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“These Predictions Discourage Young People” – Nvidia CEO Jensen Huang Critiques AI Catastrophe Forecasts

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Nvidia CEO Jensen Huang has sharply criticized scientists who issue alarming forecasts about artificial intelligence causing societal collapse, arguing that such predictions lack scientific grounding and actively harm the field.

In a recent appearance on The Ezra Klein Show, Huang pushed back against what he described as “AI doomer” narratives, insisting they are not based on rigorous research and discourage young people from entering the industry.

He specifically addressed estimates from AI pioneer Geoffrey Hinton, who suggested a 10-20% chance that advanced AI systems could lead to societal collapse.

Huang rejected the figure, stating there is no scientific research supporting such precise probabilities. He argued that simply coming from a prominent scientist does not make a prediction scientific.

He said,

“I would tell Geoff that it’s irresponsible to say all that. All of his predictions have been wrong. Enough predictions. That 10 percent chance is not grounded in science. It’s not grounded in research. Just because it comes from a scientist doesn’t make it scientific. Those predictions are hurtful.”

“Is it good or bad that we scare young people about the future of A.I., so much so that they don’t even want to go to universities and don’t want to go to college anymore because they don’t think they’ll get a job? Is that helpful or hurtful, if it were to happen? It’s hurtful.

“Don’t think for a second just because you’re an alarmist that you’re doing a social good. It is not true. So I think that we ought to just all be wiser, more mature, be evidence-based, be scientific. If you want to be scientific, be scientific. Do the science.”

According to Huang, these forecasts have a poor track record and create unnecessary fear that steers talented students away from AI-related careers at a time when the technology needs more, not fewer, skilled contributors.

The Nvidia chief framed AI safety primarily as an engineering challenge rather than an existential philosophical crisis. He maintained that the industry should focus on practical, evidence-based approaches to building reliable systems instead of amplifying speculative worst-case scenarios.

Huang’s remarks come amid an ongoing and often polarized debate within the AI community. Figures such as Hinton, along with researchers associated with organizations focused on existential risk, have repeatedly warned that rapid progress in AI capabilities could outpace society’s ability to control or align the systems.

In defense, Huang’s comments reflect his longstanding position as one of the most prominent AI optimists in technology. As the leader of the company that supplies the majority of the specialized chips powering modern large language models and generative AI systems, he has consistently emphasized the transformative benefits of the technology while downplaying catastrophic risk narratives.

In contrast, Huang and other industry leaders argue that exaggerated doomerism risks slowing innovation, reducing investment, and creating regulatory overreactions that could cede technological leadership to less cautious actors.

Huang made clear which side he occupies. He expressed concern that constant emphasis on collapse scenarios could deter the next generation of engineers and researchers precisely when their contributions are most needed to improve safety, reliability, and usefulness.

Nvidia’s central role in the AI boom gives Huang’s words significant weight. The company’s GPUs remain the dominant hardware platform for training and running the largest AI models.

Any shift in public or regulatory sentiment driven by doomer narratives could affect demand for those chips, research priorities, and talent pipelines. Huang appears determined to counter the more alarmist messaging by stressing evidence, practical progress, and the tangible benefits already emerging from AI systems in science, industry, and everyday applications.

Outlook

The debate over AI’s long-term risks is likely to remain contentious as models become more capable and increasingly integrated into the economy.

Huang’s comments could reinforce the view among technology companies and investors that AI development should remain focused on measurable safety improvements, engineering controls, and practical applications rather than speculative probabilities of societal collapse. At the same time, warnings from researchers such as Hinton are unlikely to disappear.

As AI systems become more autonomous and capable of performing increasingly complex tasks, questions around alignment, misuse, labor-market disruption, and the ability of institutions to govern advanced systems are expected to remain central to the conversation.

Bitcoin ETFs See $715M Inflows as Jack Butcher’s Credits NFTs Top 100K Sales

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Bitcoin’s institutional market is showing renewed strength as spot Bitcoin exchange-traded funds record a fourth consecutive day of net inflows, with roughly $715 million entering the products.

