Tag: Ai

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Barely weeks into 2025, the Consumer Electronics Show (CES) announced a wave of AI-powered innovations – from Nvidia’s latest RTX 50-series graphics chip with AI-powered rendering to Halliday’s futuristic augmented reality smart glasses. AI has firmly emerged from the “fringe” technology to become the foundation of industry transformation. According to MIT, 95% of businesses are already using AI in some capacity, and more than half are aiming for full-scale integration by 2026.

But as AI adoption increases, the real challenge isn’t just about developing smarter models – it’s about whether the underlying infrastructure can keep up.

The AI-Driven Cloud: Strategic Growth

Cloud providers are at the heart of the AI revolution, but in 2025, it is not just about raw computing power anymore. It’s about smarter, more strategic expansion.

Microsoft is expanding its AI infrastructure footprint beyond traditional tech hubs, investing USD 300M in South Africa to build AI-ready data centres in an emerging market. Similarly, AWS is doubling down on another emerging market with an investment of USD 8B to develop next-generation cloud infrastructure in Maharashtra, India.

This focus on AI is not limited to the top hyperscalers; Oracle, for instance, is seeing rapid cloud growth, with 15% revenue growth expected in 2026 and 20% in 2027. This growth is driven by deep AI integration and investments in semiconductor technology. Oracle is also a key player in OpenAI and SoftBank’s Stargate AI initiative, showcasing its commitment to AI innovation.

Emerging players and disruptors are also making their mark. For instance, CoreWeave, a former crypto mining company, has pivoted to AI cloud services. They recently secured a USD 12B contract with OpenAI to provide computing power for training and running AI models over the next five years.

The signs are clear – the demand for AI is reshaping the cloud industry faster than anyone expected.

Strategic Investments In Data Centres Powering Growth

Enterprises are increasingly investing in AI-optimised data centres, driven by the need to reduce reliance on traditional data centres, lower latency, achieve cost savings, and gain better control over data.

Reliance Industries is set to build the world’s largest AI data centre in Jamnagar, India, with a 3-gigawatt capacity. This ambitious project aims to accelerate AI adoption by reducing inferencing costs and enabling large-scale AI workloads through its ‘Jio Brain’ platform. Similarly, in the US, a group of banks has committed USD 2B to fund a 100-acre AI data centre in Utah, underscoring the financial sector’s confidence in AI’s future and the increasing demand for high-performance computing infrastructure.

These large-scale investments are part of a broader trend – AI is becoming a key driver of economic and industrial transformation. As AI adoption accelerates, the need for advanced data centres capable of handling vast computational workloads is growing. The enterprise sector’s support for AI infrastructure highlights AI’s pivotal role in shaping digital economies and driving long-term growth.

AI Hardware Reimagined: Beyond the GPU

While cloud providers are racing to scale up, semiconductor companies are rethinking AI hardware from the ground up – and they are adapting fast.

Nvidia is no longer just focused on cloud GPUs – it is now working directly with enterprises to deploy H200-powered private AI clusters. AMD’s MI300X chips are being integrated into financial services for high-frequency trading and fraud detection, offering a more energy-efficient alternative to traditional AI hardware.

Another major trend is chiplet architectures, where AI models run across multiple smaller chips instead of a single, power-hungry processor. Meta’s latest AI accelerator and Google’s custom TPU designs are early adopters of this modular approach, making AI computing more scalable and cost-effective.

The AI hardware race is no longer just about bigger chips – it’s about smarter, more efficient designs that optimise performance while keeping energy costs in check.

Collaborative AI: Sharing The Infrastructure Burden

As AI infrastructure investments increase, so do costs. Training and deploying LLMs requires billions in high-performance chips, cloud storage, and data centres. To manage these costs, companies are increasingly teaming up to share infrastructure and expertise.

SoftBank and OpenAI formed a joint venture in Japan to accelerate AI adoption across enterprises. Meanwhile, Telstra and Accenture are partnering on a global scale to pool their AI infrastructure resources, ensuring businesses have access to scalable AI solutions.

