AI25026 AI and Big Tech V01 291025

 

Apple launched its iPhone 17 and demand soared. 

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With the world of artificial intelligence growing ever more influential, few company earnings are as eagerly awaited as those of America’s biggest technology companies this week.

Given the hype, not to mention the stock market valuations related to AI, results from Google owner Alphabet, Amazon, Apple, Meta Platforms and Microsoft in just a few days may indicate whether the hype is justified, and show the scale of the phenomenal investment outlay on infrastructure needed to get it off the ground.

The performance of these five companies will give the market a sense of whether the bubble is about to pop. Alphabet, Meta Platforms and Microsoft report results today, followed by Amazon and Apple tomorrow.

Here are five key things to watch:

Capital expenditure — aka Big Tech’s big spending spree 
Spending on AI infrastructure by Silicon Valley heavyweights is unprecedented and shows no sign of slowing. Together, the five are expected to spend hundreds of billions of dollars this year, on cloud, data centres and chips. This year Alphabet has raised its full-year capital expenditure forecast to $85 billion, and Amazon to over $100 billion. Meta does not have a cloud business, but is to spend up to $72 billion.

These numbers will be closely watched. The companies argue bigger infrastructure now could mean bigger profits later. Sceptics worry about whether the investment will pay off, as companies take on debt to fund this expansion, and say it raises the spectre of the dotcom boom and bust era.

Revenue: the rush for AI returns
We may get some answers to these key questions: do the returns from AI sales justify all this investment? Is AI being widely adopted? And is it delivering real value to customers? A recent study from MIT (Massachusetts Institute of Technology) found a 95 per cent failure rate for AI projects. Forecasts point to the overall revenue numbers rising at a clip, but investors will want to know how much of this is related to AI.

Advertising figures are key at Meta, which is expected to deliver revenue growth of 22 per cent to $49 billion, as people spend more time on Instagram and on its increasingly popular TikToklike, Reels feature.

Advertising spending at Alphabet has been resilient, although there are concerns about this being eroded by AI-driven answers it is generating to its own search results, not to mention a drift of users towards rival chatbots or “answer engines”. Revenue growth of 13 per cent is expected, or $100 billion.

Amazon should see a 12 per cent rise in sales to $177 billion while Microsoft is forecast to report revenue growth of 15 per cent to $75 billion.

The cloud: a fight for the world’s data Microsoft and Amazon, lagged by Google, are the world’s dominant cloud providers and enterprise spend here for these behemoths are ones to watch. So far, the demand for cloud resources continues to outstrip supply. Forecasts suggest Microsoft’s Azure could see about 38 per cent growth, Google Cloud about 30 per cent and Amazon’s Web Services about 18 per cent.

Any commentary on demand that can justify their enormous valuations will be significant. Analysts will want to see if Amazon will comment further on its web outage, when an error in one of its US data centres knocked out a chunk of the world’s internet for a day.

Hardware: the gadget arms race 
Meta has continued its foray into wearable technology with its AI-powered Ray-Ban Display glasses and analysts will be watching for an update on sales.

Apple launched its iPhone 17 at the start of September and demand for the new handsets has boomed. It was the only brand in China to grow smartphone shipments in the third quarter. But analysts at Jefferies bank, noted that delivery lead times were trending down. Investors will be waiting to see what forecasts are for the Christmas holiday season and whether consumers are opting for the cheaper versions.

As always with Apple, what happens on the iPhone is as important as the handset itself and numbers from its massive services division, including the App Store, Apple TV and Apple Music, will be interesting, in the wake of pressure from competition regulators.

Finally, there may be updates about delays to key Apple Intelligence features and there are rumours about an announcement of a foldable phone.

China: the tech trade wars 
Tech is being weaponised in the US- China trade wars. As President Trump and President Xi convene for talks, the Big Tech companies remain in the eye of the storm. Are they seeing any impact on their operations, any supplychain constraints or any penalty from tariffs and trade restrictions?

AI25025 Microsoft seals OpenAI Deal. V01 291025

 

Sam Altman, chief of OpenAI, and his counterpart at Microsoft, Satya Nadella will be long-term collaborators

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OpenAI has finalised a restructuring plan with its external shareholder Microsoft that values the ChatGPT maker at $500 billion and clears the way for it to become a for-profit business.

Under the deal OpenAI, which was founded as a research-focused nonprofit business in 2015, will take on a more investor-friendly structure to allow it to raise capital. A non-profit called the OpenAI Foundation will hold equity in the company’s for-profit arm.

Microsoft, which first invested in OpenAI in 2019, will own a 27 per cent stake in the restructured artificial intelligence company worth about $135 billion, the companies said yesterday.

