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.

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!

2025 is already shaping up to be a battleground for cybersecurity. With global cybercrime costs projected to reach USD 10.5T, by year’s end, the stakes have never been higher. Cybercriminals are getting smarter, using AI-driven tactics and large-scale exploits to target critical sectors. From government breaches to hospital data leaks and a surge in phishing scams, recent attacks highlight the growing financial and operational toll of cyber threats.

As cyber threats intensify, the demand for stronger defences, top-tier cybersecurity talent, and global collaboration has never been more urgent.

Here’s a look at the recent cyber developments that are shaping 2025.

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Click here to download “Cyber Lessons from the Frontlines” as a PDF.

Major Security Breaches: A Costly Wake-Up Call

Cyberattacks are becoming more targeted, disruptive, and costly – impacting governments and organisations worldwide.

In Singapore, mobile wallet fraud is surging, with phishing tactics causing USD 8.9K in losses – 80% linked to Apple Pay. In the UK, security flaws in government IT systems have exposed sensitive data and infrastructure. South Africa’s government-run weather service (SAWS) was also forced offline, disrupting a critical resource for airlines, farmers, and emergency responders. Across the Atlantic, a data breach at a Georgia hospital compromised 120,000 patient records, while BayMark Health Services, the largest addiction treatment provider in the US, alerted patients to a similar breach.

What steps are governments, tech providers, and enterprises taking to protect themselves, critical infrastructure, and individuals?

Protecting Critical Infrastructure: The Digital Backbone

As global connectivity expands, securing critical infrastructure is paramount to sustaining growth, stability, and public trust.

Undersea cables, which carry much of the world’s internet traffic, are a major focus. While tech giants like Amazon, Meta, and Google are expanding these networks to boost global data speed and reliability, the need for protection is just as urgent – prompting the EU to invest nearly a billion dollars in securing them against emerging threats.

Governments and tech providers alike are stepping up. The European Commission has introduced a cybersecurity blueprint to strengthen crisis coordination, rapid response, and information sharing. Meanwhile, Microsoft is investing USD 700M in Poland’s cloud and AI infrastructure, working with the Polish National Defense to enhance cybersecurity through AI-driven strategies.

Quantifying Cyber Risk: Standardised Threat Assessment

As cyber threats grow more sophisticated, so must our ability to detect, measure, and respond to them.

A major shift in cybersecurity is underway – one that prioritises standardised threat assessment and coordinated defense.

The UK is leading the charge with a new cyber monitoring centre that will introduce a “Richter Scale” for cyberattacks, ranking threats much like earthquake magnitudes. Emerging countries are also joining in; Vietnam is strengthening its cyber defences with a new intelligence-sharing platform designed to improve coordination between the government and private sector.

By quantifying cyber risks and enhancing intelligence-sharing, these efforts are shaping global cybersecurity norms, improving response times, and building a more resilient digital ecosystem.

Beyond Defence: Proactive Measures to Combat AI-Driven Cybercrime

Cyber threats evolve faster than defences can keep up – a single click on a malicious email can lead to a breach in just 72 minutes.

With AI making cyberattacks more sophisticated, governments are taking an active role in cyber law enforcement.

Indonesia set up a cyber patrol to monitor and regulate harmful online content while also working to create a safer digital space for children. Thailand, Cambodia, and Laos are cooperating to curb cross-border scams through intelligence sharing and joint enforcement efforts.

Building Trust Online: Digital Identity Solutions

Governments are moving beyond enforcement to strengthen security with digital identity frameworks.

The EU is leading this shift with large-scale pilots for digital identity wallets, designed to offer citizens a secure, seamless way to verify credentials for services, transactions, and age-restricted content. By 2026, each EU member state will issue its own wallet, built on unified technical standards to ensure cross-border interoperability and stronger cybersecurity.

Digital identity wallets mark a major shift in data security, giving citizens greater control over their information while strengthening online trust. By securing identity verification, governments are reducing fraud and identity theft, creating a safer digital landscape.

Closing the Gap: Global Cyber Education Push

Cybersecurity education is no longer just for IT teams – it’s essential at every level, from executives to employees, to build long-term resilience.

Again, governments and tech giants alike are stepping up to bridge the skills gap and enhance cyber awareness.

Singapore is leading by example with a cyber-resilience training program for board directors, ensuring corporate leaders understand cyber risk management. AWS is investing USD 6.35M to support cybersecurity education in the UK, and Microsoft is expanding its global training efforts. The company has partnered with Kazakhstan to strengthen public sector cybersecurity and has committed to training one million South Africans in AI and cybersecurity by 2026.