In financial services, Palantir and TWG Global have joined forces to deploy AI models for risk assessment, fraud detection, and customer automation – leveraging shared infrastructure to reduce costs and increase efficiency.

And with tech giants spending over USD 315 billion on AI infrastructure this year alone – plus OpenAI’s USD 500 billion commitment – the need for collaboration will only grow.

These joint ventures are more than just cost-sharing arrangements; they are strategic plays to accelerate AI adoption while managing the massive infrastructure bill.

The AI Infrastructure Power Shift

The AI infrastructure race in 2025 isn’t just about bigger investments or faster chips – it’s about reshaping the tech landscape. Leaders aren’t just building AI infrastructure; they’re determining who controls AI’s future. Cloud providers are shaping where and how AI is deployed, while semiconductor companies focus on energy efficiency and sustainability. Joint ventures highlight that AI is too big for any single player.

But rapid growth comes with challenges: Will smaller enterprises be locked out? Can regulations keep pace? As investments concentrate among a few, how will competition and innovation evolve?

One thing is clear: Those who control AI infrastructure today will shape tomorrow’s AI-driven economy.

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2024 was a pivotal year for cryptocurrency, driven by substantial institutional adoption. The approval and launch of spot Bitcoin and Ethereum ETFs marked a turning point, solidifying digital assets as institutional-grade. Bitcoin has evolved into a macro asset, and the ecosystem’s outlook remains robust, with signs of regulatory clarity in the US and increasing broad adoption. High-quality research from firms like VanEck, Messari, Pantera, Galaxy, and a16Z, has further strengthened my conviction.  

As a “normie in web3,” my perspective comes from connecting the dots through research, not from early airdrops or token swaps. While the speculative frenzy, rug pulls, and scams at the “casino” end are off-putting, the real potential on the “computer” side of blockchains is thrilling. Events like TOKEN2049 in Dubai and Singapore highlight the ecosystem’s energy, with hundreds of side events now central to the experience.

As the web3 ecosystem evolves, new blockchains, roll-ups, and protocols vie for attention. With 60 million unique wallets in the on-chain economy, adoption is set to expand beyond this base. DeFi transaction volumes have surpassed USD 200B/month, yet the ecosystem remains in its early stages, with only 10 million users.

Despite current fragmentation, the future looks promising. Themes like tokenising real-world assets, decentralised public infrastructure, stablecoins for instant payments, and the convergence of AI and blockchain could reshape finance, identity, infrastructure, and computing. Web3 holds transformative potential, even if not in marketing terms like “unstable” coins or “unreal world assets.”

The Decentralisation Paradox of Web3

Decentralisation may have been a core tenet of web3 at the onset but is also seen as a constraint to scaling or improving user experience in certain instances. I always saw decentralisation as a progressive spectrum and not a binary. It is, however, a difficult north star to maintain, as scaling becomes an actual human coordination challenge.

In Blockchains. We have seen this phenomenon manifest with the Ethereum ecosystem in particular. Of the fifty-plus roll-ups listed on L2 Beat, only Arbitrum and OP Mainnet have progressed beyond Stage 0, with many still not posting fraud proofs to L1. Some high-performance L1s and L2s have deprioritised decentralisation in favour of scaling and UX. Whether this trade-off leads to greater vulnerability or stronger product-market fit remains to be seen – most users care more about performance than underlying technology. In 2025, we’ll likely witness the quiet demise of as many blockchains as new ones emerge.

In Finance. On the institutional side, some aspects of high-value transactions in traditional finance or TradFi, such as custody, need trusted intermediaries to minimise counterparty risk. For web3 to scale beyond the 60-million-odd wallets that participate in the on-chain economy today, we need protocols that marry blockchains’ efficiency, composability, and programmability with the trusted identity and verifiability of the regulated financial systems. While “CeDeFi” or Centralised Decentralised Finance might sound ironical to most in the crypto native world, I expect much more convergence with institutions launching tokenisation projects on public blockchains, including Ethereum and Solana. I like underway pilots, such as one by Chainlink with SWIFT, facilitating off-chain cash settlements for tokenised funds. Some of these projects will find strong traction and scale coupled with regulatory blessings in certain progressive jurisdictions in 2025.