The NFT market is experimenting with a more financialized structure, as digital artist Jack Butcher sells more than 100,000 Credits NFTs through an X-based money mint while Tokenworks launches CREDITSTR, a vehicle designed to buy and relist Credits NFTs.

The developments illustrate two different sides of the crypto economy: one increasingly connected to traditional financial markets and another attempting to turn digital collectibles into tradable financial assets.

The Bitcoin ETF inflows are particularly important because sustained flows provide a clearer picture of institutional demand than a single strong trading session. Spot ETFs give investors exposure to Bitcoin through regulated market infrastructure without requiring them to manage wallets or private keys directly.

Four consecutive days of inflows therefore suggest that capital is continuing to find its way into Bitcoin through conventional investment channels. The $715 million figure also arrives at a time when Bitcoin’s market narrative remains closely tied to liquidity, interest rates and institutional positioning.

ETF flows can influence market sentiment because large creations require underlying Bitcoin exposure to be maintained by fund issuers. However, inflows should not automatically be interpreted as a guarantee of continued price appreciation.

Investors can change allocations quickly when macroeconomic conditions, risk appetite or portfolio strategies shift. The NFT developments represent a different experiment. Jack Butcher’s Credits ecosystem has long attempted to connect digital art, collectibles and financialized ownership.

Selling more than 100,000 Credits NFTs through an X money mint demonstrates the continuing ability of established digital artists to generate substantial demand when distribution, community and scarcity intersect.

The more intriguing development is Tokenworks’ CREDITSTR. Rather than simply creating another collection, the project introduces a mechanism intended to acquire Credits NFTs and relist them.

That structure pushes NFTs toward a model that resembles inventory management or an asset marketplace, where value can potentially be created through repeated acquisition, pricing and resale.

This approach raises an important question for the broader NFT industry: can digital collectibles evolve from primarily cultural objects into financial instruments with deeper liquidity? The answer remains uncertain.

Traditional financial markets rely on continuous price discovery, transparent information and large pools of buyers and sellers. NFTs often have much thinner liquidity, meaning that an apparent market price can change dramatically when only a small number of participants are willing to transact.

Buying and relisting assets can create additional market activity, but activity alone does not guarantee sustainable demand. CREDITSTR therefore represents an experiment in NFT market structure rather than proof that NFTs have become mature financial assets.

Its success will depend on whether genuine buyers emerge beyond speculative traders and whether the underlying Credits ecosystem maintains cultural relevance. The contrast with Bitcoin ETFs is revealing. Bitcoin is increasingly being absorbed into established financial infrastructure.

While NFTs are still searching for mechanisms that can create comparable liquidity and accessibility. Both developments nevertheless point toward the same broader trend: crypto markets are becoming increasingly financialized.

Bitcoin is moving deeper into portfolios through ETFs, while NFT entrepreneurs are experimenting with structures that treat digital collectibles as assets capable of being accumulated, priced and traded.

The next phase of crypto may therefore be less about creating entirely new asset classes and more about building financial infrastructure around the digital assets that already exist.

Airtel Money Plans London IPO as Africa’s $213bn Mobile Payments Business Targets Next Growth Phase

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Airtel Money is preparing to list on the London Stock Exchange, setting the stage for one of the most significant public-market debuts by an African digital financial services business as it seeks to expand beyond its existing customer base and deepen its position in the continent’s fast-growing mobile payments market.

The company, which operates across 13 African markets, said it plans to publish a prospectus in early October, with the final offer price expected in mid-October after an institutional book-building process. The proposed offering will involve only existing shares, meaning Airtel Money itself will not receive proceeds from the sale.

The company expects at least 10% of its shares to be in public hands following the listing. The International Finance Corporation has agreed to invest up to £67.2 million, or about $90 million, as a cornerstone investor, subject to the final offer price and other conditions.

