24 September 2026 1:46 am
CMU-Q Grads Stay Behind to Build Qatar’s Future

CMU-Q Grads Stay Behind to Build Qatar’s Future

Jun 26, 2026

This modern architectural passageway in Doha, Qatar, showcases unique designs and vivid colours.  by Natalya Rostun via Pexels. CMU-Q Grads stay behind and go through it to build their own future and contribute to building Qatar’s Future

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‘95% of CMU-Q grads stay behind to build Qatar’s knowledge economy’

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Peter Alagos
CMU-Q Grads Stay Behind to Build Qatar's Future
Carnegie Mellon University in Qatar (CMU-Q) dean Michael Trick

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The country’s push towards a knowledge-based economy is gaining measurable ground, with nearly 95% of the university’s recent graduates choosing to remain and work in Qatar, according to Carnegie Mellon University in Qatar (CMU-Q) dean Michael Trick.

Trick told Gulf Times that building such an economy requires a full ecosystem rather than isolated components, and that Education City, Qatar’s universities, and CMU-Q itself collectively function as a talent pipeline for that transition.

“We now have two decades of graduates from CMU-Q, most of whom stay in Qatar and contribute to the knowledge economy. When we attend conferences, summits, and industry events within Qatar, we are proud to see so many of our alumni attending, presenting, networking, and leading the conversations,” Trick said.

He said the primary remaining gap is not structural but time-related, noting that a transformation of this scale cannot happen overnight.

Asked about the sectors most ripe for disruption in the next five years, Trick said any discussion on disruption must start with artificial intelligence (AI), which he described as capable of fundamentally transforming industries through the optimisation of core business processes.

He said Qatar is already seeing innovation-led disruption across education, energy, food security, and sports. “In the area of sports and large-scale sporting events, Qatar has the potential to lead globally through technological innovation,” Trick noted.

On whether Qatar is producing enough homegrown entrepreneurial talent, Trick distinguished two models for meeting workforce demand. The first, he explained, involves paying foreign firms a premium to set up a temporary presence — a model that was essential when Qatar’s educational system could not yet keep up with demand. The second model, which he described as more organic, is education-led.

“Through significant investments in higher education, Qatar attracts exceptional minds, both Qatari and international. Our students are exceptionally gifted, and often the top students in their schools and home countries,” he said.

Trick said international students who come to study in Qatar often choose to stay after graduating, drawn by four years of growing attachment to the country. “They want to stay, and they hope to make a lasting economic impact,” he said.

He added: “This second model takes longer, but it is a more organic approach: introduce brilliant young people to a country that values the development of human capital, foster their connection to Qatar, encourage their entrepreneurial aspirations, and allow them to build their futures here.”

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Transforming the Middle East’s Energy Landscape with AI

Transforming the Middle East’s Energy Landscape with AI

Jun 21, 2026

Rooftop air conditioning units on a gravel surface with a mountainous backdrop under a clear sky. by PURPLE24 via Pexels

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Intelligent sustainability and resilience: Transforming the Middle East’s energy landscape with AI

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ME PoV Spring 2026 issue

Deloitte – 21/06/2026

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Intelligent sustainability and resilience
Download PDF (358KB)View PDF

Navigating complexity in the Middle East energy sector

The Middle East holds a unique and influential position in the global energy landscape. Long regarded as the world’s hydrocarbon powerhouse, the region now stands at a strategic crossroads. National visions across the Middle East are increasingly focused on diversifying energy portfolios, enhancing resilience, and embedding sustainability at the heart of energy systems. These ambitions signal a deliberate and strategic approach toward balancing economic growth, societal value, and environmental responsibility.

Recent geopolitical disruptions, such as the closure of the Strait of Hormuz, have exposed vulnerabilities in regional energy supply chains. At the same time, global climate commitments and sustainability mandates are redefining energy strategies. Challenges such as water scarcity, extreme heat, and the pressures of integrating renewable energy sources are adding further complexity. Modernizing transmission grids and improving energy efficiency are now urgent imperatives to ensure stable, adaptive, and secure supply.

Meeting these demands calls for innovative solutions capable of safeguarding operational continuity, reducing environmental impact, and supporting economic diversification. Artificial intelligence (AI) is emerging as a powerful enabler in this transformation. By harnessing AI-driven insights and technologies, Middle Eastern governments and energy companies can optimize operations, strengthen resilience, and accelerate the transition toward intelligent, adaptive energy systems that deliver value across economic, social, and environmental dimensions.