In Infrastructure. While decentralised compute clusters for post-training and inference from the likes of io.net can lower the cost of computing for start-ups, scaling decentralised AI LLMs to make them competitive against LLMs from centralised entities like OpenAI is a nearly impossible order. New metas such as decentralised science or DeSci are exciting because they open the possibility of fast-tracking fundamental research and drug discovery.

Looking Back at 2024: What I Found Exciting

ETFs. BlackRock’s IBIT ETF became the fastest to reach USD 3 billion in AUM within 30 days and scaled to USD 40 billion in 200 days. The institutional landscape now goes beyond traditional ETFs, with major financial institutions expanding digital asset capabilities across custody, market access, and retail integration. These include institutional-grade custody from Standard Chartered and Nomura, market access from Goldman Sachs, and retail integration from fintechs such as Revolut.

Stablecoins. Stablecoin usage beyond trading has continued to grow at a healthy clip, emerging as a real killer use case in payments. Transaction volumes rose from USD 10T to USD 20T in a year, and yes, that is a trillion with a “t”! The current market capitalisation of stablecoins is approximately USD 201.5 billion, slated to triple in 2025, with Tether’s USDT at over 67% market share. We might see new fiat-backed stablecoins being launched this year, such as Ethena’s yield-bearing stablecoin, but I don’t expect USDT’s dominance to change.

RWAs. Even though stablecoins represent 97% of real-world assets on-chain and the dollar value of all other types of assets is still insignificant, the potential market for asset tokenisation is still a staggering USD 1.4T, and with regulatory clarity, even if RWAs on-chain were to quadruple, the resulting USD 50B will be a sliver of the overall opportunity. We can expect more projects in asset classes such as private credit – rwa.xyz is a great dashboard to watch this space.

DePIN. Decentralised public infrastructure across wireless, energy, compute, sensors, identity, and logistics reached a USD 50B market cap and USD 500M in ARR. Key developments include the emergence of AI as a major driver of DePIN adoption, the maturation of supply-side growth playbooks, and the shift in focus toward demand-side monetisation. More than 13 million devices globally contribute to DePINs daily, demonstrating successful supply-side scaling. Notable projects include:

• Helium Mobile: Adding 100k+ subscribers and diversifying revenue streams.
• AI Integration: Bittensor leading decentralised AI with successful subnets.
• Energy DePINs: Glow and Daylight addressing challenges in distributed energy systems.
• Identity Verification: World (formerly Worldcoin) achieving 20 million verified identities.

These trends indicate significant advancements in the web3 ecosystem, and the continued evolution of blockchain technologies and their applications in finance, infrastructure, and beyond holds immense promise for 2025 and beyond.

In my next Ecosystm Insights, I’ll present the trends in 2025 that I am excited about. Watch this space!

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AI is Transforming Operations – Are You Ready to Scale?

The future of operations is AI-driven, ensuring efficiency, agility, and smarter decision-making. But scaling AI isn’t just about technology – it’s about strategy.

To stay ahead, Leaders must focus on:

• Balancing cost savings with business priorities
• Aligning stakeholders for cross-functional success
• Looking beyond surface-level ROI
• Breaking down data silos for smarter decisions

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Automation and AI hold immense promise for accelerating productivity, reducing errors, and streamlining tasks across virtually every industry. From manufacturing plants that operate robotic arms to software-driven solutions that analyse millions of data points in seconds, these technological advancements are revolutionising how we work. However, AI has already led to, and will continue to bring about, many unintended consequences.

One that has been discussed for nearly a decade but is starting to impact employees and brand experiences is the “automation paradox”. As AI and automation take on more routine tasks, employees find themselves tackling the complex exceptions and making high-stakes decisions.

What is the Automation Paradox?

1. The Shifting Burden from Low to High Value Tasks

When AI systems handle mundane or repetitive tasks, ‘human’ employees can direct their efforts toward higher-value activities. At first glance, this shift seems purely beneficial. AI helps filter out extraneous work, enabling humans to focus on the tasks that require creativity, empathy, or nuanced judgment. However, by design, these remaining tasks often carry greater responsibility. For instance, in a retail environment with automated checkout systems, a human staff member is more likely to deal with complex refund disputes or tense customer interactions. Or in a warehouse, as many processes are automated by AI and robots, humans are left with the oversight of, and responsibility for entire processes. Over time, handling primarily high-pressure situations can become mentally exhausting, contributing to job stress and potential burnout.