The decision to sell existing shares rather than raise fresh capital is significant. Airtel Money says it is debt-free, capital-light and highly cash-generative, allowing it to pursue a public listing without relying on the IPO to finance its expansion.

“Today marks the start of a new chapter for Airtel Money as we announce our plans to list on the London Stock Exchange,” CEO Ian Ferrao said.

He said the business had reached approximately 53 million monthly active users and was entering the market with the financial capacity to fund its next stage of expansion.

The numbers underpinning the IPO are substantial. Airtel Money processed $213 billion in total payment value in the 12 months to June 30, 2026, while revenue reached $1.346 billion in the financial year ended March 2026. EBITDA stood at $676 million, giving the business an EBITDA margin of roughly 50%.

The company also reported a pre-tax cash conversion ratio above 90% in each of the past three financial years. Capital expenditure accounted for only 3% of revenue in the year ended March 2026, while Airtel Money said it had no external borrowings.

That combination of high margins, low capital requirements and strong cash generation is likely to be central to the investment case as Airtel Money approaches international investors. It also distinguishes the business from many younger fintech companies that continue to rely heavily on external funding to finance growth.

Airtel Network Provides A Large Expansion Opportunity

Airtel Money’s existing scale is only part of the growth story. The company said its 53 million monthly active users represented about 41% of Airtel Africa’s 128.9 million telecommunications subscribers. That leaves more than 75 million Airtel subscribers who are not currently using Airtel Money, creating a large potential customer pool without requiring the business to build an entirely new distribution network.

The relationship with Airtel Africa is particularly important because the telecom company remains Airtel Money’s founding shareholder and distribution partner. The mobile operator provides access to an established customer base, retail presence, and agent network across its markets.

Customer numbers have grown at a compound annual rate of 20% from March 2018 through June 2026. Total payment value has grown even faster, with dollar-denominated TPV increasing at a 33% compound annual rate over the same period.

The growth also shows how mobile money has evolved beyond basic person-to-person transfers in many African markets. Airtel Money has expanded into a broader digital financial services platform, allowing customers to make payments and conduct other financial transactions through mobile devices in markets where traditional banking infrastructure remains uneven.

For investors, the opportunity extends beyond the current 53 million users. Airtel Money is effectively seeking to monetize an existing telecommunications ecosystem while increasing the frequency and value of transactions among its customers.

The proposed London listing also gives the business access to an international investor base at a time when global investors have increasingly focused on African financial technology and digital payments. The IFC’s participation as a cornerstone investor provides an additional institutional endorsement of the offering, although the eventual valuation will depend on the price range and market conditions disclosed in the prospectus.

The IPO has been in preparation for several years. Airtel Africa had been exploring a listing of its mobile money operation since at least 2025, when Citi was reported to have been selected as lead adviser. Airtel Africa subsequently identified London as its preferred listing venue in July 2026.

The business has expanded rapidly during that period. Airtel Africa reported that Airtel Money generated $1.08 billion in revenue in the financial year ended March 2026, compared with $770 million a year earlier.

The proposed transaction nevertheless comes with an important limitation for investors: Airtel Money will not receive new capital from the IPO. The offering is therefore primarily a shareholder liquidity event and a mechanism for establishing a public market valuation rather than a direct funding exercise for expansion.

That could make the company’s ability to sustain its existing growth rate crucial after listing. With the business already generating substantial cash and carrying no external debt, management will need to demonstrate that further expansion can continue through its existing operating model while converting the large pool of Airtel Africa subscribers who have yet to adopt its financial services.

The prospectus will provide the market with the missing pieces, including the indicative price range, offer size and detailed financial and risk disclosures. Those details will determine how investors value a business whose growth profile is closely tied to Africa’s continuing shift toward digital payments.

However, Airtel Money is positioning the London IPO around a combination of scale, profitability and further room for penetration. Its 53 million users and $213 billion in annual payment value already give it substantial weight in African digital finance, while the much larger Airtel subscriber base provides a built-in avenue for expansion.

