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World No. 1 Shin Jin-seo Beats AI KataGo 2-1, Ten Years After Lee Sedol’s Historic Match With AlphaGo

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SEOUL — Shin Jin-seo, the world’s top-ranked Go player, defeated the artificial intelligence program KataGo 2-1 in a three-game series that concluded Tuesday, delivering a symbolic human victory a decade after Lee Sedol’s landmark loss to Google DeepMind’s AlphaGo reshaped public understanding of what AI could achieve.

Shin won the deciding third game by 11.5 points as Black after 221 moves, capping a comeback that saw him rebound from an opening-game loss to sweep the final two games of the series, held at a television studio in Seoul’s Jung-gu district and broadcast live on Baduk TV.

A rematch three anniversaries in the making

The series, dubbed the “Ssen Math·Hankyung Gishin Match,” was organized by the Korea Baduk Association specifically to mark the 10th anniversary of the 2016 Google DeepMind Challenge Match, in which Lee Sedol faced AlphaGo on even terms and lost the five-game series 4-1. That earlier match, played in Seoul in March 2016, is widely credited with transforming global perceptions of artificial intelligence, with Lee’s lone victory in Game 4 remaining one of the most celebrated moments in the sport’s history.

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Unlike Lee’s even-terms match against AlphaGo a decade ago, Shin’s series against KataGo, currently regarded as the strongest existing Go AI, was played under a two-stone handicap, reflecting how dramatically AI capability in the game has advanced since 2016. Ahead of the series, Shin acknowledged the gap that remains between human and machine play at the highest level. “It is currently impossible to beat artificial intelligence in an even game, but I believe it is meaningful if I can narrow the gap,” Shin said before the match began.

How the series unfolded

Shin lost the opening game on July 17, resigning after 245 moves in a contest where his win probability had briefly exceeded 99% before a critical error in the lower-right corner allowed KataGo to seize control. Two days later, on July 19, Shin rebounded to win Game 2 by 4.5 points after a marathon contest lasting nearly five hours and 290 moves.

The series concluded Tuesday with Shin’s decisive Game 3 victory. Unlike the first two games, where KataGo opened at the star point, the AI began the final game at the upper-left 3-4 point, prompting Shin to respond with a corresponding move in the lower-right corner and establish a different overall flow than in the previous two contests. Rather than engaging in complex fighting, Shin pursued a territory-focused strategy, building solid influence along the upper and right sides of the board before consolidating a large framework extending toward the center into confirmed territory.

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Shin entered the deciding game with an estimated 99% win probability under the handicap evaluation, equivalent to roughly an 18.5-point advantage. According to AI-based win-rate analysis of the game, his winning chances never dropped below 95% at any point, making it his most convincing performance of the series. The game lasted approximately three hours and 20 minutes.

Shin’s reaction

Despite securing the series victory, Shin was measured in assessing his achievement relative to Lee Sedol’s earlier feat. “I don’t think this compares with the one victory that Lee Sedol achieved against AlphaGo 10 years ago,” Shin said following the match, a comment reflecting both the different competitive conditions, an even match for Lee versus a handicapped series for Shin, and the outsized cultural significance of Lee’s original win.

Prize money and format details

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Under the terms of the series, Shin received 150 million won, or roughly $108,000, in appearance fees at a rate of 50 million won per game, along with an additional 50 million won bonus for each of his two wins, bringing his total earnings to 250 million won. Because he secured two or more victories in the series, Shin also received a Genesis G90 luxury sedan as an additional prize.

The match conditions reflected the different capabilities of human and AI competitors: Shin operated under a standard five-hour time limit with a single 30-second byoyomi period for overtime moves, while KataGo faced no overall time limit but was required to make each individual move within 20 seconds.

A decade of change in the sport

The rematch arrives amid a broader transformation in how professional Go is played and studied. In the years since AlphaGo’s 2016 victory over Lee Sedol, AI has fundamentally altered the game at the highest levels, overturning long-held strategic principles and introducing new ones that professional players now study and attempt to replicate rather than relying primarily on their own intuition. Today, competing at the top professional level without incorporating AI-assisted training and analysis is considered essentially impossible.

