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Artificial Intelligence (AI), we are facing nowadays, is the ultimate end result of the IT revolution started last century. The IT revolution, in its new shape, started by youngsters creating new tech companies like Google, Facebook, LinkedIn, X and new others are in the pipeline. All of them, opened new routes enabling technology to expand endlessly.From this expansion comes the birth of the 4th industrial revolution based on artificial intelligence. The emergence of AI plays great role in new inventions engineered to enable machines to undertake tasks in replacement of humans. This could be a problem, as the intelligence of humans will be replaced by machines and could end-up in bundles of idle people.Therefore, need arises for certain regulations to cater for new human resources strategies.Many entities including ILO, raised concerns regarding the “machine” or the new job masters and their strong competitive privilege.Robots are used to help in medical operations, counter staff, TVs presenters, inspectors or fixers of tiny items in industries. Some are utilised in dangerous duties including detection of mines or hidden metals or poisonous liquids in the high air or the bottom of oceans. What alerts, is the role of robots in medical surgeries or dealing directly with the body of humans. Irrespective of such role, any mistake is irreparable and could happen.Another example is drones used to achieve many normal and sensitive duties in replacement for humans. Drones, are to deliver risky and non-risky items, photography, videos and others. Also, they are used in military purposes.There are many risks, as drones may detour and go to a different place or while going to the designated area, they may trespass others property without consent or even knowledge. The role of drones, is of interest due to fast services and other reasons, however, there is genuine need to preserve the interests and safety of all.Now e-cars are crossing streets to serve others in replacement for bus or taxi drivers or other commuters. The new e-cars are self-driven and their artificial mind is working under certain technologically prepared program for driving. E-cars could give a cheap, fast, confidential service, however, there are risks to passenger, items, pedestrians and others.The 4th industrial revolution, will rely on machines and give them the necessary artificial intelligence to be able to do work. Earlier we used to control machines. Now, it appears that time has come for machines to control humans or work by themselves.Here comes the risk, as machines are deaf, ductless and lack senses and due to this they could be dangerous. E-development is the new spirit that we need, however, to achieve better, secure, save and sustainable results, there is strong need to legislations.The new legislations are not to handicap the AI or other e-development nor to curb or distract new inventions. Rather, it should open the way and prepare the legal infrastructure for e-development to grow, blossom and breed all products for the benefit of humans. The law could possibly walk along, hand in hand, with new IT technologies and such marriage will certainly give legitimate off-spring for all e-future technologies including AI.Dr AbdelGadir Warsama Ghalib is a corporate legal counsel. Email: [email protected]

The development of increasingly powerful models is central to the unfolding AI revolution. But this revolution has a second, equally important component: the adaptation and adoption of AI models across the economy, both to lower the cost of existing products and services, and to create new or improved products and services capable of advancing economic and social development. Whereas model development is happening largely in the United States and China, diffusion can and must take place everywhere.Overall, AI will follow a J-curve pattern. At first, there is a huge amount of investment – in areas like physical infrastructure, software, business-model adaptation, data consolidation, and human-capital development – which does not yield immediate benefits. During this period, there is downward pressure on productivity, broadly defined to include benefits not measured by conventional national income accounts.Then the technology’s value-creation potential kicks in, and the curve slopes upward. Since we haven’t yet reached this point, it is impossible to say exactly what this upswing will look like – the J-curve’s height and slope. By and large, investors seem to be betting on a massive payoff, but a distinct sense of uncertainty still permeates discussions about AI, and some predict that the technology will fall short of expectations, leading to a bust. Who turns out to be right will depend far more on diffusion than development.So far, AI diffusion has been uneven, with some sectors (especially technology, finance, and professional services) embracing the technology, and others (including large-employment sectors like health care and construction) lagging