AI Investment Keeps Global Economy Afloat as Energy Shock Darkens 2027 Outlook – OECD

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Heavy investment in artificial intelligence infrastructure is helping the global economy remain more resilient than previously expected in 2026, but an increasingly persistent energy shock is threatening to weaken growth and keep inflation elevated into 2027, the Organization for Economic Co-operation and Development said on Wednesday.

The OECD raised its forecast for global economic growth this year to 2.9%, from 2.8% in its June outlook, although the pace would still represent a marked slowdown from the 3.4% expansion recorded last year.

The improvement is being driven in part by an investment cycle centered on AI, with companies continuing to spend heavily on data centers, semiconductors and related infrastructure. The OECD said that spending has become an important source of economic resilience, particularly in the United States, while also supporting technology exports from major Asian producers such as Japan and South Korea.

But the organization’s more cautious outlook for 2027 highlights the growing tension between the AI investment boom and a deteriorating energy environment.

Global growth is now projected at 3.0% next year, down from the 3.1% forecast in June. The downgrade is largely linked to the commodity price shock associated with the conflict in the Middle East, which is expected to weigh on household purchasing power, business costs and economic activity.

The OECD’s assessment points to an important question for the global economy: whether the strength of AI-related capital spending can continue to offset weakness elsewhere, particularly if energy costs remain elevated and financial conditions become more restrictive.

The organization warned that the outlook could deteriorate substantially if several risks materialize simultaneously. These include renewed energy-market volatility, extreme weather associated with a strong El Niño, rising government bond yields and weaker-than-expected returns from AI investment.

Taken together, those risks could reduce global growth by 0.7 percentage points next year while increasing global inflation by 1.1 percentage points, according to the OECD.

That risk has raised concerns because inflation is already proving more persistent than previously anticipated. The OECD raised its forecast for inflation across the G20 economies to 4.1% in 2026, from 4.0% in its June forecast. Its 2027 projection was increased much more sharply, to 3.6% from 3.1%.

The higher inflation outlook could complicate central banks’ path. If energy costs begin feeding into broader prices, monetary policymakers may have less room to reduce interest rates even as economic growth weakens. The OECD said central banks could be forced to adjust policy if price pressures broaden or economic activity deteriorates.

AI Investment Offsets Weaker Demand in The US

The United States remains one of the clearest examples of the economy being supported by the AI investment cycle.

The OECD raised its US growth forecast to 2.2% for 2026 and 2.1% for 2027, both higher than its previous projections. Heavy investment linked to AI is helping offset weaker consumer spending, providing a powerful source of demand at a time when households are facing higher costs.

The resilience, however, comes with a serious vulnerability. Much of the current investment boom is concentrated in a relatively narrow part of the economy, particularly technology infrastructure. Data centers, advanced chips and other computing infrastructure require enormous amounts of capital and energy. That means the economic benefits of the AI boom could weaken if companies begin questioning the returns from the enormous sums being committed to AI infrastructure.

US inflation is expected to reach 3.6% in 2026 before easing to 2.6% in 2027. The OECD said tariffs and higher energy prices are likely to put pressure on household purchasing power and increase costs for businesses.

This creates a difficult combination for policymakers. AI investment can support growth, but higher energy prices and trade costs can simultaneously push inflation higher.

China faces a different set of constraints. The OECD expects the world’s second-largest economy to grow 4.5% in 2026 and 4.2% in 2027, leaving its forecasts unchanged from June.

Beijing’s efforts to curb excess industrial capacity are expected to weigh on investment, while consumer spending is projected to recover gradually. The combination points to a Chinese economy increasingly dependent on domestic consumption as industrial investment faces tighter constraints.

Europe Faces A Sharper Energy Problem

The euro zone is expected to grow just 1.0% in both 2026 and 2027. Higher energy prices and interest rates are weighing on economic activity, although new defense spending initiatives are expected to provide some support.

Inflation presents a more immediate problem. The OECD forecasts euro zone inflation at 3.0% this year and 2.9% next year, well above the European Central Bank’s medium-term objective.

