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AI tokens outpace record-breaking bitcoin By Reuters

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© Reuters. FILE PHOTO: Figurines with computers and smartphones are seen in front of the words “Artificial Intelligence AI” in this illustration taken, February 19, 2024. REUTERS/Dado Ruvic/Illustration/File Photo

By Medha Singh and Lisa Pauline Mattackal

(Reuters) – The artificial intelligence boom has hit the crypto market with a bang.

Coins linked to AI-focused crypto projects have jumped alongside tech stocks like Nvidia (NASDAQ:), driven by insatiable investor appetite for applications like machine-learning.

The rise of many AI crypto tokens has outpaced even that of bitcoin over the past year as the world’s biggest cryptocurrency has surged to record levels.

Their combined market value has ballooned to $26.4 billion, from just $2.7 billion last April, according to CoinGecko data. Tokens linked to these projects are up between 145% and 297% in the past 30 days.

If the more optimistic industry predictions come to pass, there could be more room to run, as some market watchers say crypto and blockchain technology could help solve some of the AI industry’s teething problems such as privacy and a need for computing power.

“As both AI systems and blockchain networks continue to grow, we will see more and more use cases fusing together the two industries,” said Markus Levin, co-founder of blockchain data storage firm XYO Network.

The CoinDesk Indices Computing Index, which includes AI-linked tokens, has leapt over 165% over the past 12 months, outpacing even bitcoin’s 151% rise to record levels.

Trading volumes in AI tokens have also risen sharply this year, Kaiko Research data showed, hitting an all-time high of $3.8 billion in late February.

“There is a significant chance that … AI applications will be crypto’s raison d’être,” fund manager VanEck’s Matthew Sigel and Patrick Bush said in a note.

Some of the top blockchain projects at the moment include the Render Network, a blockchain platform for peer-to-peer sharing of AI-generated graphics, Fetch.AI, a platform to build AI apps and SingularityNET, an AI services marketplace.

“Investors are starting to realize that if you want real value, you need products that are uncorrelated to the crypto market,” said Ahmad Shadid, founder of AI-focused blockchain startup io.net.

WINNERS AND LOSERS

AI-linked blockchain products include a wide variety of services including payments, trading models, machine-generated non-fungible tokens and blockchain-based marketplaces for AI applications where users pay developers in cryptocurrency.

Investment manager VanEck has predicted that revenue from AI crypto projects could reach $10.2 billion by 2030 in their base case, and over $51 billion in their bullish scenario.

VanEck pointed to the use of crypto tokens as rewards, developing physical computation infrastructure, data verification, and transparency in proving digital ownership as primary areas where blockchain technology lends real-world value to AI development.

Offering crypto tokens as incentives allows quick scalability, said io’s Shadid. His company plans to launch a token later this year.

“The reason we can scale fast is because of the token we have coming out,” he added. “The token incentivizes owners of physical infrastructure to bring their computers on to our network,” Shadid added.

Yet, just as with the AI boom itself, picking winners and losers could be fraught with peril.

“We’re still in the very early stages of AI networks integrating with blockchain-based networks, and the utility of a lot of tokens is still very much uncertain,” cautioned Levin.



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Ex-Post Office boss regrets ‘missed opportunity’ to halt Horizon scandal

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“On reflection, and I have reflected on this very hard, when I finished being the Horizon programme director [in early 2000] it would have been very beneficial if I had notified both the lawyers and the [investigations team] that Horizon was a new system coming in, and that they should be very cautious about evidence coming out of that system,” he said.



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Sri Lanka’s economic crisis and debt restructuring efforts By Reuters

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COLOMBO (Reuters) – Sri Lanka’s government rejected a proposal from its international bondholders on Tuesday on restructuring the more than $12 billion the country owes to them.

It means a near two-year spell in default will drag on for Sri Lanka and that the country’s next tranche of vital IMF support money could potentially get delayed.