How can leaders harness AI to build resilience amid geopolitical uncertainty?

The recent disruptions in the Middle East region highlighted the challenges GCC oil and gas companies face in maintaining smooth operations amid geopolitical uncertainty, emphasizing the potential value of adaptive and resilient operational strategies. To navigate these complexities, leaders should consider prioritizing the deployment of scalable AI platforms that integrate diverse real-time data, from vessel locations and port capacities to shifting geopolitical risks, enabling dynamic rerouting of logistics, and supporting uninterrupted supply flows despite disruptions.

Investments should focus on modular AI models that enhance demand forecasting, inventory optimization, and operational scenario simulation. AI-driven demand forecasting, which combines data on energy consumption, refinery outputs, storage levels, and socio-economic factors, can provide accurate short-term predictions. This capability will help companies adjust production and distribution swiftly, reducing the risk of costly overproduction or stockouts during volatile market conditions.

AI-powered inventory management can monitor stock levels across refineries, terminals, and distribution hubs, recommending optimal allocation and replenishment schedules to prevent shortages and maintain continuity. Its ability to simulate disruption scenarios, such as partial port closures or workforce constraints, assists operators in prioritizing asset utilization, balancing efficiency with financial prudence.

Leaders should also explore investing in upskilling their workforce to interpret AI insights and fostering collaboration between IT and operational teams to facilitate seamless integration. Establishing ethical and transparent AI governance frameworks is equally important for building trust and ensuring compliance.

Pilot projects demonstrating quick wins, such as AI-enabled logistics rerouting or predictive maintenance, should be prioritized to build momentum and validate AI’s value. Strengthening cybersecurity to protect operational data remains additionally essential.

By considering these approaches, Middle Eastern energy companies have the opportunity to transform AI into a core enabler of intelligent, sustainable energy systems that are resilient to geopolitical shocks and aligned with economic and environmental objectives.

Advancing sustainability through intelligent energy systems

While operational resilience is vital, the Middle East’s energy sector is also undergoing a profound transformation driven by sustainability imperatives.

National strategies such as the UAE’s Energy Strategy 2050 and Saudi Arabia’s Circular Carbon Economy framework exemplify the region’s commitment to reducing emissions, expanding renewable capacity, and improving resource efficiency. These frameworks reflect a growing recognition that sustainability is central to energy sector success.

AI technologies are instrumental in realizing these goals. For example, AI-driven predictive analytics improve renewable energy forecasting by accurately anticipating solar and wind generation patterns. This reduces reliance on fossil fuels, enhances grid stability, and facilitates higher renewable penetration.

Beyond energy generation, AI supports efficient water management, critical in the arid Middle East, by optimizing usage and reducing waste. It also plays a key role in carbon capture and storage initiatives by helping monitor and manage emissions more effectively.

Importantly, AI enables data-driven policymaking and investment decisions. By providing granular insights into emissions, energy consumption, and economic impacts, AI helps design sustainability initiatives that are both impactful and economically viable. Practical applications such as predictive maintenance and digital twins reduce downtime and operational costs. AI-driven scenario modeling equips policymakers to evaluate trade-offs and benefits of different energy mixes and infrastructure investments, further cementing AI’s role as a strategic enabler of intelligent sustainability.

The era of intelligent sustainability: Integration, innovation, and inclusive growth

The energy sector is entering an era of intelligent sustainability, characterized by the seamless integration of advanced technologies, especially AI, that enable real-time optimization of complex energy systems. AI transcends operational efficiency to become a strategic enabler that balances energy security, emissions reduction, and economic diversification.

The success of this transformation depends on fostering ecosystems that promote innovation, collaboration, and transparency. Intelligent sustainability requires not only technological adoption but also a cultural shift towards data-driven governance and inclusive stakeholder engagement. This holistic approach ensures energy systems that are resilient, equitable, and aligned with long-term national and global goals.

Workforce development and community engagement are evolving to become more inclusive and scalable. AI facilitates workforce transitions by identifying skills gaps and tailoring training programs, helping to prepare employees for new roles in a green economy. Simultaneously, AI enhances community engagement through transparent reporting and impact measurement, ensuring that the benefits of the energy transition are broadly shared and contribute to social stability alongside economic progress.