behind. While such disparities are not surprising at this point, their persistence would lead to a flatter J-curve, representing muted returns on today’s investments and delays in growth and productivity gains. Put differently, whether or not we currently have an AI investment bubble will be largely determined by the pattern and speed of diffusion in the next few years.Diffusion happens through multiple channels, the fastest of which is arguably software-as-a-service (SaaS) providers. Providers such as Google Search, Microsoft Office, Copilot by Notion, Salesforce, and Adobe are already embedding AI into their offerings. AI can also be incorporated into scientific processes relatively quickly. And with the major developers of large language and multimodal models providing application programming interfaces (APIs) that allow for the quick creation of tailored AI models, progress may pick up in other areas.Open-source models – so far seen more in China than in the US – create even more opportunity, because they enable increased specialization and competition, including from smaller firms and countries that lack the massive computing infrastructure needed for the largest models. But there are still barriers to entry: a reliable electricity supply, robust computing capacity, and accessible mobile-internet connectivity are prerequisites to broad adoption.Trade – especially of inputs like advanced semiconductors – also makes a difference. So does human capital: from advanced AI engineering and high-level strategic management to user-related skills, an economy needs to ensure access to an array of capabilities through education, reskilling, and labor mobility. The final piece of the puzzle is data. Where data systems are fragmented, incomplete, inaccurate, or inaccessible, training effective models will be slow, at best.While AI diffusion depends significantly on private-sector initiatives, policy frameworks and regulatory structures also matter. China’s leaders understand this. As Huawei founder Ren Zhengfei recently observed, China has adopted a practical approach aimed at using AI to address real-world development and economic challenges. So, while developing increasingly capable large models is a high priority, so is deploying AI broadly, in order to secure the rapid gains in service quality, efficiency, and productivity that will be needed to offset the effects of rapid population aging.China’s government is actively directing innovators toward these outcomes. Beyond encouraging the large tech platforms to build open-source models, China’s government has tasked them with developing or enabling applications in specific sectors, such as autonomous driving, health care, robotics (in manufacturing and logistics), supply-chain management, and green technologies. China’s government also regularly sponsors developer conferences and competitions.Such efforts have paid off. For example, China accounts for over 30% of total global manufacturing output. In 2024, China accounted for 54% of all robot installations globally. The country now boasts almost half the world’s installed robots – at just over 2mn. Relative to the US, China’s policy framework is much more engaged and geared toward providing direction with respect to applications and adoption across sectors in the economy. By contrast, US tech giants and well-funded AI startups are pushing the boundaries of large models, often in pursuit of artificial general intelligence and artificial superintelligence. While diffusion channels are open, their use is being left largely up to the private sector.That may work in a few sectors, like tech, finance, and professional services, with the resources and know-how to experiment and then adopt. But private actors alone are unlikely to address the factors inhibiting AI adoption in specific sectors, such as data fragmentation, capacity deficiencies, regulatory hurdles, and scale problems. The likely – and unnecessary – result is a two-speed pattern of diffusion, leading to subpar economic growth, negative distributional outcomes, and the erosion of the economic underpinnings of national security.When it comes to defence, the US government has long recognized that some state guidance is appropriate to ensure that private-sector innovation advances public goals. AI diffusion demands a similar approach. Something like this hybrid, active, pragmatic, and sector-specific approach is needed across a wide swath of the economy. Failure to do so will result in subpar economic growth, problematic distributional outcomes, and a weakening of the economic underpinnings of national security.When it comes to diffusion, watching, waiting, and hoping is not a strategy. —Project Syndicate(Michael Spence, a Nobel laureate in economics, is Emeritus Professor of Economics and a former dean of the Graduate School of Business at Stanford University and a co-author (with Mohamed A El-Erian, Gordon Brown, and Reid Lidow) of Permacrisis: A Plan to Fix a Fractured World (Simon & Schuster, 2023)). 