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That shift has drawn mixed reactions within the Go community. Some players and observers argue AI’s dominance has diminished the creative, improvisational character the game once rewarded, while others contend it has opened new strategic possibilities that human players continue to explore. The technology has also had a democratizing effect on access to high-level training resources, a development some attribute to more female players climbing the professional ranks in recent years.

A symbolic moment, ten years later

For a sport whose modern relationship with artificial intelligence was defined by Lee Sedol’s 2016 defeat, Shin’s comeback victory over KataGo offers a symbolic, if conditionally framed, counterpoint a decade later. While the two-stone handicap means Shin’s win cannot be directly compared to an even match against the world’s strongest Go AI, the result nonetheless marks the first official series victory by a human player over KataGo under the competition’s specific conditions, giving the Go world a fresh moment to reflect on how far both human players and the machines they train against have come since that first, era-defining match in March 2016.

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Why the UK’s AI Buildout Needs to Learn From the Retrofit Model

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Why the UK’s AI Buildout Needs to Learn From the Retrofit Model

Britain has committed £1.5 billion to its next wave of AI infrastructure

The question now isn’t whether the money will be spent — it’s whether it will be spent well.

Across the industry, the default answer to rising compute demand has been to build. Break ground on a greenfield site, lay years of planning and grid connection applications end to end, and hope the facility is ready before the workloads it was designed for become obsolete. It is a model built for a slower era of technology, one where a five-year construction timeline was an inconvenience rather than a competitive death sentence.

However, the default has proven to be outdated.

Samir Tabar, chief executive of the Nasdaq-listed AI infrastructure company WhiteFiber, has spent the past year proving there is a faster and cheaper way in.

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Rather than building from scratch, WhiteFiber has focused on acquiring underutilised industrial sites that already come with the two things a data centre most desperately needs: substantial power capacity and proximity to major metro areas. Its flagship US project, a former textile mill in Madison, North Carolina, was bought for a fraction of the cost of comparable greenfield land and shell development, and converted into a hyperscaler-grade AI campus in a little over a year. The result was validated in the clearest way an infrastructure model can be: a ten-year, roughly $865 million colocation agreement with the European AI hyperscaler Nscale, one of the largest names in the sector’s European expansion.

The economics of that deal are the part the UK should be paying closest attention to.

Tabar’s retrofit approach compressed years off the delivery timeline, converted a fast-moving acquisition into a project institutional lenders were comfortable underwriting, and did it all while sidestepping the cost overruns and planning delays that have become endemic to greenfield data centre construction.

In an industry where the decisive competitive variable is shifting from the size of a campus to the speed at which it can be delivered at uncompromised quality, that is not a marginal advantage. It is the whole game.

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The UK, by contrast, is still leaning heavily on the greenfield playbook, even as it pours fresh capital into AI compute through its AI Growth Zones and national supercomputer commitments.

Grid connection queues remain one of the single biggest bottlenecks to getting new capacity online, and the industrial landscape — much like America’s — is dotted with underused, well-powered sites that a retrofit-first strategy could bring into service in a fraction of the time.

If the UK wants its £1.5 billion to translate into operational AI capacity rather than years of planning applications, the retrofit model Tabar has proven out in North Carolina offers a template worth studying seriously.

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EasyJet expands base at Bristol Airport supporting hundreds of jobs

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The budget airline now operates 90 routes from Bristol

Bristol Airport - newest addition to easyJet’s fleet flies into Bristol Airport - new Airbus 320 joins the airline’s Bristol-based fleet.  Photographer: Michael Lloyd/Staff   Reporter:    Copyright: Bristol News and Media

An easyJet aircraft(Image: Bristol News and Media)

Budget airline easyJet has added an extra aircraft to its base at Bristol Airport in a move it says will support 400 jobs including pilot and cabin crew roles. The A320 Neo plane is the carrier’s 20th at the South West transport hub.