The deal will also give Microsoft access to OpenAI’s technology until 2032, even if OpenAI reaches artificial general intelligence, the point at which AI systems can match a well-educated human adult’s cognitive abilities.

Separately, OpenAI has agreed to buy $250 billion of Microsoft’s Azure cloud infrastructure services. However, Microsoft will lose its right of first refusal on new cloud services from OpenAI.

Shares of Microsoft gained $10.55, or 2 per cent, to $542.07 at the close in New York, propelling the group’s valuation to $4.1 trillion.

Raimo Lenschow, an analyst at Barclays, said: “The scale of the Azure commitment underscores Microsoft’s continued dominance in AI infrastructure, and the deal also sets the stage for long-term collaboration between the two firms.”

OpenAI has become a commercial giant since the launch of ChatGPT in 2022 which has 800 million weekly active users. It signed a deal in 2019 with Microsoft that gave the company rights over much of OpenAI’s work in exchange for providing the costly cloud computing services needed to carry it out. Open- AI is expected to make losses as it prioritises growth and developing more advanced AI models over profitability.

Analysts at HSBC have forecast OpenAI losses of $23.5 billion in 2025, rising to $60 billion in 2027.

The restructuring paves the way for OpenAI to pursue a potential public listing.

Bret Taylor, its chairman, said: “OpenAI has completed its recapitalisation, simplifying its corporate structure.

The nonprofit remains in control of the for-profit, and now has a direct path to major resources before AGI arrives.”

Gil Luria, head of technology research at DA Davidson, said the deal “resolves the longstanding issue of OpenAI being organised as a not-forprofit and settles the ownership rights of the technology vis-à-vis Microsoft.”

Silicon Valley and Wall Street have been consumed by an AI gold rush.

PayPal said yesterday it had entered into a deal with OpenAI that would allow ChatGPT users to buy products using the payment firm’s platform, sending its shares up $2.77, or 3.9 per cent, to $73.02 at the close of trading in New York.

PayPal also raised its profit forecast for the year again, banking on resilient consumer spending, and announced the first dividend in its 27-year history

AI25024 AI and Copyright V01 281025

 Who will stop the AI data heist taking place in plain sight?

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Katie Prescott

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The Louvre heist had us gasping at the ingenuity of the jewel thieves. Getting less attention is the repeated daylight robbery online, as a growing industry of data launderers scrape websites, steal copyrighted information and flog it to AI companies.

Far from a daredevil evasion of authority, this Great E-scrape is a grubby, damaging practice happening in plain sight and it needs to be stopped. Fast.

Reddit is the latest business to try to prevent its information being taken by the army of “would-be bank robbers” as it calls them.

It is suing three companies, who it claims hide their identities, locations and even mimic real people to dodge its website protections and pilfer at will.

These businesses allegedly worked around Reddit’s technical protections by scraping Google search results — three billion of them — which revealed information from forum posts, comments and discussions.

“Scrape even the toughest sites with market-leading success rates”
Lithuania’s Oxylabs, one of the accused, boasts on its website, advertising its “dynamic CAPTCHA bypassing to ensure uninterrupted access”.

Reddit is a prime target. The website has over 100 million users, discussing all sorts of topics and interests within hundreds of thousands of communities. This web chatter is gold dust for generative AI companies looking for constantly refreshed, real-life human-made information to feed their machines.

The US-listed business has already done a deal with Google and OpenAI for its content, so is understandably keen to protect its valuable asset. Yet, the response from scraping companies is always the same: they are doing nothing wrong. Hey, the internet is open and should be free for all, right? “No single company owns public data ... and private companies should not be permitted to place limits on who may share and receive information online,” Oxylabs said.

Is scraping legal? No. Not when it bypasses controls, scrapes protected content or collects personal data without a valid basis. Yet it continues and it is so easy. Websites have something known as “robots.txt” files, which instruct crawlers and scrapers what they can access, which have all the effectiveness of a road sign. They only work if you’re prepared to obey the rules. Primarily, we need a resolution to the sludgy dispute between publishers and AI companies over copyright. After all, there would be no theft, without a market for the goods.

In the Reddit case, it alleged that Perplexity, an AI-powered search engine, was illegally using its data and set a trap which it described as the equivalent of a marked banknote. Putting out a specific piece of information, Reddit found it was only used by Perplexity.

Perplexity punched back in a Reddit post (!) saying it does not train AI models on content and argued that efforts to stop it citing Reddit’s content were “the opposite of the open internet”. If anyone quotes the statement, they could be breaching Reddit rules, Perplexity seemed to mock.