2. Increased Reliance on Human Judgment in Edge Cases

AI excels at pattern recognition and data processing at scale, but unusual or unprecedented scenarios can stump even the best-trained models. The human workforce is left to solve these complex, context-dependent challenges. Take self-driving cars as an example. While most day-to-day driving can be safely automated, human oversight is essential for unpredictable events – like sudden weather changes or unexpected road hazards.

Human intervention can be a critical, life-or-death matter, amplifying the pressure and stakes for those still in the loop.

3. The Fallibility Factor of AI

Ironically, as AI becomes more capable, humans may trust it too much. When systems make mistakes, it is the human operator who must detect and rectify them. But the further removed people are from the routine checks and balances – since “the system” seems to handle things so competently – the greater the chance that an error goes unnoticed until it has grown into a major problem. For instance, in the aviation industry, pilots who rely heavily on autopilot systems must remain vigilant for rare but critical emergency scenarios, which can be more taxing due to limited practice in handling manual controls.

Add to These the Known Challenges of AI!

Bias in Data and Algorithms. AI systems learn from historical data, which can carry societal and organisational biases. If left unchecked, these algorithms can perpetuate or even amplify unfairness. For instance, an AI-driven hiring platform trained on past decisions might favour candidates from certain backgrounds, unintentionally excluding qualified applicants from underrepresented groups.

Privacy and Data Security Concerns. The power of AI often comes from massive data collection, whether for predicting consumer trends or personalising user experiences. This accumulation of personal and sensitive information raises complex legal and ethical questions. Leaks, hacks, or improper data sharing can cause reputational damage and legal repercussions.

Skills Gap and Workforce Displacement. While AI can eliminate the need for certain manual tasks, it creates a demand for specialised skills, such as data science, machine learning operations, and AI ethics oversight. If an organisation fails to provide employees with retraining opportunities, it risks exacerbating skill gaps and losing valuable institutional knowledge.

Ethical and Social Implications. AI-driven decision-making can have profound impacts on communities. For example, a predictive policing system might inadvertently target specific neighbourhoods based on historical arrest data. When these systems lack transparency or accountability, public trust erodes, and social unrest can follow.

How Can We Mitigate the Known and Unknown Consequences of AI?

While some of the unintended consequences of AI and automation won’t be known until systems are deployed and processes are in practice, there are some basic hygiene approaches that technology leaders and their organisational peers can take to minimise these impacts.

1. Human-Centric Design. Incorporate user feedback into AI system development. Tools should be designed to complement human skills, not overshadow them.
2. Comprehensive Training. Provide ongoing education for employees expected to handle advanced AI or edge-case scenarios, ensuring they remain engaged and confident when high-stakes decisions arise.
3. Robust Governance. Develop clear policies and frameworks that address bias, privacy, and security. Assign accountability to leaders who understand both technology and organisational ethics.
4. Transparent Communication. Maintain clear channels of communication regarding what AI can and cannot do. Openness fosters trust, both internally and externally.
5. Increase your organisational AIQ (AI Quotient). Most employees are not fully aware of the potential of AI and its opportunity to improve – or change – their roles. Conduct regular upskilling and knowledge sharing activities to improve the AIQ of your employees so they start to understand how people, plus data and technology, will drive their organisation forward.

Let me know your thoughts on the Automation Paradox, and stay tuned for my next blog on redefining employee skill pathways to tackle its challenges.

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AI is redefining HR – are you ready?

Here’s some data-backed guide to advancing AI in HR for 2025. It’s no longer just about recruitment; AI is reshaping everything from employee experience to strategic decision-making.

To stay ahead, focus on:
• Setting clear goals with HR-specific KPIs
• Prioritising ethics & transparency in AI use
• Embedding Human-AI collaboration into HR practices
• Taking a seat at the table in AI strategy discussions