By conventional indicators, the valuation of technology stocks has risen this year to take them into bubble territory. Valuations that reached multiples of forward earnings scarcely seen before indicated that they were priced for perfection. So a fall in valuations since mid-October was not a surprise.This dip may reflect caution and profit-taking. It may presage a bigger fall, or it may be a pause in a long bull market accompanying the AI revolution. The indicators are not all pointing in the same direction.So far, the stock market slide is just a correction. Markets fell in the first week of November. They nudged upwards in the week commencing Monday 10th, but then fell at the end of the week. The market as a whole remains at elevated levels compared with April, when there was a drop associated with President Donald Trump’s announcement on tariffs. To take just one example: Nvidia fell around 10% in the first week of November, but it was still around 60% higher than it was just six months earlier. The S&P 500 dipped to 6,700 on 14 November, which compares with a high of 6,920 but a low of 4,835 over the previous 12 months, and remains nearly 70% higher than November 2022. The dominance of large technology companies in aggregate market valuations has become pronounced. By the end of October, while the S&P had risen through most of the year, during that period some 397 of the stocks actually fell in value. Eight of the 10 biggest stocks in the S&P are tech firms. They account for 36% of the entire US market value, and 60% of the gains since April.Palantir Technologies, a business applications software specialist, reached a peak valuation of 230 times future earnings. In early November, it was revealed that prominent hedge fund manager Michael Burry took a $912mn position against Palantir, whose stock has fallen from over $200 per share to around $170, though remains more than 150% higher over the year. Mr Burry later closed his hedge fund Scion Asset Management.There has been an uneven pattern to the sell-off, following earnings reports in late October. Meta, the owner of Facebook, fell 12% over concern of its high investment levels in AI, given disappointing returns from its investment in virtual reality, though has recovered slightly. Alphabet, the owner of Google, rose 3%, though has since dipped by around 5%, and Microsoft fell by just 3%, then fell further before a partial recovery.Unlike the dotcom start-ups of 25 years ago, the tech firms have strong revenues and a sound business model. Their services extend far beyond AI, covering business application software and cloud computing. In the case of Amazon, it is a general retailer as well as a tech firm. A strong argument is that much of the investment in AI is from large, profitable companies with a strong cash position.The hyper-scalers, Amazon, Meta, Alphabet and Microsoft, all have strong underlying global businesses. The scale of the investment in data centres being planned has caused some investors to be concerned, however. Some tech firms have been issuing bonds; for example, in late October Meta announced a $25bn bond issuance to finance AI investment, following Oracle’s $18bn bond sale in September. In early November, yields on big tech firms’ bonds started to rise. Oracle’s stock suffered bigger falls in the middle of November, with investors concerned over debt, heavy reliance on OpenAI, negative free cash flow. It emerged that the outgoing CEO Safra Catz sold $2.5bn of Oracle shares this year.Also, 10 loss-making AI specialist start-up companies have between them been valued at nearly $1tn, while there have been patterns of circular financing, especially concerning OpenAI.Another dimension is that there is softening economic data from the wider economy. With the US government lockdown entering its second month, there has been no official jobs data since 5 September. Analysts and economics have been relying on private sources. Data from the private company Challenger, Gray and Christmas showed the highest level of October job lay-offs since 2003, while the payroll company ADP reported that US companies shed 32,000 jobs in September, the biggest fall in two and a half years.Earnings from mainstream businesses have disappointed. The stock of the popular restaurant chain Chipotle fell 13% in late October following disappointing quarterly results.The price of bitcoin has been unpredictable in recent weeks. It often rises in a counter-cyclical manner, increasing as stock market falls, but cryptocurrencies generally were off their highs at the time of the wider market correction. Bitcoin fell from around $125,000 on 7 October to just below $100,000 by mid-November. Many bitcoin investors are leveraged, and some forced, automated sales are likely to have occurred, accelerating and drop in price. Gold has fallen from a high of $4,400 per ounce to around the $4,100.The AI investment industry is one of the few sectors to be registering growth, so if technology firm leaders fall short of their ambitions, the impact would ripple outside the industry.Against that, the bearish commentators point to the relatively narrow foundation of asset price investment, and debt and macro-economic fragility in the higher-tariff era, a combination that compares unfavourably with the more benign macro-economic picture of 2000-2001.The emerging technology of AI comes during an extended period of cheap money and globalisation, including of retail investing, and rapid growth of private credit. The worldwide exposure of investors to US stocks are part of a highly inter-connected system. High levels of government debt limit the extent of any fiscal stimulus following a shock. A loss in market value of the same proportion as the dotcom crash would have a far bigger impact on the real economy. The economist Gita Gopinath has estimated that it would cause a loss of $20tn for US households, or 70% of GDP. Foreign investors might lose $15tn.There are two dimensions to risk assessment: Likelihood, and impact. The likelihood of an asset price collapse that is equivalent in proportional terms to that of the dotcoms would not appear to be high, although it is possibility. The impact would be seismic, and felt across the global economy.The author is a Qatari banker, with many years of experience in the banking sector in senior positions.