Below is a timeline of the key events in the crisis and the efforts to resolve it:

2021-2022: Sri Lanka’s economy crumbles after years of overspending leaves its foreign exchange reserves critically low and the government unable to pay for essentials, such as fuel and medicine.

The country’s bonds suffer from multiple downgrades by credit rating agencies warning of the increasing risk of default. At the start of 2022 it manages to make a $500 million bond payment but it leaves its foreign exchange reserves precariously low.

MAY, 2022 – Sri Lanka is declared in default after it fails to make a smaller $78 million bond coupon payment.

JULY, 2022 – Public anger drives protesters to storm then-President Gotabaya Rajapaksa’s office and residence. Rajapaksa flees to the Maldives, before moving on to Singapore.

Current President Ranil Wickremesinghe is voted into power by Sri Lankan lawmakers.

MARCH, 2023 – The International Monetary Fund approves a near $3 billion bailout for Sri Lanka after talks with Wickremesinghe’s government and assurances about its plans to repair the country’s finances.

OCTOBER, 2023

Sri Lanka announces an agreement with China’s EXIM (export/import) Bank to delay payments on about $4.2 billion worth of loans the Chinese lender it has extended to the country.

NOVEMBER, 2023

Other creditor nations including India, Japan and France agree to restructure about $5.9 billion in debt.

MARCH, 2024

A group of Sri Lankan officials arrives in London to meet with a number of investment funds that hold its more than $12 billion worth of government bonds. Talks advance to the key “restricted” phase where proposals are discussed privately and those involved agree not to buy or sell any of the debt on the open market.

© Reuters. FILE PHOTO: A general view of the main business district as rain clouds gather above in Colombo, Sri Lanka, November 17, 2020. REUTERS/Dinuka Liyanawatte/File Photo

APRIL, 2024

The government rejects a proposal tabled by the bondholders. The main stumbling blocks are that some the “baseline” assumptions used differ to those of the IMF and that the plan did not include a contingency option for the government in case the economy fails to recover as expected.





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AI could gobble up a quarter of all electricity in the U.S. by 2030 if it doesn’t break its addiction

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Before artificial intelligence can transform society, the technology will first have to learn how to live within its means.

Right now generative AI have an “insatiable demand” for electricity to power the tens of thousands of compute clusters needed to operate large language models like OpenAI’s GPT-4, warned chief marketing officer Ami Badani from chip design company Arm Holdings. 

If generative AI is ever going to be able to run on every mobile device from a laptop and tablet to a smartphone, it will have to be able to scale without overwhelming the electricity grid at the same time.

“We won’t be able to continue the advancements of AI without addressing power,” Badani told Fortune’s Brainstorm AI conference in London on Monday. “ChatGPT requires 15 times more energy than a traditional web search.” 

Not only are more businesses using generative AI, but the tech industry is in a race to develop new and more powerful tools that will mean compute demand is only going to grow—and power consumption with it, unless something can be done. 

The latest breakthrough from OpenAI, the company behind ChatGPT, is Sora. It can create super realistic or stylized clips of video footage up to 60 seconds in length purely based on user text prompts. 

The marvel of GenAI comes at a steep cost

“It takes a 100,000 AI chips working at full compute capacity and full power consumption in order to train Sora,” Badani said. “That’s a huge amount.” 

Data centers, where most AI models are trained, currently account for 2% of global electricity consumption, according to Badani. But with generative AI expected to go mainstream, she predicts it could end up devouring a quarter of all power in the United States in 2030.

The solution to this conundrum is to develop semiconductor chips that are optimized to run on a minimum of energy.

That’s where Arm comes in: its RISC processor designs currently run on 99% of all smartphones, as opposed to the rival x86 architecture developed by Intel. The latter has been a standard for desktop PCs, but proved too inefficient to run battery-powered handheld devices like smartphones and tablets. 

Arm is adopting that same design philosophy for AI.

“If you think about AI, it comes with a cost,” Badani said, “and that cost is unfortunately power.”  

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