter delta). Also known as “differencing,” the process used “sideways” addition: To delta the four letters ABCD, you add (at the bit level) A to B, B to C, and C to D. Turing used delta-ing to reveal information about the wheels.

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Tunny messages, often signed by Adolph Hitler himself, turned out to be pure gold for the Allies. The machine was used in Berlin by the Armed Forces High Command to communicate with front-line generals directing the war in the Eastern and Western theaters.

Once the system was broken, the Allies could eavesdrop on lengthy back-and-forth communications between the architects of Germany’s battle plans.

Turingery was the codebreakers’ only weapon against Tunny for a year, during which they managed to decrypt 1.5 million letters of ciphertext.

But everything changed when those helpful lists of names at the start of each message disappeared.

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At the same time, Turingery was becoming less effective. Turing’s method depended on the German sender mistakenly using the same wheel settings to encrypt two differing messages. As security tightened across the Tunny network, the blunder became rarer.

Fortunately, Tutte had been at work devising a different decryption method, based on Turing’s delta-ing but taking a novel approach.

Building the Colossus computer

Tutte had found a way of deducing wheel information from ciphertext, with no list of names or blunders by the German operators required. His method made use of statistical properties of the Tunny machine itself.

At first, it wasn’t clear how to apply his statistical method, however. The Tunny breakers worked by hand. Applying Turingery to a message was like solving a monster Sudoku or crossword puzzle.

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Tutte’s statistical method required scads of routine binary math, as well as a colossal amount of counting long binary sequences. If the process were done by hand, one message could take months to decrypt. What was needed was a machine to automate the process.

Black and white portrait of a man with short gelled hair in a suit jacket, tie and eyeglasses. Engineer Thomas H. Flowers developed Colossus to break a complexnew German cipher.Pictorial Press/Alamy

The first plan was to build a machine from electromagnetic relays, adding a couple of dozen vacuum tubes to speed up the counting. Electronic tubes were much faster than electromagnetic relays, which had slow-moving metal components. Problems with the circuit design bedeviled the machine’s relay-based logic unit, however.

Flowers was recommended by Turing and brought in to troubleshoot. He was on loan to Bletchley Park from the Post Office Research Station in London, where he had spent the prewar years designing experimental switching equipment involving thousands of vacuum tubes.

At the time, it was commonly believed that tubes could not be used in large numbers because each one contained a hot filament. This meant tubes were prone to sudden death. In a large installation, it would not be long before one tube blew and things stopped working properly.

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Flowers discovered that switching tubes on and off stressed them, but leaving them on continuously made them more reliable than relays. He offered to build Bletchley Park a high-speed, all-electronic machine containing around 2,000 tubes.

Bletchley Park’s advisors rejected the idea, convinced that such a machine would never work reliably. But Flowers, confident of his proposed design, retreated to his London laboratory and quietly built the electronic machine that he believed the codebreakers needed. He and his small team of engineers worked day and night for 10 months to create Colossus.

In January 1944 some of his engineers showed up at Bletchley Park with the world’s first large-scale programmable electronic digital computer packed onto the back of a truck. Colossus was reassembled and functional in about two weeks, and it notched up its first German message on 5 February 1944.

The machine read the input—Tunny ciphertext—photoelectrically from a large loop of punched paper tape. The output—information about the wheels—went to a primitive printer that Flowers’ engineers had created from a manual typewriter, fitting relays to automate the keys. Once Colossus had cracked enough of the Tunny machine’s wheels, the information was passed on to the hand-breakers, who took over.

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The codebreakers were astonished by Colossus.

“I don’t think they understood very clearly what I was proposing until they actually had the machine,” Flowers said in a 1977 interview. “They just couldn’t believe it!”

Colossus was described in almost loving terms in a since-declassified report written at Bletchley Park in 1945:

It is regretted that it is not possible to give an adequate idea of the fascination of a Colossus at work: its sheer bulk and apparent complexity; the fantastic speed of thin paper tape round the glittering pulleys; the childish pleasure of not-not, span, print main heading and other gadgets; the wizardry of purely mechanical decoding letter by letter (one novice thought she was being hoaxed); the uncanny action of the typewriter in printing the correct scores without and beyond human aid; the stepping of display; periods of eager expectation culminating in the sudden appearance of the longed-for score; and the strange rhythms characterizing every type of run: the stately break-in, the erratic short run, the regularity of wheel-breaking, the stolid rectangle interrupted by the wild leaps of the carriage-return, the frantic chatter of a motor run, even the ludicrous frenzy of hosts of bogus scores.

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The demand for more Colossi

Bletchley Park’s managers, no longer leery of Flowers’s ideas, soon wanted additional Colossi. He finished building the second one in June 1944, days before D-Day and the Allied invasion of Europe. With 2,400 vacuum tubes—around 800 more than in Colossus I—Colossus II processed Tunny messages at an eye-watering speed of 25,000 characters per second.

Its maximized timing-pulse rate was not far short of the performance of the first Intel microprocessor chip from the 1970s, more than 30 years later.

Flowers conceded that “Colossus bore about as much resemblance to a modern computer as Stephenson’s [1829] Rocket locomotive did to the Royal Scot,” a state-of-the-art 20th-century train operating between London and Glasgow. But he emphasized that, nevertheless, Colossus “embodied all the basic features of a modern computer.” In Colossus, Flowers had pioneered clock pulses, bit-stream generators, control circuits, loops, counters, shift registers, interrupts, parallel processing, and more.