The Path Forward: A Collective Responsibility

The cybersecurity landscape underscores a crucial truth: resilience can’t be built in isolation. Governments, businesses, and individuals must move past reactive measures and adopt a collective, intelligence-driven approach. As threats grow more sophisticated, so must our commitment to collaboration, vigilance, and proactive defence.

In an increasingly interconnected world, securing the digital landscape is not just necessary – it’s a shared responsibility.

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AI has rapidly transitioned from a theoretical concept to a strategic imperative, reshaping core business functions and fundamentally altering the operational landscape of technology teams. By empowering teams with increased autonomy and data-driven capabilities, organisations are positioned to realise substantial value and achieve a decisive competitive advantage.

The most profound impact of AI can be observed within tech teams. AI-driven automation of routine tasks and streamlined operations are enabling technology professionals to refocus their efforts on strategic initiatives. This shift transforms the technology function from a reactive system maintenance role to a proactive developer of intelligent infrastructure and future-oriented systems.

Ecosystm research reveals key findings that Tech Leaders need to know.

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Click here to download “AI Stakeholders: The Tech Leader’s Perspective” as a PDF.

Strategic AI Deployment

Ecosystm research reveals a clear trend: technology leaders are strategically investing in the immense potential of AI. While 61% currently leverage AI for IT support and helpdesk automation, there is a clear aspiration for broader deployment across infrastructure, development, and security. 80% are prioritising cloud resource allocation and optimisation, followed by 76% focusing on network optimisation and performance monitoring, along with significant interest in software development and testing, and cyber threat detection.

One Infrastructure Leader shared that the organisation uses AI to dynamically scale infrastructure while automating maintenance to prevent outages. This approach has led to unprecedented efficiency and freed up their teams for more strategic work. The leader emphasised that AI is helping to tackle complex infrastructure challenges and is key to achieving operational excellence.

A Cyber Leader discussed the role of AI in enhancing their defense capabilities. While not a “silver bullet,” it is a powerful tool in the fight against cyber threats. AI significantly enhances threat intelligence and fraud analysis, complementing, rather than replacing, security team efforts. This integration has helped streamline security operations and improve the ability to respond to emerging risks.

AI is also making waves in software development. A Data Science Leader explained how AI quality control tools have reduced bug counts by 30%, enabling faster release cycles and a 10% improvement in internal customer satisfaction.

Collaborative AI Implementation: A Cross-Functional Approach

The successful implementation of AI requires a collaborative, cross-functional approach. The responsibility for identifying viable use cases, developing and maintaining systems, and ensuring robust data governance is distributed among various technology leadership roles. CIOs, in collaboration with business stakeholders, define strategic use cases, considering infrastructure requirements. Data Science Leaders bridge the gap between AI’s technical capabilities and practical business applications. CISOs safeguard data, while CIOs manage the systems that store and organise it.

Navigating Challenges, Prioritising Strategic AI Initiatives

Despite the acknowledged potential of AI, technology leaders must address several critical challenges, including use case prioritisation, skill gaps, and the development of comprehensive AI strategies. Nevertheless, the strategic importance of AI will continue to drive its prioritisation in 2025. Key anticipated outcomes include increased technology team productivity (56%) and technology cost optimisation (53%).

AI is no longer a supplementary tool but a core strategic asset. By strategically integrating AI, technology teams are transitioning from operational support to strategic innovation, building the intelligent systems that will define the future of business.

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Customer Success leaders are keenly aware of AI’s burgeoning potential, and our latest research confirms it. AI is no longer a futuristic concept; it’s a present-day reality, already shaping content strategies for 55% of organisations and poised to expand its influence across a multitude of use cases.

Over the past two years, Ecosystm’s research – including surveys and deep dives with business and tech leaders – has consistently pointed to AI as the dominant theme.

Here are some insights for Customer Success Leaders from our research.

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Click here to download “AI Stakeholders: The Customer Success Perspective” as a PDF.

AI in Action: Real-World Applications

The data speaks for itself. We’re seeing a significant uptake of AI in automating sales processes (69%), location-based marketing (63%), and delivering personalised product/service recommendations (61%). But beyond the numbers, what does this look like in practice?

In Marketing, AI tailors campaigns in real time based on customer behaviour, ensuring content and offers resonate. For e.g. in the Travel industry, AI analyses customer preferences to create customised itineraries, boosting satisfaction and repeat bookings. In Sales, AI-driven analysis of buying patterns helps teams stay ahead of trends, equipping them with the right products to meet demand. In Customer Experience, AI-powered feedback analysis identifies pain points before they escalate, leading to proactive problem-solving. We have already seen organisations using  conversational AI to enable 24/7 customer engagement, instantly resolving issues while reducing team workload and enhancing CX.