Forward-looking organizations are already investing resources to equip their employees and the communities they work with on green skills and technology, recognizing the interconnection between the two. An example of this future-focused investment is Deloitte’s Green Skills for a Green Economy training program, developed in collaboration with the Institute of Sustainability and Environmental Professionals (ISEP), which has equipped to date thousands of corporate and academic professionals in the Middle East, with the skills needed to thrive in the transition to a more sustainable future.

A call to leadership: Seizing the AI-driven opportunity for sustainable energy transformation

The next five years will be critical for the Middle East’s energy sector. Integrated AI-driven solutions will play an essential role in balancing energy security, sustainability, and economic diversification.

According to the 2025 Deloitte Global C-suite Sustainability Report, 83% of global executives increased sustainability investments in the preceding year. Leaders recognize the business case and related benefits from corporate sustainability efforts, with revenue generation being the most frequently cited business benefit across a range of sustainability actions. Furthermore, AI and data analytics are emerging as central tools to meet emissions targets and boost operational efficiency.

For Middle Eastern economies, this presents a transformative opportunity: leveraging AI to optimize energy production and consumption, strengthen grid stability, reduce carbon intensity, and enable more agile and intelligent energy systems. Technologies like predictive maintenance, digital twins, and AI-driven scenario modeling are already reducing downtime, lowering costs, and enabling smarter policymaking.

However, technology alone will not determine success. Organizations that lead in this new era will be those that embed AI strategically across their operations, invest in digital infrastructure and workforce capabilities, and establish strong and ethical governance.

The time to move from ambition to action is now. Those who lead this transformation will not only secure their organizations’ competitive advantage but also position the Middle East as a global exemplar of intelligent sustainability in energy, delivering lasting value for their nations and the world.

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By Amro Suleiman, Director, Technology & Transformation, Artificial Intelligence & Data and Rybale Al Hage, Senior Manager, Corporate Responsibility & Sustainability Lead, Deloitte Middle East 

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Riyadh Metro Finally: The Snøhetta Station

Riyadh Metro Finally: The Snøhetta Station

May 18, 2026

Cityscape of Riyadh with busy streets and modern skyscrapers on a sunny day. by Fahad Puthawala via Pexels

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Riyadh finally has a metro, and its symbol is the Snøhetta station

The new Riyadh metro station is set to transform the Al-Qiri transport hub. Designed by Snøhetta, it features a large reflective canopy, bright underground spaces, and an underground garden intended to serve as a new urban public square. Snøhetta talks to Domus about the project.

This article was previously published in Domus 1112, May 2026.
As one of four main hubs in the Saudi capital’s new metro system, connecting two of the main metro lines, the station in the his toric Al-Qiri district is designed as an open urban and pedestrian plaza with a large stainless steel canopy that acts as an urban periscope.

The station levels are visually linked by the mirror-like overhang structure that reflects the outside inwards and the inside outwards, while also directing natural light into the underground station and providing shade to the surrounding public areas.
Qasr Alhokm Metro Station, Snøhetta, Riyadh, 2025. Photo Iwan Baan
The steel canopy serves as the focal point and marks the station’s main entrance. The supporting steel space frame allows the canopy to extend above and beyond its base to form a massive cone wall. Beneath ground level, the sloping interior walls are finished with a rendered surface inspired by the ar ea’s traditional architecture. Acting as both a unifying architectural element and a point of orientation within the building, the steel canopy also reflects indirect sunlight down wards from its mirror-like surface.

 Designed to create subtle glimpses between the different sections of the station, the patterned openings – formed by 326 tri angular carvings in three different sizes – al so filter light gently into the atrium.