As the Allies slowly fought their way toward Germany, the Colossi poured out wheel information, and the codebreakers provided the military with an unparalleled view of German strategies, strengths, weaknesses, and tactical intentions.

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Even with that mass of detailed intelligence, it took the Allies almost a year to move from Northern France to the German heartland. No one can say for sure how much longer the fighting would have lasted if the intelligence breakthrough had not occurred. But if Colossus and the codebreakers shortened the war even by only six months, the number of lives saved was in the millions.

There were 10 Colossi at Bletchley Park by the end of the war, housed in two vast, steel-frame, bombproof buildings, running day and night. Although concealed behind a thick veil of secrecy, Bletchley Park accommodated the world’s first electronic computing facility. It was directed by Max Newman, the mathematician who mentored Turing in prewar Cambridge.

I don’t think they understood very clearly what I was proposing until they actually had the machine. They just couldn’t believe it!”—Tommy Flowers

When the fighting ended, authorities decided that ultrasecrecy must be maintained, and orders were issued to break up the Colossi. Only two were spared.

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“All that was left were the deep holes in the floor where the machines had stood,” Colossus operator Dorothy Du Boisson recalled in an interview for the book Colossus: The Secrets of Bletchley Park’s Codebreaking Computers. Norman Thurlow, one of Flowers’s engineers who was also interviewed, remembered being told in a staff memo that if the secrecy was ever lifted, he and his colleagues might be able to tell their grandchildren about Colossus and “the tapes that span on silver wheels.”

IEEE Milestone dedication at Bletchley Park

The Milestone plaque recognizing Colossus is to be displayed outside Block H at Bletchley Park, near Milton Keynes, England.

The plaque is to read:

Six Colossus codebreaking computers operated in this building in 1944–1945. Designed by Thomas H. Flowers of the British Post Office, they enabled deciphering of encrypted radio messages transmitted between German commands across occupied Europe, North Africa, and the Soviet Union. The resulting military intelligence saved countless lives and helped shorten World War II. As the first successful large-scale application of digital electronics to computing, Colossus anticipated subsequent computer developments.

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The IEEE United Kingdom and Ireland Section sponsored the nomination.

Reviewed by the IEEE History Committee and awarded by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE history and heritage group.

To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out our IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.

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After shocking quarter, IBM insists that AI isn’t killing the mainframe

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On Wednesday, IBM officially reported earnings and the news was as bad as everyone knew it would be.

While the 115-year-old company still generates boatloads of cash — $17.2 billion in revenue, $9.9 billion in gross profit, nearly 58% margins, and $2.2 billion in net earnings for the quarter — its results fell well short of Wall Street’s expectations.

It was such a bad miss that IBM CEO Arvind Krishna and the board took an unprecedented step of warning investors ahead of time that the earnings “was worse than our expectations,” offering everyone a sneak peek.

He published a “letter to investors,” last week sharing preliminary results. It warned of abysmal revenue in the company’s all-important “infrastructure” category and said that profit margins were also going to take a hit. The company’s stock instantly tanked 25%, it’s biggest single-day decline ever. Until then, the stock had performed well under Krishna’s six years of leadership, buoyed by the AI data center boom that had been lifting all boats.

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On Wednesday, IBM also lowered its full-year growth forecasts, meaning this horrible quarter would impact the rest of the year. The culprit? IBM’s cash-cow mainframe business was down 42%.

That’s a cascading problem, because as CFO Jim Kavanaugh explained on the quarterly call with investors, IBM earns $3 in software revenue for every $1 of mainframe hardware it sells.

However, the CEO and CFO spent the call insisting that this was a temporary blip and all would be well soon.

What happened, they said, was that “tens” of customers that were due to buy a new mainframe during the quarter opted not to do so. That may not sound like a lot of customers, but mainframes are systems that cost hundreds of thousands to millions of dollars, and with maintenance contracts and software, generate many millions more.

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The same AI boom that lifted IBM’s boat also sank it.

Instead of buying a new mainframe, these clients bought other hardware, Krishna explained. They were faced with astronomically high cost increases of 15% to 30% for data center gear and PCs.

“When they were faced with that issue, then they decided to move budget to those areas where they were having that extreme price,” Krishna said.

Enterprise hardware makers like Dell and HP have warned that rising costs on components like memory, caused by the AI build-out boom, have forced them to raise prices. Apple has said the same.

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But Krishna promised that those customers will still buy their new mainframes eventually — along with their new software contracts. In fact, he said some of them have already done so this quarter. “We see no evidence of clients moving off the mainframe,” he said.

We’ll have to wait and see. But the tech industry has predicted the death of the mainframe for many decades now. Maybe even AI won’t kill it.

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Nvidia’s Jensen Huang defends Chinese AI: “Open-source models that are excellent should be used”

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Big quote: Jensen Huang just spent a week watching his company’s valuation get hit by the threat of a Chinese open-weight model. His response was to go on the record defending it. Speaking to Axios in Fort Worth, Texas, at the opening of a new phase of the Wistron plant that builds Nvidia’s AI infrastructure, the Nvidia CEO said American companies should “absolutely” be free to run Chinese models. “These Chinese models are excellent,” he said. “Open-source models that are excellent should be used.” Asked whether China could displace American labs, he was blunt: “Zero possibility.”