Natural gas prices are a particular concern. European gas storage levels are at 15-year lows heading into the winter heating season, leaving the region more exposed to further increases in energy costs.

The combination of weak growth and elevated inflation could limit the ability of policymakers to provide additional monetary support. If energy prices remain high for an extended period, the shock could also spread beyond headline inflation into transportation, manufacturing, food production and other parts of the economy.

Japan’s outlook is comparatively stable, with growth forecast at 0.8% in 2026 and 0.7% in 2027. Strong business investment is supporting activity, but higher policy rates and more expensive energy imports are expected to offset some of that strength. Japan also stands out because inflation is expected to accelerate rather than decline. The OECD projects inflation at 1.8% this year before rising to 2.6% in 2027, citing a tight labor market and strong wage growth.

Canada’s outlook has deteriorated more sharply. The OECD cut its 2026 growth forecast to 0.9% from 1.2% and reduced its 2027 projection to 1.3% from 1.7%, citing new US tariffs on Canadian exports.

The contrasting forecasts underline how uneven the global expansion has become. AI-related capital spending is providing a significant lift to some economies and industries, while energy costs, trade barriers, monetary tightening and weaker consumers are creating pressure elsewhere.

The central risk for 2027 is therefore not simply slower growth. It is the possibility that several shocks reinforce one another. A prolonged energy shock could raise inflation just as weaker demand reduces growth, while higher government bond yields could increase borrowing costs and put additional pressure on businesses and governments.

At the same time, the global economy is becoming increasingly reliant on whether the enormous investment in AI infrastructure ultimately translates into productivity gains and sustainable returns.

While the spending boom is currently helping prevent a sharper global slowdown, the OECD’s projections suggest, however, that AI investment alone may not be sufficient to shield the world economy from a prolonged energy shock, particularly if inflation remains elevated and financial conditions tighten further.

Why AI Risks Are Driving Interest in Cybersecurity Stocks

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The AI revolution in software development is beginning to reveal a contradiction at the heart of automation: the technology designed to make engineers more productive can also make the work feel less meaningful.

The same AI acceleration is creating a new investment narrative for cybersecurity, as companies and governments confront the possibility that more powerful artificial intelligence will also create more powerful security threats.

That tension was captured by a viral post from an anonymous software engineer using the X handle “v0xium.” The engineer described a new workplace where AI coding tools, particularly Claude Code, were generating product specifications, tests, tickets, reports and substantial portions of software.

The complaint was not simply that machines were writing code. It was that engineers were increasingly being measured by how quickly they could supervise automated production.

The post said employees were working 12 to 13 hours a day essentially “pressing enter,” while having less time to understand what was being built. The reaction exposed a deeper question about the economics of AI.

If software production becomes dramatically cheaper, companies can potentially build more products with fewer engineering hours. But productivity gains do not automatically translate into better work.

The engineer argued that corporate incentives around feature volume and shipping speed could turn AI from an assistant into an industrial production system in which human expertise becomes increasingly detached from the underlying technology.

That concern matters because software engineering has traditionally rewarded understanding: architecture, debugging, systems thinking and the ability to make trade-offs when requirements are ambiguous.

AI coding agents can accelerate many of those tasks, but the viral debate suggests that organizations still need people capable of reviewing outputs, identifying hidden failures and understanding the systems those agents create.

The productivity question is becoming inseparable from a governance question: who remains accountable when the machine produces most of the work?

Ironically, the cybersecurity industry may become one of the beneficiaries of precisely these fears. Société Générale has identified cybersecurity as a potential investment theme as concern about AI-related risks pushes spending beyond computing infrastructure toward protecting and governing AI systems.

The bank’s basket includes Palo Alto Networks, CrowdStrike, Cloudflare, Fortinet, Zscaler, CyberArk, Check Point Software, Okta, Gen Digital and Akamai Technologies.

Société Générale said earnings-per-share growth for the cybersecurity theme has compounded at roughly 16% annually since 2020, compared with 10% during the preceding decade.