When passengers step off a train and look up, they see a 360-degree view of the ur ban landscape reflected on the underside of the canopy, giving them an immediate pic ture of where they are in the city. Likewise, people arriving from the city can look up to the canopy and see a mirrored reflection of everything happening below. The two metro lines traverse the open space within trans parent tubes, creating a striking visual pres ence and enhancing wayfinding throughout the station. The platforms are also each en capsulated within glazed tubes that pro trude into the atrium void, allowing a seam less integration between interior and exte rior, and opening the platform areas to the grandeur of the atrium for both arriving and departing travellers.
Qasr Alhokm Metro Station, Snøhetta, Riyadh, 2025. Photo Iwan Baan
At the base of the atrium, at around 35 metres below city level, an accessible garden helps to maintain a temperate environment even during the hot summer periods. Wa ter for irrigation is collected from the paved plaza areas and canopy above. The new pla za and garden further strengthen the public realm, providing valuable shared spaces for the nearby communities.With respect for the station’s historic setting, the inner atrium walls are adorned with a window-cut pattern inspired by tradi tional Najdi motifs, echoing the architectur al character of the surrounding neighbour hoods.
Qasr Alhokm Metro Station, Snøhetta, Riyadh, 2025. Photo Iwan Baan
Designed to create subtle glimpses between the different sections of the station, the patterned openings – formed by 326 tri angular carvings in three different sizes – al so filter light gently into the atrium.
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Read also: Domus 1112 hits the shelves
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Mustafa Suleyman: AI Development Will Keep Advancing

Mustafa Suleyman: AI Development Will Keep Advancing

May 3, 2026

A man working on website design and coding at a home office with a dual monitor setup. by Lisa from Pexels via pexels

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Mustafa Suleyman: AI development won’t hit a wall anytime soon—here’s why

The compute explosion is the technological story of our time. And it is still only just beginning.

April 8, 2026
Mustafa Suleyman: AI Development Will Keep Advancing

An abacus, a slide rule, a calculator, an old desktop computer and a GPU shown along a line of progress

Courtesy of Microsoft

We evolved for a linear world. If you walk for an hour, you cover a certain distance. Walk for two hours and you cover double that distance. This intuition served us well on the savannah. But it catastrophically fails when confronting AI and the core exponential trends at its heart.

From the time I began work on AI in 2010 to now, the amount of training data that goes into frontier AI models has grown by a staggering 1 trillion times—from roughly 10¹⁴ flops (floating-point operations‚ the core unit of computation) for early systems to over 10²⁶ flops for today’s largest models. This is an explosion. Everything else in AI follows from this fact.

The skeptics keep predicting walls. And they keep being wrong in the face of this epic generational compute ramp. Often, they point out that Moore’s Law is slowing. They also mention a lack of data, or they cite limitations on energy.

But when you look at the combined forces driving this revolution, the exponential trend seems quite predictable. To understand why, it’s worth looking at the complex and fast-moving reality beneath the headlines.

Think of AI training as a room full of people working calculators. For years, adding computational power meant adding more people with calculators to that room. Much of the time those workers sat idle, drumming their fingers on desks, waiting for the numbers to come through for their next calculation. Every pause was wasted potential. Today’s revolution goes beyond more and better calculators (although it delivers those); it is actually about ensuring that all those calculators never stop, and that they work together as one.

Three advances are now converging to enable this. First, the basic calculators got faster. Nvidia’s chips have delivered an over sevenfold increase in raw performance in just six years, from 312 teraflops in 2020 to 2,250 teraflops today. Our own Maia 200 chip, launched this January, delivers 30% better performance per dollar than any other hardware in our fleet. Second, the numbers arrive faster thanks to a technology called HBM, or high bandwidth memory, which stacks chips vertically like tiny skyscrapers; the latest generation, HBM3, triples the bandwidth of its predecessor, feeding data to processors fast enough to keep them busy all the time. Third, the room of people with calculators became an office and then a whole campus or city. Technologies like NVLink and InfiniBand connect hundreds of thousands of GPUs into warehouse-size supercomputers that function as single cognitive entities. A few years ago this was impossible.

These gains all come together to deliver dramatically more compute. Where training a language model took 167 minutes on eight GPUs in 2020, it now takes under four minutes on equivalent modern hardware. To put this in perspective: Moore’s Law would predict only about a 5x improvement over this period. We saw 50x. We’ve gone from two GPUs training AlexNet, the image recognition model that kicked off the modern boom in deep learning in 2012, to over 100,000 GPUs in today’s largest clusters, each one individually far more powerful than its predecessors.

Then there’s the revolution in software. Research from Epoch AI suggests that the compute required to reach a fixed performance level halves approximately every eight months, much faster than the traditional 18-to-24-month doubling of Moore’s Law. The costs of serving some recent models have collapsed by a factor of up to 900 on an annualized basis. AI is becoming radically cheaper to deploy.