EasyJet said the expansion has enabled it to provide more routes from Bristol including to destinations such as Reus, Thessaloniki, Seville, Cape Verde, Bari and Budapest.

The airline now operates 90 routes from Bristol to 27 countries.

Kevin Doyle, easyJet’s UK Country Manager, said: “We are delighted to have welcomed the arrival of a 20th aircraft and our 13th Neo aircraft at our Bristol base. Our continued commitment and growth in Bristol supports many skilled jobs and plays a vital role in connecting the South West to Europe and beyond.

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“Providing more affordable air travel not only ensures flying remains accessible to the six million passengers who choose to fly with us from Bristol each year, but also drives inbound tourism, bringing visitors and economic benefits to the South West.”

Dave Lees, chief executive of Bristol Airport, said the airport was “especially pleased” to welcome another Airbus A320 Neo.

According to easyJet, Airbus’s Neo aircraft are 20 per cent more fuel efficient per seat as well as 50 per cent quieter than the planes in the rest of the fleet. Neo now make up 65 per cent of the easyJet fleet at Bristol.

“This underpins our commitment to local communities that we are actively encouraging newer, quieter and more fuel-efficient aircraft to Bristol Airport,” said Mr Lees.

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“We’re really proud to be one of easyJet’s biggest European bases, which is so important for providing not only unrivalled choice of destinations, more inbound tourism opportunities, connections and frequency, but more high-quality jobs in our region.”

Earlier this month, US investment giant Apollo agreed to acquire easyJet for £5.7bn in a surprise move that trumped an earlier approach from rival asset manager Castlelake.

The budget airline confirmed it was prepared to accept an all-cash proposal from Apollo, valuing the airline at 714p per share. The carrier said Apollo’s offer “delivers a superior outcome for easyJet shareholders”.

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IBM cuts annual revenue growth forecast as customers prioritize AI infrastructure spending

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IBM cuts annual revenue growth forecast as customers prioritize AI infrastructure spending
IBM cut its annual revenue growth forecast on Wednesday, days after shocking Wall Street with a warning that corporate spending was shifting toward AI-focused data-center gear at the expense of its software and mainframe computers.

The company also missed profit and revenue expectations for the second quarter ended June 30. Executives sought to reassure shareholders that customers prioritized spending on AI in the quarter but were not looking to move away from mainframes in the ‌longer term.

The Armonk, ⁠New York-based ⁠company’s shares dipped marginally in extended trading, following a 2% rise earlier.

CEO Arvind Krishna said last week IBM had “faltered” in adapting and “numerous large deals” had slipped, sending the company’s shares down 25%, its steepest one-day fall in more than a century.

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On the earnings conference call, Krishna said a “majority of what didn’t happen in the second quarter was large capex deals at large clients” and added that about one-third of those deals had now closed in the current third quarter.


“A lot of the demand is deferred, not destroyed,” ⁠Krishna said.
IBM’s ‌forecast spotlights how the scramble for AI hardware has stoked investor fears that companies rushing to secure scarce servers, chips and networking gear could be cutting back on spending on ⁠the wider software sector. IBM now expects 2026 revenue growth between 4% and 5%, down from its previous expectations of more than 5% growth. The midpoint of the forecast is below analysts’ average estimate of a 4.8% rise to $70.77 billion in revenue, according to data compiled by LSEG.

“For the broader software sector, this should be treated as a positive print, with IBM’s software woes more likely to reflect specific IBM-related hardware issues, as management outlined in its investor letter last week,” CFRA analyst Brooks Idlet said.

Revenue from IBM’s Z mainframe, which processes millions of ‌daily transactions across industries such as banking and airlines, slumped 42% in the second quarter, dragging infrastructure revenue down 7% to $3.84 billion.

“That mainframe stack of hardware and transaction processing software impacted IBM’s growth by over five ⁠points in the quarter,” IBM finance chief James Kavanaugh told Reuters. “We were only expecting about a point or two of an impact.”