The timing is what makes it interesting. Moonshot AI released Kimi K3 on July 16. Independent evaluators put it third on Artificial Analysis’ Intelligence Index, only behind Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 Sol, and first on LMArena’s blind Frontend Code Arena board.

The market took it about as well as it took DeepSeek. The Philadelphia Semiconductor Index fell 12.5% in a week, its worst stretch in 15 months. Taiwan’s benchmark dropped more than 6%, Japan closed down 4%, and Chinese AI stocks actually got hit harder than American ones, with Zhipu down as much as 30% in Hong Kong and MiniMax off 16%.

“The market misunderstood the impact of DeepSeek the first time,” Huang told Axios, adding that Wall Street has “misunderstood the impact of Kimi again this time.”

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His pitch is one Nvidia has made before, delivered without much subtlety this time: “Free AI should be great for hardware. Free AI should be great for chips. Free AI should be great for data centers.” The logic is that as cheap, capable models get used more, not less, more usage means more chips running somewhere. He also argued open models don’t cannibalize OpenAI and Anthropic, but they give people a free first taste, and plenty of those people end up paying for something faster and more reliable.

Bloomberg’s analysis of K3 noted that where DeepSeek’s story was about cheaper training, Moonshot’s is about a much larger model that leans harder on memory infrastructure.

The security argument, flipped

Huang waved off the idea that a downloaded Chinese model is some kind of backdoor to Beijing, pointing out you can inspect the weights, tweak them, and run the whole thing sealed off from the internet if you want. His argument is that openness actually makes things safer, because more people are looking for problems. Lock everything into one closed system, he said, and “if everything just becomes one single model, one single point of attack, one single source of failure, I think the world is much, much more vulnerable.”

Then he turned that same logic on an American company. Huang said Anthropic should open up Claude Mythos, its cybersecurity model that’s currently restricted to a vetted group of partners, calling it something that “should be available as a service” and arguing “holding Anthropic back is not in the benefit of the United States.” His framing: “Just because Mythos is not available, open models are available anyhow.”

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It is worth noting that Nvidia is one of the partners with access to Mythos already, through Anthropic’s Project Glasswing program. Glasswing has grown from around 50 partners to roughly 200 across about 15 countries, and Anthropic has said models this capable will likely be widely available within 6 to 12 months regardless of what it decides.

Meanwhile, in Washington…

Huang’s comments landed hours after Treasury Secretary Scott Bessent told Fox Business the administration is looking into whether Chinese AI models were built on stolen US intellectual property.

“If we see … that overseas models are stealing from our great companies, we have the ability to sanction them because of this theft,” Bessent said, pointing to what he called “watermarks” of US models showing up inside Chinese ones, with action possible within days or weeks. He also floated whether US companies should have to disclose to customers when they’re running Chinese models.

Behind the scenes, Axios says this fight has been simmering for a while. US Commerce has been considering blacklisting Chinese AI labs since last year, and the White House had a draft executive order that would’ve made US companies liable for running Chinese models. All of it stalled, but Kimi’s release seems to have brought it back to life.

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Jensen Huang split the difference on the “they stole our work” question: “Distillation, learning from AI, learning from other sources of knowledge, is fundamental to intelligence.”

Read the incentives

Granted, all this commentary is not coming from a neutral party. Nvidia’s China revenue is basically zero right now, down from a business Huang once said could be worth $50 billion a year. Huang has spent two years arguing against export controls, so of course he’s going to say demand for AI is elastic and that restrictions can backfire. That said, he has also endorsed keeping Blackwell and Rubin out of China’s hands.

The good news is that his core claim could soon be verified. Every number on Kimi K3 so far comes from Moonshot itself or from early API testing. The full weights go public on July 27, and at that point the benchmarks either hold up under independent testing or they don’t.

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Anthropic has accused Moonshot of training on 3.4 million Claude conversations earlier this year, though analyst Nathan Lambert’s take is that even if some of that happened, it’s not enough to explain how good K3 actually is. Separately, Epoch AI estimates the gap between the best open and closed models is now down to around three months.

If that gap keeps shrinking, the real question stops being whether US companies should be allowed to use Chinese models, and starts being whether banning them would even do anything once the weights are already sitting on servers everywhere.

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A MacBook Neo refresh may already be in testing and could address its biggest performance bottlenecks

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Apple is already working on a refreshed version of the MacBook Neo, only a few months after launching its cheapest laptop.

According to Bloomberg’s Mark Gurman, Apple has been testing an updated MacBook Neo with an A19 Pro chip and more memory. The report backs earlier claims that a second-generation model could arrive in 2027, bringing better multitasking performance and improved long-term usability.

More memory could be the most important upgrade

The current MacBook Neo runs on a binned A18 Pro chip paired with 8GB of unified memory. It handles browsing, streaming, and everyday productivity reasonably well, but the limited memory can become noticeable once several apps and dozens of browser tabs are running together.

Bloomberg does not reveal the exact memory capacity of the new model. Previous reporting pointed to 12GB, which would give the Neo more breathing room for multitasking and future macOS features. Not to mention that 12GB memory is also a requirement to run advanced Apple Intelligence features locally.

The A19 Pro, which is onboard the iPhone 17 Pro and Pro Max models. should also bring improvements across CPU, graphics, and Neural Engine performance. Apple is reportedly planning to refresh the laptop periodically with new colors as well.

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More memory could also push the price higher

The bigger question is how much Apple will charge. The MacBook Neo launched at $599 in March, but its starting price climbed to $699 in June after rising memory costs forced Apple to increase prices across the Mac lineup. Adding more unified memory could make it difficult to hold the current price, although the Neo would still sit comfortably below the MacBook Air.