The numbers for the near future are just as staggering. Consider that leading labs are growing capacity at nearly 4x annually. Since 2020, the compute used to train frontier models has grown 5x every year. Global AI-relevant compute is forecast to hit 100 million H100-equivalents by 2027, a tenfold increase in three years. Put all this together and we’re looking at something like another 1,000x in effective compute by the end of 2028. It’s plausible that by 2030 we’ll bring an additional 200 gigawatts of compute online every year—akin to the peak energy use of the UK, France, Germany, and Italy put together.

What does all this get us? I believe it will drive the transition from chatbots to nearly human-level agents—semiautonomous systems capable of writing code for days, carrying out weeks- and months-long projects, making calls, negotiating contracts, managing logistics. Forget basic assistants that answer questions. Think teams of AI workers that deliberate, collaborate, and execute. Right now we’re only in the foothills of this transition, and the implications stretch far beyond tech. Every industry built on cognitive work will be transformed.

The obvious constraint here is energy. A single refrigerator-size AI rack consumes 120 kilowatts, equivalent to 100 homes. But this hunger collides with another exponential: Solar costs have fallen by a factor of nearly 100 over 50 years; battery prices have dropped 97% over three decades. There is a pathway to clean scaling coming into view.

The capital is deployed. The engineering is delivering. The $100 billion clusters, the 10-gigawatt power draws, the warehouse-scale supercomputers … these are no longer science fiction. Ground is being broken for these projects now across the US and the world. As a result, we are heading toward true cognitive abundance. At Microsoft AI, this is the world our superintelligence lab is planning for and building.

Skeptics accustomed to a linear world will continue predicting diminishing returns. They will continue being surprised. The compute explosion is the technological story of our time, full stop. And it is still only just beginning.

Mustafa Suleyman is CEO of Microsoft AI.

A New Innovation Politics for Global Sustainability

A New Innovation Politics for Global Sustainability

Apr 29, 2026

A globe surrounded by plastic against a soft blue background symbolises environmental issues. by MART PRODUCTION via pexels

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A new innovation politics for global pathways to sustainability?

Published in Institute of Development Studies on 29 April 2026

The ability of innovation – both technical and social – to stretch and redefine ‘limits to growth’ was recognised at Stockholm in 1972, and has been a key feature in debates through to Rio+20 in 2012. Compared with previous major moments of global reflection about human and planetary futures – Stockholm, Rio in 1992, Johannesburg in 2002 – we now have a better understanding of how innovation interacts with social, technological and ecological systems to contribute to transitions at multiple levels. What can this improved understanding offer in terms of governance approaches that might enhance the interaction between local initiatives and global sustainability objectives post-Rio+20?

The global political agenda over the last two decades has largely focussed on creating economic and regulatory incentives to drive more sustainable industrial development patterns within and between nation states – resulting most notably in the CBD and the UNFCCC. At the other end of the spectrum, ‘Local Agenda 21’, launched at the first Rio summit, envisaged a community-led response to sustainable development challenges. Local initiatives often flourished and drew on people’s own, vibrant forms of knowledge, technology and experimentation, but for the most part they remained at the margins, focused on local sustainable development needs rather than articulating with bigger-picture global challenges. This paper discusses the successes and challenges of globally-linked local action through a number of illustrative examples, reflecting on how these have contributed to Rio 1992’s original objectives. In doing so, we will draw upon innovation studies and development studies to highlight three key issues for the new hybrid politics of innovation for sustainability that is required to link global and local. First, the direction in which innovation and development proceed. Second, the distribution of the costs, benefits and risks associated with such changes. Third, the diversity of approaches and forms of innovation that can contribute to global transitions to sustainability. Drawing on this analysis, we will also reflect on Rio+20, including the extent to which this new hybrid politics is already emerging, whether this was reflected in the formal Rio+20 outcomes, and what this suggests for the future of international sustainable development summits.

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Cite this publication

Ely A, Smith A, Stirling A, Leach M, Scoones I, (2013) “Innovation politics post-Rio+20: hybrid pathways to sustainability?” Environment and Planning C: Government and Policy advance online publication, doi:10.1068/c12285j

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