Along with losing their IP, scrapers also create another cost for publishers who are forced to implement stronger guardrails to keep them out. Furthermore, the AI-related surge in traffic means more pressure on websites which have to increase their server power, even though these “visitors” are just bots, as Jimmy Wales, founder of the (very open) Wikipedia said at The Times Tech Summit.

It has been a year since Feryal Clark, the UK’s former AI minister, told me that the AI copyright issue would be resolved by … last Christmas. Instead, we are stuck with an industry mushrooming online, capitalising on the lack of a definitive legal framework.

Speed is of the essence. But don’t expect any 2025 Christmas miracles.
Early next spring the UK government is due to lay out its proposals for reform and a litany of legal cases might also slowly help shape new rules in the United States.

In the meantime, sites like Wikipedia and Reddit are there for everyone to look at and, like the Louvre’s stolen crown jewels, are ripe for the picking.

Katie Prescott is Technology Business Editor of The Times

AI25023 AI regenerating History. V01 281025

 Musk vows to rewrite the Roman history books

Italy 

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James Imam - Milan 

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Whether likening his social media platform to a colosseum battleground or challenging another tech magnate to a gladiatorial duel, Elon Musk has long made clear his fascination with the ancient world order.

Now the world’s richest man, who once called himself the “Imperator of Mars”, says he is “rewriting the history books” by financing a new age of data-driven research in archaeology, powered by artificial intelligence.

Musk spoke on Friday at the award ceremony for the annual Expandere Conscientiae Lumen prize at the seat of the mayor of Rome, on the Capitoline Hill that was once home to the Temple of Jupiter, the most sacred place in ancient Rome. Eleven grants were given to innovative archaeological projects harnessing digital technology.

The initiative, organised by the American Institute for Roman Culture, aims to improve understanding of Greco-Roman culture. This year’s is funded with $1 million from Musk’s US-based charitable foundation.

“I am interested in history and Rome constitutes a large part of the history of western civilisation,” Musk said via video link at Friday’s ceremony. AI, he said, would fill knowledge gaps by writing “a new history book based entirely on ancient material and archaeology”.

The winning projects showed that the technology was about far more than writing texts. One used advanced drone fleets and photogrammetry powered by AI to capture high-resolution aerial data of Greco-Roman heritage in Jordan — from the grand colonnades of Jerash to the Roman amphitheatre in Amman — to preserve intricate architectural details in danger of being lost to climate change in “stunning” 3D models.

Another initiative aimed to reconstruct the supply chains of ancient Rome by analysing the geological “fingerprints” of pigments used in the frescoes of Pompeii, the city buried by Mount Vesuvius in AD 79, and through simulations of ancient sea routes, integrating historical weather patterns and ship performance data.

Five of the winning projects were Italian, and others were based in Morocco, Tunisia and Albania.

Musk, who in 2023 challenged Mark Zuckerberg, the tech billionaire rival, to a cage fight at an ancient site in Italy, before the culture minister ruled out Rome, was accused of making a fascist gesture at a rally celebrating President Trump’s second inauguration. Some commenters on X, Musk’s platform, insisted it was a “Roman salute”.

His latest comments coincide with an era of dawning digital experimentation at Italian heritage sites.

In Naples, researchers are using x-ray imaging and computer software to “digitally unroll” Roman papyrus carbonised at Herculaneum by the Vesuvius eruption. The project, which has shown that one text was written by the Epicurean philosopher Philodemus and revealed the location of Plato’s garden grave, was awarded €2 million by Musk this year.

In Pompeii, researchers are using drone-mounted lasers, engineering simulations and 3D renderings to reconstruct the lost upper floors and observation towers of ancient domuses used for stargazing at banquets. “We can reconstruct experiences, spaces and social dynamics of the time,” Gabriel Zuchtriegel, director general of the archaeological park in Pompeii, said.

Marta Modolo, an archaeology specialist in AI at Milan University, who is analysing thousands of Neolithic animal bones from central Italy, said digital technology could slash research time from years to months and reduce the risk of human error.

AI25022 Synthetic AI V01 271025

 The term “synthetic AI” can have a few meanings depending on context, but generally, it refers to artificial intelligence systems that can generate, simulate, or synthesize new data, experiences, or intelligence-like behavior — rather than just analyze or classify existing data.

Here are the main interpretations:

🧠 1. AI That Synthesizes (Generates) Content

In this sense, synthetic AI is another name for generative AI — systems that synthesize new outputs such as:

• Text (e.g., ChatGPT)

• Images (e.g., DALL·E, Midjourney)

• Music (e.g., Suno, Mubert)

• Video (e.g., Runway Gen-3)

• Synthetic data for model training (e.g., simulated datasets for privacy or rare events)

👉 Example:

A model that creates realistic photos of people who don’t exist is a synthetic AI system generating synthetic data.