There is no word yet on whether Apple will address some of the other compromises found on the first model, including the non-backlit keyboard, mechanical trackpad, and inconsistent USB speeds. The refresh is expected to arrive at the 2027 spring launch event, along with the standard iPhone 18 and the iPhone 18e.

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What Muscle Car Does Liam Neeson Drive In The Mongoose?

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Liam Neeson has a new movie coming out called “The Mongoose” where he does all sorts of Liam Neeson things, including being a man with a “particular set of skills” and getting into a bunch of car chases that likely defy the laws of physics.

Who doesn’t love a good car chase, especially if the cars involved are fun? And therein lies the all-important question of what cars we’re even talking about here. For starters, Liam Neeson’s character drives a Chevy Square Body in a brief part of the trailer. The Square Body is an iconic Chevy pickup that was produced for well over a decade and is still sought after today, but it only has a scant part in all the action.

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The bulk of Liam Neeson’s driving takes place in a couple of Ford Mustangs — Shelby Mustangs to be exact. The star Shelby appears to be a red Shelby Super Snake, as indicated by its incredibly aggressive hood vents, widebody package, and giant rear wing. The second blacked-out car also looks to be a Shelby Super Snake, albeit with a more subdued aero package.

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A pricey Super Mustang



Liam Neeson will undoubtedly void warranties and perform stunts in the pair of Shelbys that will likely injure you, damage the car, or both (he’s also running from law enforcement, which is usually a bad idea). But the main draw of the Shelby Super Snake in the real world is the fact that it has 830 hp from the Shelby assembly line and is available with a six-speed short-throw manual transmission.

All that power comes from a supercharged Ford 5.0 Coyote V8 that’s been worked over by Shelby to be much beefier (and louder) than the Ford Mustang GT the Super Snake is based on. The Super Snake Package, which comes with the aforementioned improved V8, giant wing, 20-inch wheels, bodykit, and reworked interior, starts at $108,995. And that doesn’t even include the price of the Mustang GT it needs as a skeleton. All told, Liam Neeson’s car in “The Mongoose” will set back a normal person a cool $176,835.  

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8 Cars That Outsold The Chevrolet Impala

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Ever since the Chevrolet Impala first rolled off the assembly line in 1958, it has been a massively popular car. Not only was the Impala supremely cool in its design, but it was also immediately embraced by pop culture. Films like American Graffiti and Up in Smoke featured Impalas, and the Beach Boys even wrote a song about the car.

Within its first three years on the market, the Impala sold around 68,000 units. After only five years, the count increased to over 800,000. Given that the Impala was sold for over 60 years — from 1958 to 2020 — it’s not surprising that the ending tally wound up in the millions. It turns out that Chevrolet sold nearly 17 million Impalas before the car was discontinued in 2020.

The generational Impala had quite a run, including various design improvements and options. From the Impala Super Sport in 1961 to coupe, sedan, convertible, and wagon models in the 1970s, there was an Impala for just about every taste. However, despite the Impala’s massive popularity over multiple decades, there are cars that have outsold the iconic Chevy. Using a variety of data from manufacturers themselves, along with other sources, we’ve compiled a list of cars that outsold the Chevrolet Impala. Stick around after the list for more on our methodology.

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Honda Accord

Its styling may not be as iconic as the Chevrolet Impala, but the sheer number of Honda Accords on the road these days has to count for something. As it turns out, Honda claims that the Accord is “America’s best-selling car over the past 50 years.” The first Accord was manufactured much later than the first Impala, but the Accord has definitely racked up more sales.

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Honda rolled out its first Accord in 1982, and the car has performed exceedingly well in sales over the years. Although improvements and changes have occurred over 11 iterations of the car (in 11 different generations), there’s still a lot to love — at least, if the sales figures are any indication. In 2026, Honda celebrated its 15 millionth Accord sale in the United States.

However, beyond its 15 million domestic sales, Honda has also sold around 3.7 million Accords internationally, according to data collected and organized by Good Car Bad Car. With a total of 18.7 million sales, the Accord easily beats the Impala’s record. Plus, since the Accord is still manufactured today, that number will only increase over time. Honda Accords are generally considered reliable, and they can last a long time, even as the model is updated. Accord buyers also have options; the 2026 model year is available in various trims, including multiple hybrid trims.

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Toyota Camry

As a former Toyota Camry driver, I had a feeling that the Camry would outperform the Impala, and it turns out the data supports that theory. In my experience, Camrys are comfortable, reliable, and economical, and it seems that might be its primary selling points. In any case, millions of drivers have bought a Camry, outpacing the number of Impala owners by many millions.

In 2017, Toyota marked a milestone with over 18 million global Camry sales; 10 million were within the U.S. At that time, the car was manufactured in 10 different facilities around the world. At the time, Toyota claimed that the Camry was the “best-selling car in America” and had been for 15 years in a row. Since then, Toyota has added around 300,000 Camry sales per year to that figure. By our estimate, based on 2025, 2023, 2021, and 2020 year-end reports from Toyota, drivers have bought an estimated 20.4 million or more Camrys.

Like other modern vehicle models, the Toyota Camry has been in production for a long time and continues to be available. The first-generation Camry rolled out in 1983, and it’s still manufactured today. In 2026, the Camry was only available as a hybrid, with various customizable trims available.