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He said IBM sees “no evidence of clients moving off a mainframe,” adding that it expects “significant outperformance in the program to continue through the second half.”

Software revenue in the second quarter rose 5% to $7.76 billion but missed an average estimate of $7.88 billion.

The company’s second-quarter revenue ticked up 1% to $17.16 billion, missing estimates of $17.58 billion. IBM reported a net profit of $2.17 billion, a dip from a year earlier, while adjusted profit of $2.93 per share missed an average estimate of $2.97.

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Centaurus Metals at Noosa Mining Conference 2026: Jaguar funding nears

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defence stocks rally, No 11 hedges

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defence stocks rally, No 11 hedges

Defence shares rallied the moment John Healey was named chancellor. By Tuesday afternoon, Downing Street had declined to confirm the number the sector actually wants, and ruled out the funding mechanism some had hoped for.

Healey resigned as defence secretary last month after accusing Sir Keir Starmer’s government of falling “well short” on military spending. Andy Burnham’s decision to hand him the Treasury was a surprise, and markets read it as an instruction rather than a consolation prize.

Shares in Babcock International, which builds warships and maintains Britain’s naval bases, rallied more than 7 per cent on the London Stock Exchange before closing up 4.1 per cent at £10.80½, one of the biggest risers on the FTSE 100.

BAE Systems, which builds fighter jets and submarines, rose 1.8 per cent. Qinetiq, spun out of the Ministry of Defence’s research agency, gained 3.1 per cent on the mid-cap FTSE 250.

For most business owners, the share prices are the least interesting part. The appointment has raised the prospect of greater private sector procurement, and that is where the money reaches the wider economy: through the tiers of engineering, machining, software, logistics and testing firms that sit beneath the primes.

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That supply chain has been the target of a concerted push. The MoD is aiming to lift direct and indirect spending with smaller suppliers to £7.5 billion by May 2028, a 50 per cent increase, and has stood up a dedicated unit to help small defence firms navigate procurement. Manufacturers have separately pressed ministers to go further by tying foreign contract wins to binding reinvestment in Britain.

None of that works without the budget behind it. A spokesman for the prime minister said on Tuesday that Healey’s appointment was a “signal of intent” on defence spending, but declined to commit to increasing it to 3 per cent of GDP by 2030. Spending is due to rise to 2.7 per cent by the end of the decade. The spokesman also said “war bonds are not something we’re looking at”.

That gap between signal and commitment is the practical issue for suppliers weighing capacity investment. Order books built on 2.7 per cent look different from order books built on 3 per cent, and hiring or tooling decisions taken this year will be judged against whichever number turns up.

Healey’s appointment was welcomed by Stephen Phipson, chief executive of Make UK, whose members include BAE and Rolls-Royce, and which is pressing the government to bring down industrial energy costs and business rates.

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“Manufacturers will welcome the appointment of someone with a reputation for being pragmatic, focused on delivery, and committed to making government work effectively.”

That welcome carries a bill attached. Make UK’s members are absorbing a near-£1 billion annual increase in business rates alongside some of the highest industrial electricity prices in Europe. A chancellor who wants a bigger British defence industrial base has to make it viable to manufacture here first, which is a Treasury question rather than a Ministry of Defence one.

Healey is not new to the building. He served as a Treasury minister in Sir Tony Blair’s government, which may explain why the appointment was read as more than symbolic.

Lord Dannatt, a former head of the British Army, told Times Radio that the appointment was “a masterstroke”.

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He said: “John Healey, as we all know, resigned not that long ago, having said that the previous prime minister was unable to produce the funding that defence needed, and the previous chancellor was unwilling, so now he is the one behind the desk in No 11 and has really got to answer his own question.”

For SMEs in and around the defence supply chain, the answer arrives at the Budget rather than in this week’s share prices. Until then, the sensible read is that procurement reform is accelerating while the funding envelope stays exactly where it was.