Qatar National Bank (QNB) said that rare earths were key to the electronics and digital revolution and are becoming even more important as new industries and technologies emerge. In its weekly economic commentary, the QNB added that AI, semiconductors, defense and aerospace, as well as energy transition are becoming some of the most strategic sectors for the 21st century and should require massive growth in rare earth supply. This further strengthens China's dominant position in these supply chains and creates bottlenecks as well as vulnerabilities to the US and other competitors. US-China strategic competition is set to be one of the major drivers of the global economy in the years to come."In recent weeks, disputes over export controls of rare earth-related supply chains almost led to a major escalation of US-China trade conflicts," it said.Despite their name, rare earth elements are not particularly rare in the Earth's crust. The challenge lies in their extraction and refining, which are technologically complex, environmentally sensitive, and capital intensive.The group includes 17 elements such as neodymium, dysprosium, terbium, cerium, lanthanum, and yttrium, each with unique magnetic, optical, or catalytic properties that make them indispensable for modern industry. In addition, several related critical minerals, including gallium, germanium, indium, cobalt, and lithium, play similar roles across supply chains.Together, the importance of these materials can be seen most clearly in three of the most important, rapidly expanding sectors in the world. In the field of AI and semiconductors, rare earths are integral to the machinery and processes that make advanced chips possible. Cerium oxide is used to polish silicon wafers with nanometric precision, yttrium is a core component of plasma etching systems, and neodymium-based magnets power the high-efficiency cooling and motor systems used in AI data centers. Meanwhile, related elements such as gallium and germanium are used directly in high-performance chips and optical communications.In defense and aerospace, other rare earths are key inputs for jet engines, radar systems, and precision-guided weapons. Finally, in the energy transition, rare earths like neodymium and paraseodyum are essential for the powerful magnets that make EVs and wind turbines operate efficiently, while lanthanum and cerium play crucial roles in catalytic converters and energy storage technologies.The exponential growth in demand has transformed rare earths and critical minerals from industrial commodities into strategic assets. This growing importance has also created new geopolitical frictions, largely because production and processing capacity are highly concentrated in a few countries, particularly China.In a now-famous remark, former Chinese leader Deng Xiaoping observed in 1987 that "The Middle East has oil; China has rare earths."China invested heavily in geological surveys, mining, and refining technology. By the early 2000s, China had become the dominant player in nearly every stage of the supply chain. Today, it accounts for around 65 percent of global mining output but over 85 percent of global refining and processing capacity. It also produces the majority of the permanent magnets and other high-value downstream products that depend on these materials. The country has also invested in expanding its footprint across the industry overseas, controlling significant assets, resources and reserves even outside China.In 2021, Beijing consolidated several state-owned companies into the China Rare Earth Group, strengthening its control and coordination over the sector. More recently, China has introduced export controls for national security reasons. Officials have emphasized that these controls are not outright bans but measures to ensure "responsible and secure" trade in dual-use goods. Nonetheless, these steps have reinforced perceptions that China views control over critical minerals as an important element of its broader geopolitical toolkit.In response, other countries have moved to diversify supply chains and reduce strategic dependence. The US has classified rare earths as critical to national security and is investing in domestic mining and processing through the Defense Production Act. However, these efforts will take years to bear fruit.