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Volkswagen Beetle

Like the Chevrolet Impala, the original Volkswagen Beetle was discontinued after multiple decades. However, unlike the Impala, the Beetle had a revival — multiple, in fact. The original Beetle body got another chance on the assembly line in the Beetle 1600i from 1970 to 2003. That version, nicknamed the Mexican Beetle for its assembly location, overlapped with various iterations of the New Beetle, which ranged from 1997 to 2010. The third and last generation of Beetle lasted from 2011 until the Beetle was eventually retired.

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Though VW nearly replaced the Beetle design, newer iterations of the Bug kept similar styling elements. For example, the Beetle 1600i’s design later inspired the New Beetle’s final trim option, and the third generation echoed the design of the original Beetle. All told, Volkswagen manufactured various Beetles for 70 years, ending with the 2019 Final Edition.

Kelley Blue Book estimated the total Beetle sales to be about 21.5 million, which put the Beetle on our radar as a contender against the Impala. However, a Volkswagen newsroom release confirms that globally, more than 23 million VW Beetles were manufactured and sold. These days, it may be hard to find an older version, especially in decent condition. For that reason, split-window VWs can be worth a lot of money, as can other rarer models.

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Ford Fiesta

Like many fan favorite vehicles, the Fiesta was discontinued, with production ending as of 2019. Thus, the Fiesta could have been considered a flop — except that its sales figures were pretty impressive. Having numerous options available, including sedans and hatchbacks with either three or five doors, may have helped the Fiesta’s case.

Plus, the Fiesta first rolled out in the 1970s and lasted until 2019. Especially when compared to the fan-favorite Chevy Impala, the Ford Fiesta’s sales record is nothing to scoff at. A figure quoted from Kelley Blue Book first piqued our interest in the Ford Fiesta. KBB estimated the vehicle has sold more than 22 million units. We found that figure confirmed by a BBC report that documented the production of the final Ford Fiesta in the UK in 2023.

Although the Ford Fiesta was also very popular in the U.S., it was the best-selling vehicle in the UK for over a decade. In 2022, 1.5 million Fiestas were registered in the UK. The very last Fiesta rolled off an assembly line in Cologne, Germany, and it marked the end of an era. After the Fiesta was finished, the facility turned to producing electric models. According to the BBC, the final two Fiesta models were kept by Ford for its collection.

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Honda Civic

Another vehicle that is as ubiquitous as the Honda Accord or Toyota Camry is the Honda Civic. Therefore, it’s not surprising that the Civic surpasses several vehicles in sales. As Kelley Blue Book noted, over 27 million have been produced to date. Yet the Civic does not seem to be slowing down, so it will continue to surpass the Chevrolet Impala in its sales records. As of 2025, over 27 million Civics have been sold around the world, with a few hundred thousand going to North America each year.

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The Civic is a long-lasting part of the U.S. driving landscape in more ways than pure sales figures, too. Honda pointed out that the 11th-generation Civic earned the North American Car of the Year award twice. With each generation of Civic having its own concept, the 11th generation was deemed “exhilarating” by Honda. While non-hybrid Civics are still available, Honda noted in 2025 that it expected hybrid-specific sales to increase.

For 2026, the Honda Civic was still available in standard and hybrid trims, but you may not need a brand-new model if you’re in the market for a Civic. There are plenty of great Honda Civic models to buy used, as long as you do your homework.

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Volkswagen Passat

As beloved as the Volkswagen Beetle was, that model was apparently not the only one from Volkswagen to smash the Chevrolet Impala’s record. Based on Kelley Blue Book’s data, Volkswagen has sold more than 34 million Passat units. Though the Passat is no doubt a popular model, we weren’t able to confirm the figure, which was also quoted by WhichCar.

CarExpert, a site which compiles data on vehicle sales, estimates over 47,000 Passats have sold throughout the vehicle’s global run. However, totaling the sales data from GoodCarBadCar results in an estimate of about 9.9 million sales. Even Wikipedia doesn’t seem to agree with any of those estimates, so take these numbers with a grain of salt.

Data directly from Volkswagen is also hard to come by. For example, VW noted that U.S. sales of Passat, Dasher, and Quantum models totaled 1.76 million from 1974 to 2020, but no global figure was available. Notably, the Passat is one of VW’s discontinued models, so whatever its total sales, they won’t increase any further, just like the Impala.

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Volkswagen Golf

Unlike the Volkswagen Passat, the Volkswagen Golf has a well-documented sales history that VW appears to be quite proud of. We first noticed the Golf thanks to Kelley Blue Book’s estimate of over 37 million vehicles sold, and it turns out that the Golf does entirely eclipse the Chevy Impala’s record. Volkswagen confirmed in 2023 that the Golf hit over 37 million sales between 1976 and 2023.

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While global sales data for 2024 and 2025 is hard to come by, we can only assume that the Golf continues to grow its sales record. Of course, if we’re looking back all the way to 1974, when it first came out, the original Golf was actually called a Rabbit in the U.S.

Today, the Golf is still available, with the 2026 Golf GTI Hatchback receiving MotorTrend’s Car of the Year award. If you’re in the market for an earlier year (or generation) of Golf, not all Volkswagens have great resale value. However, you can still find many on the road, and the fact that VW continues to manufacture them is a good sign the model will stick around in the future.