Jamie Young

Jamie Young

Jamie Young is Senior Reporter at Business Matters, covering SME finance, employment law and Westminster policy since 2016. He has reported on every Budget and Autumn Statement since 2018, helped make sense of the ‘covid era’ and the bounce-back loan scheme from launch through the fraud investigations, and broke the magazine’s coverage of the 2024 late-payment reforms. He joined Business Matters straight from completing his BA in Administration from Exeter University and is NCTJ-qualified. Reach him at jyoung@cbmeg.co.uk

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Why is Cathay Pacific Airways stock surging today?

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Earnings call transcript: Keppel DC REIT posts stronger H1 2026 DPU

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Syrah Q2 2026 slides: Balama curtailed, Vidalia nears commercial sales

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Syrah Q2 2026 slides: Balama curtailed, Vidalia nears commercial sales


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Charles Schwab Beats Earnings Estimates. Its Customers Just Can’t Get Enough.

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Charles Schwab Beats Earnings Estimates. Its Customers Just Can’t Get Enough.

Charles Schwab Beats Earnings Estimates. Its Customers Just Can’t Get Enough.

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Tesla robotaxis log 380,000 unsupervised miles with no ‘notable’ incidents

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Tesla robotaxis log 380,000 unsupervised miles with no 'notable' incidents

Tesla said Wednesday that its robotaxi fleet has logged more than 380,000 unsupervised miles across six cities in two states without what the company described as a “notable” safety incident.

Ashok Elluswamy, Tesla’s vice president of AI software, highlighted the fleet’s safety record during the electric vehicle maker’s second-quarter earnings call, telling investors it had recorded “zero notable incidents.”

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Any reported incidents involved “other actors impacting us when we were stationary,” Elluswamy said.

“I’d like to emphasize how safe the operation has been so far,” Elluswamy said. “Zero notable incidents over 380,000 miles.”

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A Tesla robotaxi in Austin

A Tesla robotaxi travels along South Congress Avenue in Austin, Texas, June 22, 2025. Tesla said that its robotaxi fleet has logged more than 380,000 unsupervised miles across six cities in two states without what the company described as a “notable” (Reuters/Joel Angel Juarez / Reuters)

Elluswamy said the results support Tesla’s camera-based approach to autonomous driving.

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“Historically, the so-called experts have always claimed that you need lidars, radars, HD maps and the entire kitchen sink to drive safely,” he said. “Here, we show that such is not true. You can have safe, comfortable and affordable autonomy with just cameras.”

Tesla said mileage traveled by its unsupervised robotaxi fleet has grown at a double-digit weekly rate for months.

“We have grown at such a high compounding rate on a week-over-week basis over the last several months,” Elluswamy said. “Not only that, we expect to continue growing at such a large rate through the rest of this year.”

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FILE PHOTO: Tesla robotaxis launch in Austin, Texas

A Tesla robotaxi operates on South Congress Avenue in Austin, Texas, on June 22, 2025.  (Reuters/Joel Angel Juarez / Reuters)

The remarks came one day after Tesla expanded its robotaxi service to Orlando and Tampa, according to Reuters.

Tesla launched the service in Austin in June 2025, initially placing safety monitors inside the vehicles. 

It later began offering fully unsupervised rides in Austin and expanded the service to Dallas, Houston and Miami, Reuters reported.

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SELF-DRIVING CAR COMPANIES WAYMO, TESLA TO TESTIFY AT KEY SENATE COMMITTEE ON REGULATING GROWING INDUSTRY

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waymo vehicle picks up passenger

Passengers exit a Waymo self-driving car, Dec. 26, 2025, in San Francisco. Unlike Waymo, which uses lidar sensors, Tesla relies mainly on cameras and AI software. (John J. Kim/Chicago Tribune/Tribune News Service via Getty Images / Getty Images)

Unlike Waymo, which uses “light detection and ranging” or “lidar” sensors, Tesla relies mainly on cameras and AI software, according to the outlet.

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We expect that the time to launch to a new city will continue to trend towards zero, towards an end where we operate in entire states as a whole, instead of going city by city,” Elluswamy added.

Tesla could not immediately be reached by FOX Business for comment.

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Reuters contributed to this report.

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