🧬 2. Artificial (Synthetic) General Intelligence

Some researchers use “synthetic AI” in a philosophical or theoretical sense — to describe AI systems that aim to synthesize intelligence itself, not just mimic it.

• This overlaps with Artificial General Intelligence (AGI) research.

• The goal is to build a synthetic mind — an intelligence that learns, reasons, and adapts like humans.

🧪 3. Synthetic Data + AI

In industry, synthetic AI sometimes refers to the use of AI to create synthetic data that can train other AI models:

• Used in healthcare, finance, autonomous vehicles, etc.

• Improves privacy, reduces bias, and expands rare-event datasets.

🔍 In Summary

Context Meaning of “Synthetic AI” Example

Generative AI AI that creates new content ChatGPT, DALL·E

AGI Research AI that synthesizes intelligence Cognitive architectures

Data Science AI that generates synthetic data Synthetic patient records for training models

Perfect 👍 — let’s unpack both sides of “synthetic AI”:

1️⃣ Synthetic AI as a Data/Content Generator (Practical, Today)

2️⃣ Synthetic AI as Synthetic Intelligence (Theoretical, Future-Oriented)

🧠 Part 1: Synthetic AI as a Generator of Data and Content

This is the current, real-world meaning — where “synthetic” refers to something artificially created.

Here, synthetic AI is any system that synthesizes new data, content, or information using machine learning.

🔧 How It Works

Synthetic AI systems use generative models — especially large neural networks — trained on real-world data to learn patterns and then generate new, similar outputs.

• Language: GPT, Claude, Gemini — generate text, dialogue, code.

• Images: DALL·E, Midjourney — synthesize new visuals from text.

• Audio & Music: Suno, Udio — generate speech or music.

• Video: Runway, Pika Labs — synthesize moving visuals.

• Synthetic Data: Models like Gretel.ai or MostlyAI generate artificial datasets for training other AIs.

💡 Why It Matters

• Privacy: Synthetic data doesn’t expose real users or patients.

• Cost Efficiency: You can train models without collecting expensive or sensitive real-world data.

• Bias Control: Synthetic datasets can be balanced to reduce discrimination.

• Scalability: Unlimited synthetic examples for training machine learning systems.

⚠️ Challenges

• Realism: Synthetic data can miss subtle real-world nuances.

• Bias Transfer: If the original data is biased, synthetic data may replicate it.

• Ethics: Generated content can be used for misinformation or deepfakes.

In short: Synthetic AI today = AI that makes new, artificial data or creative output.

🧬 Part 2: Synthetic AI as Synthetic Intelligence (Future Vision)

This is the philosophical and scientific sense of the term — where “synthetic” means constructed or engineered.

Here, synthetic AI refers to the creation of genuine, autonomous intelligence — an engineered mind.

🔍 Goal

To synthesize intelligence itself, not just simulate parts of it.

In this sense, synthetic AI is a step toward Artificial General Intelligence (AGI) or even Artificial Consciousness.

🧩 Key Research Areas

• Cognitive architectures (e.g., ACT-R, Soar, LIDA) that model human reasoning.

• Neurosymbolic AI: combining neural networks (learning) with symbolic reasoning (logic).

• Embodied AI: robots or agents that learn by interacting with the physical world.

• Emergent intelligence: large-scale systems where complex cognition arises spontaneously from simple rules (like evolution or brain networks).

🧠 Vision

Synthetic intelligence would:

• Learn any domain without task-specific retraining.

• Understand context, goals, and emotions.

• Reason, plan, and adapt autonomously.

• Possibly exhibit consciousness or self-awareness.

⚠️ Challenges

• We don’t yet fully understand how human cognition arises.

• Ethical concerns: alignment, control, consciousness rights.

• The “black box” problem — how do we know why an intelligent system acts as it does?

In short: Synthetic AI (in theory) = AI that builds or embodies real intelligence.

🧭 Summary Table

Aspect Synthetic AI (Data/Content) Synthetic AI (Intelligence)

Focus Generating new data, text, or media Building true, autonomous intelligence

Examples ChatGPT, DALL·E, synthetic data generators AGI, cognitive architectures, embodied AI

Goal Creativity, simulation, augmentation Understanding and recreating intelligence itself

Timeframe Present-day (2020s) Future (2030s–2050s and beyond)

Risks Deepfakes, bias, misinformation Alignment, control, ethics, safety