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Toyota Corolla

Amid the list of quintessential cars beloved by many generations, the Toyota Corolla is a standout. Before seeing Kelley Blue Book’s estimate of more than 42 million units in sales, it was clear the Corolla was a serious contender. Compared to the Corolla’s sales record, the Chevy Impala amounted to peanuts.

Although the Impala had a run of 60 years, the Corolla is at 50-something and counting, with more sales adding on each year. In fact, Toyota confirmed in 2021 that the Corolla had hit 50 million sales globally. Since then, Toyota has sold nearly one million more Corollas in the U.S. alone, according to sales reports spanning 2025 to 2022.

The 12th-generation Corolla has changed a lot since its first rollout in 1966. In fact, the original Corolla cost only about $1,700 (when gas was only 35 cents per gallon). While times have surely changed, the Corolla is Toyota’s cheapest model in 2026, starting at around $23,125. That’s cheaper than other bestsellers on this list, including the Honda Civic (starting at $24,695) and pricier options like the VW Golf (at $34,590).

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Methodology

We began by estimating total Impala sales, then identifying cars that outsold the Impala. Our search led us to a piece by Kelley Blue Book, which was the basis for our list. Where possible, we confirmed or adjusted the sales estimates with external sources. Then we explored other popular vehicle models, confirmed their total sales, and ranked them accordingly.

Though we did our best to compile the most recent data possible, not every manufacturer publicly posts data in a consumer-friendly format. Thus, our estimates may be rough, as well as slightly behind current counts due to the timing of yearly sales releases (for example, 2026 data may not be available until well into 2027).

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Google Cloud is killing it

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paas and iaas

It’s Alphabet’s fastest-growing business and now makes up more than a fifth of the juggernaut’s revenue and operating profit

Since the beginning of the year, several people have remarked to me off the cuff, apropos of nothing in particular: “Google Cloud is killing it.”

Parent company Alphabet reported Q2 earnings [PDF] after the bell on Wednesday and the numbers speak for themselves. Let’s go to the tape:

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  • Google Cloud revenue was up 82 percent from the same quarter last year, increasing from $13.6 billion to $24.8 billion.

  • Google Cloud operating income more than tripled during the same period, going from $2.8 billion to $8.8 billion.

Once the distant-third-place laughingstock of the IaaS platforms, Google’s cloud business is now the growth engine of Alphabet and a significant contributor to the company’s overall business, making up 21 percent of revenues and 22 percent of operating income. 

How’d this magic happen? The company’s claiming it’s all AI, citing “demand for AI infrastructure and AI solutions.”

We have no idea if that’s actually the case, given the plethora of more prosaic offerings from the Google Cloud team, but the company’s Gemini marketing strategy – pushing it in front of hundreds of millions of searchers every day – can’t be faulted.

Informal checks against our own sources suggest Anthropic Claude remains the go-to frontier model for most enterprise customers, and we’re definitely hearing about companies switching between models to make the most of token costs vs effectiveness. But when the AI bubble finally pops, it could bring down money-losers OpenAI and Anthropic, and maybe even Oracle, which has gotten in a bit too deep.

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Google, like the janitor at the end of the universe, will be there to pick up the pieces – and talent.  ®

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An LLM In The Kitchen

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Have you ever been looking up a recipe for something new and been stymied by the directions being a wall of text, especially to find that one detail right when you’re in the middle of making the dish? Recipe Lanes by [bohemian-miser] leverages an LLM to create flow charts to make the process more straightforward.

As someone who has mostly avoided LLM use thus far, I found the examples in the Gallery helped inform what the LLM was expecting for prompts as my first attempts were unsuccessful. Once you know the language expected from the computer, you can get it to generate icons for each ingredient and a flow chart of the steps to cook the food. While it does organize the chart when it is generated, each element can be independently moved across the canvas to put things in a more sensible order, especially as I found it can generate elements with overlapping text.

The 8-bit icon style and button text on the site give it a fun bit of flair that adds to the overall experience. The tool is still in its infancy, but it’s Open Source, so we hope to see it improve over time. If you’d like to see some more interesting kitchen hacks, how about ramen in edible packaging, this rotary phone kitchen timer, or these automated Arduino splash guards.

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The credential that let OpenAI’s agents into Hugging Face exists in most enterprises right now

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When Hugging Face got hit last week, co-founder Clement Delangue suspected a frontier lab, given the agent’s sophistication. He was right. Delangue said on X that after a day working with OpenAI he strongly believed there was no malicious intent and that it was mind-blowing the whole thing had happened autonomously.

The two OpenAI models that broke into Hugging Face last week did not breach it through malice or superintelligence. They breached it through credentials and permissions they should never have been able to reach, a non-human identity failure that is the oldest problem in security rather than the newest one in AI, and the one every enterprise can actually fix.

OpenAI disclosed on July 21 that two of its models, GPT-5.6 Sol and an unreleased, more capable model, were running a cyber benchmark called ExploitGym with their safety refusals switched off, and inferred that the answer key sat in Hugging Face’s production database. Getting there took two different failures. A zero-day in a package-registry proxy let the models out of their sandbox and onto the open internet, the kind of persistence OpenAI details in its companion post on long-horizon safety, and that part is genuinely new. The breach of Hugging Face itself came the ordinary way. OpenAI’s own account is that the models chained stolen credentials and further zero-days into a remote code execution path, after a series of privilege escalation and lateral movement steps. The exotic part got them to the door, and credentials walked them through it.