It also noted that the basket’s forward price-to-earnings ratio was around 25, below its historical average of about 30.2. These figures describe the bank’s investment thesis, rather than guaranteeing future performance.

The connection between the two stories is increasingly difficult to ignore. As AI coding agents become more autonomous, the attack surface around software development could expand. More generated code means more code requiring validation.

More AI agents operating across repositories, credentials and production environments could create additional security considerations. And organizations deploying AI at scale will need systems capable of monitoring identity, access, vulnerabilities and anomalous behavior.

The result is an unusual AI feedback loop. Automation is transforming the role of the engineer while increasing the importance of cybersecurity expertise. The future of AI may therefore depend not only on how much software machines can produce.

But on whether humans retain enough technical understanding to verify, secure and govern what those machines build.

Artificial Intelligence and the Future of Financial Services

Artificial intelligence is moving from the margins of financial services toward the center of how institutions operate, compete and manage risk. Banks, insurers, asset managers and fintech companies have spent years experimenting with machine learning, generative AI and automated decision systems.

Yet experimentation is proving easier than transformation. The difficult question is no longer whether financial institutions can use AI, but whether they can deploy it at scale without compromising trust, security or financial discipline. The opportunity is substantial.

AI can process enormous volumes of financial information, identify patterns that humans may overlook and automate repetitive work. In banking, this can mean faster fraud detection, more sophisticated credit assessment, personalized customer services and automated compliance processes.

Asset managers can use AI to analyze market data, corporate disclosures and alternative datasets. Insurers can apply similar technologies to underwriting, claims processing and risk assessment.

Generative AI has expanded the opportunity further by making sophisticated analytical tools accessible through natural language. Employees can potentially summarize documents, generate reports, search internal knowledge and interact with complex datasets without relying entirely on specialized technical teams.

This could reduce administrative costs while allowing professionals to devote more time to decisions requiring judgment. But financial institutions face a fundamental scaling problem. A successful pilot does not automatically become a reliable enterprise system.

An AI model that performs well in a controlled environment can encounter very different conditions when connected to millions of customers, legacy technology and constantly changing financial data. Institutions therefore need infrastructure capable of supporting AI securely and consistently across business units.

Data is central to this challenge. Financial AI depends on high-quality, accessible and appropriately governed information. Fragmented databases, inconsistent definitions and outdated technology can undermine even the most sophisticated model.

Building a scalable AI strategy consequently requires investment in data architecture, cloud infrastructure, cybersecurity and application programming interfaces alongside investment in the models themselves.

The economics of AI also demand greater discipline. Financial executives cannot simply count the number of AI projects launched.

They need measurable outcomes. Does an application reduce processing time? Does it lower fraud losses? Does it improve customer retention? Does it increase employee productivity without creating additional operational risk? These questions turn AI from a technology experiment into an investment decision.

Risk management becomes equally important as deployment expands. AI systems can produce inaccurate outputs, inherit biases from training data, expose confidential information or become vulnerable to manipulation. In highly regulated financial markets, an institution must also be able to explain how important automated decisions are made and establish accountability when systems fail.

This means governance cannot be treated as an obstacle to innovation. Clear human oversight, model validation, access controls, audit trails and continuous monitoring can become part of the infrastructure that makes large-scale adoption possible.

The objective is not necessarily to eliminate human involvement, but to determine where humans remain essential and where machines can safely perform routine tasks. The competitive landscape is likely to reward institutions that combine technological ambition with organizational discipline.

AI adoption will increasingly involve partnerships among executives, engineers, data scientists, compliance professionals and frontline employees. Institutions that treat AI solely as an IT project may struggle to capture its broader economic value.

The transformation of financial services through AI will not be determined by who adopts the most advanced model first. It will depend on who can integrate AI into real business processes while maintaining reliable data, measurable economics, strong governance and customer trust.

The next phase is therefore less about experimentation and more about execution. AI’s lasting impact on finance will emerge when institutions turn promising demonstrations into dependable infrastructure.