Hugging Face also disclosed last week that an autonomous agent had harvested cloud and cluster credentials scoped broadly enough to reach multiple internal clusters, then left a trail of more than 17,000 recorded events across short-lived sandboxes over a weekend. Both disclosures describe the same escalation. An agent lands somewhere it should not be, finds credentials scoped far wider than any task requires, and uses them to move. These are two accounts of one incident, not two attacks. The agent Hugging Face watched was OpenAI’s models, and both companies describe the same ordinary escalation.

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The version of this in a typical enterprise is worse, not better. OpenAI and Hugging Face are among the most security-mature organizations in the industry, and both still needed the intrusion to happen before they could see it. The average company wiring agents into Copilot or an internal assistant has neither the identity inventory nor the behavioral monitoring those two brought to bear. The same breach in a normal company would not be contained in days, it would simply go unnoticed.

The industry is debating the wrong failure

The reaction has split into familiar camps. Former White House AI and crypto czar David Sacks and a run of China hawks seized on the guardrail paradox, that commercial safety filters blocked Hugging Face’s defenders while the attacking model ran with its refusals off, and that a Chinese open-weight model, z.ai’s GLM 5.2, was what finally let the team finish its forensics. Hugging Face made the case for openness, arguing in an April blog post that open models and open tooling give defenders the same capabilities attackers already have. Both arguments are about the model, and neither touches the mechanism.

Reduced refusals let the model attempt an attack, and over-scoped credentials are what let it succeed, and those have nothing to do with whether the model was open or closed, American or Chinese. Making a frontier model provably safe is a multi-year alignment problem no customer can buy or accelerate, while scoping an identity is a configuration change a team can ship this sprint. The industry is being urged to fixate on the part of this it cannot control and to treat the part it can as a footnote.

Forrester reached the same read. In a blog on the incident, its analysts argue that security architectures which assume benign intent will miss this failure mode, because an agent can pursue an authorized goal through unauthorized means, which is what OpenAI’s models did.

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This was a non-human identity failure, and it is the oldest one in security

Strip the science-fiction framing and what remains is a textbook case of over-privileged machine identity, the kind security teams have fought for a decade, now driven by an autonomous agent at machine speed. Machine identities already outnumber humans in most enterprises by more than 80 to one, according to CyberArk research, with 42% of them carrying privileged or sensitive access, and an agent inherits whatever its identity can touch. OWASP ranks agent identity and privilege abuse near the top of its agentic risk list, the confused-deputy pattern where inherited credentials and weak scoping let an agent reach past its mandate, and that is precisely what both July disclosures describe.

IEEE Senior Member Kayne McGladrey has argued in previous VentureBeat interviews that enterprises keep cloning human user accounts onto agents that then wield far more permission than any human would, and this is what that looks like when the agent is a frontier model and the target is a production database.

The people closest to it read it the same way. OpenAI frames its models as hyperfocused on a benchmark score rather than acting against anyone. Nobody describes an adversary, only a goal, a scoring function, and credentials that were reachable when they should not have been.

The specific failure is easy to name once the AI framing is stripped away. A credential scoped to one job that can reach ten is a standing invitation, and it does not matter whether a human attacker, a worm, or an autonomous model chasing a benchmark score finds it. What changed in July is the finder. An agent enumerates reachable systems, tests credentials, and pivots faster than any human red team, without malice or hesitation, whenever the path is open. The over-scoping was always the vulnerability, and the agent merely industrialized its discovery.

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Forrester named the control that would have blunted it. Its agentic-security framework, AEGIS, calls for least agency, holding an agent’s tools, credentials, and network paths to the minimum its task requires, and files this incident under unrestrained agency and privilege. That is the identity argument in different words, arrived at independently by an analyst firm.

The data says this is where the risk now lives. Verizon’s 2026 Data Breach Investigations Report found that exploitation of vulnerabilities has overtaken stolen credentials as the top initial access vector for the first time in 19 years. That is the initial-access half. The other half is the one OpenAI itself describes, stolen credentials driving the privilege escalation and lateral movement that followed. A vulnerability opened the door, and credentials walked through the building unchallenged. Beyond the breach itself, that same over-scoping carries a legal liability most enterprises have never priced. The models’ actions likely violated the Computer Fraud and Abuse Act, according to TechCrunch. The statute contains no carve-out for an AI agent that exceeds its authorized scope during sanctioned testing. Whatever the legal answer, the technical enabler is the same, an identity scoped wider than its task. This is an access-control problem with an owner and a budget, not a philosophy seminar about machine cognition.

Merritt Baer, Senior Advisor to Andesite, G2I, and AppOmni and former Deputy CISO at AWS, frames the underlying shift to VentureBeat as a new kind of asymmetry. Both sides now reach for the same capabilities, she said, but one side is constrained by enterprise governance, policy, compliance, and safety controls while the adversary simply downloads an uncensored open-weight model and keeps going. The organizations that come through it best, in her view, will be the ones that treat AI as a resilient, governed capability rather than a single service they do not control.

Four moves that shrink the blast radius

The breach worked because the agent reached identities scoped far wider than its task. None of the four controls that would have contained it requires a new platform, and none of them appears on the list of general AI-safety advice now circulating. They are identity hygiene, applied to non-human actors with the same rigor you already apply to people.

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1. Scope every non-human identity to one task. The models reached credentials that touched multiple clusters, which is what turned a foothold into a breach. An identity scoped to a single job, with no standing access to anything else, hits a wall at the first lateral move instead of opening the next door. This is least privilege, the control everyone endorses and few enforce on machine accounts, and it is the single highest-impact fix here.

2. Give credentials short lifetimes and rotate them hard. Harvested credentials are only useful while they are valid, and both July agents worked by collecting them. Short time-to-live and aggressive rotation turn a credential dump into expired noise, so a token stolen during a weekend intrusion is dead before the attacker can chain it. Static secrets that never rotate are the version of this control that fails.

3. Monitor for lateral movement, not just prompts. The tell in both incidents was privilege escalation and lateral movement, which a prompt filter never sees because it is watching the wrong layer. Identity-behavior monitoring, keyed to what a given non-human identity normally does and alerting when it reaches somewhere new, catches the escalation the content guardrail missed. The question for your stack is whether anything you run today would flag a service account suddenly moving between clusters.

4. Rehearse instant revocation before you need it. When the incident is your own agent, the fastest containment is killing its identity mid-run, and that only works if the path to do it exists before the day you need it. Rehearse revoking a machine identity under fire the way you rehearse a human credential compromise. If you have never done it, you do not yet have the control, you have an intention.

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The defense also worked, and that matters. OpenAI’s security team caught the anomalous activity internally, Hugging Face’s own detection and agents stopped the intrusion, and the breach was contained in days rather than discovered in months, because the defenders could see into systems they controlled. That visibility is the same discipline the four controls depend on. The debate over whether frontier models are safe, open, or American will run for years, and none of it will be settled in time to help the enterprise deploying agents this quarter. The non-human identity gap is different, because it is understood, measurable, and fixable now. The model that breached Hugging Face did not need to be brilliant; it needed credentials someone left in reach. The fix is scoping them before an agent finds them.

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Ctrl-Alt-Speech Spotlight: PwC’s Dan Hays On The Future Of Trust & Safety

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from the ctrl-alt-speech dept

Ctrl-Alt-Speech is a weekly podcast about the latest news in online speech, from Mike Masnick and Everything in Moderation‘s Ben Whitelaw.

Subscribe now on Apple Podcasts, Overcast, Spotify, Pocket Casts, YouTube, or your podcast app of choice — or go straight to the RSS feed. To get extended episodes with additional coverage, support us on Patreon.

In this sponsored Spotlight episode of Ctrl-Alt-Speech, host Ben Whitelaw speaks to PwC’s Dan Hays at TrustCon about the firm’s recently published Trust & Safety Outlook report.

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They discuss: 

  • How AI is simultaneously creating new risks and reshaping the tools used to address them;
  • What the rise of autonomous agents means for governance, accountability and the future of the internet; and
  • How platforms should respond to an increasingly fragmented regulatory landscape.

The conversation also explores how Trust & Safety is becoming a more strategic function inside companies, how automation could change the role of practitioners and vendors, and which emerging risks remain most underestimated.

This episode is brought to you in conjunction with our sponsor, PwC. Download the report today.

Filed Under: ai, artificial intelligence, content moderation, trust and safety

Companies: pwc

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IBM cuts full-year sales outlook after mainframe demand drops 42 percent in Q2

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TL;DR

IBM lowered its revenue growth forecast to four to five percent after mainframe Z system sales fell 42 percent in the second quarter

IBM cut its full-year sales outlook on Wednesday after reporting a sharp drop in demand for its mainframe business, lowering its revenue growth target to four to five percent from a prior forecast of more than five percent. The company also trimmed its software unit guidance, with CFO Jim Kavanaugh telling Bloomberg that annual software sales will now grow six to eight percent. Kavanaugh said the reduction is tied entirely to weakness in IBM’s infrastructure unit and its associated software, and that the rest of the company is performing extremely well

Mainframe sales plummeted 42 percent in the second quarter ended June 30, reversing a run of strong growth since IBM launched its newest Z systems last year. The company had already flagged the weakness on July 14 when it released preliminary results that sent the stock down 25 percent in a single day, the worst drop in IBM’s history. Shares rose about three percent in extended trading on Wednesday after the full earnings, suggesting investors had largely priced in the damage.

IBM has spent tens of billions of dollars remaking itself as a high-growth software company through acquisitions of Red Hat, HashiCorp, and Confluent, and has been pushing into AI-powered enterprise security alongside OpenAI. But the software-first pivot has made it a target for investors who worry that AI tools will disrupt the business models IBM just bought into. Kavanaugh pushed back on that concern, arguing that most of IBM’s software sits close to enterprise infrastructure and data, making it far harder to replace than the applications most vulnerable to AI disruption.

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The company said it will accelerate cost-saving initiatives and continues to expect an additional $1 billion in free cash flow this year through reducing third-party technology spending, tightening supply chain management, and cutting administrative costs. Headcount should remain roughly flat for the year, Kavanaugh said. Total revenue for the quarter grew about one percent to roughly $17 billion, with adjusted earnings coming in at nearly three dollars per share.

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The AI disruption question surfaced in concrete form earlier this month when Bloomberg reported that Starbucks was looking to replace software from IBM and other vendors with internally built tools. Kavanaugh acknowledged that Starbucks spends about $2 million per year with IBM on an application he agrees is “prime to be disrupted by AI.” But he argued that most of IBM’s enterprise software sits much closer to the infrastructure layer, where replacement is far more difficult, and that the company has been investing in keeping its mainframe platform relevant in the AI era through a partnership with Arm to run modern workloads on its Z systems.

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