Challenges and Opportunities: Navigating the AI Landscape

However, the path to AI adoption isn’t without its hurdles. Customer Success leaders face significant challenges, including the lack of an organisation-wide AI strategy, data complexity and access issues, and the cost of implementation.

Despite these challenges, the focus on AI to enhance Customer Success is evident, with nearly 40% of AI initiatives geared towards this goal. This requires a more active role for these leaders in shaping AI strategies and roadmaps.

Our research reveals that there lies a critical gap: Customer Success leaders have limited involvement in AI initiatives. Only 19% are involved in identifying and prioritising use cases, and a mere 10% have input into data ownership and governance. This lack of participation is a missed opportunity.

The 2025 Vision: AI-Driven Customer Success

Looking ahead, Customer Success leaders expect AI to deliver significant benefits, including improved customer experience (56%), increased productivity (50%), and enhanced innovation (44%). These expectations underscore AI’s pivotal role in shaping the future of customer success.

To fully harness AI’s potential and advancements like Agentic AI, leaders must take a more active role. This means driving a clear AI strategy, tackling data challenges, and working closely with IT and data science teams to ensure AI solutions address real customer pain points and business gaps.

In my previous Ecosystm Insight, I explored the Automation Paradox – how AI shifts human roles from routine tasks to more complex, high-pressure responsibilities. Now, let’s look at its impact on entry-level roles and what it means for those starting their careers.

AI is reshaping the skills mix in enterprises, automating many repetitive, lower-complexity tasks that traditionally serve as stepping stones for new professionals. Roles like Level 1 IT support or paralegal work – once common entry points – are increasingly being automated or significantly reduced.

The question now is: how will the next generation gain the experience needed to advance?

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Click here to download “AI’s Unintended Consequences: Redefining Employee Skill Pathways” as a PDF

Why Are Entry-Level Roles Changing?

• Automation of Routine Tasks. AI-driven tools are taking over routine tasks. AI-driven tools and chatbots now handle common helpdesk issues instantly, eliminating the need for human intervention. Contract review software scans and analyses legal documents, cutting the workload of junior paralegals.
• Demand for Specialised Knowledge. As AI handles grunt work, remaining roles demand higher-level skills – technical, analytical, and interpersonal. For e.g., IT support shifts from password resets to configuring complex systems, interpreting AI diagnostics, and crafting custom solutions.

With routine tasks automated and remaining work more complex, traditional career entry points may shrink – or vanish entirely.

If an organisation no longer has a roster of junior positions, where will young professionals gain the foundational experience and institutional knowledge needed to excel?

The Ripple Effect on Talent & Development

Reduced Traditional Apprenticeships. Entry-level roles have historically provided new hires with an informal apprenticeship – learning basic skills, building relationships, and understanding organisational nuances. Without these roles, new talent may miss out on crucial developmental opportunities.

Potential Skills Gap. By removing the “lower rungs” of the career ladder, we risk ending up with professionals who lack broad foundational knowledge. A fully automated helpdesk, for example, might produce mid-level analysts who understand theory but have never troubleshot a live system under pressure.

Pressure to Upskill Quickly. New recruits may have to jump directly into more complex responsibilities. While this can accelerate learning, it may also create undue stress if the proper structures for training, mentoring, and support are not in place.

Strategies to Create New Skill Pathways

1. Reimagined Entry Pathways for New Employees

• Rotational Programs. One way to fill the void left by disappearing junior roles is through rotational programs. Over the course of a year, new hires cycle through different departments or projects, picking up hands-on experience even if traditional entry-level tasks are automated.
• Apprenticeship-Style Training. Instead of “on-the-job” experience tied to low-level tasks, companies can establish apprenticeship models where junior employees shadow experienced mentors on live projects. This allows them to observe complex work up close and gradually take on real responsibilities.

2. Blended Learning & Simulation

• AI-Driven Training. Ironically, AI can help solve the gap it creates. AI simulations and virtual labs can approximate real-world scenarios, giving novices a taste of troubleshooting or document review tasks.
• Certification & Micro-Credentials. More specialised skill sets may be delivered through structured learning, using platforms that provide bite-sized, verifiable credentials in areas like cybersecurity, analytics, or advanced software configuration.
• Knowledge Sharing Communities. Team chat channels, internal wikis, and regular “lunch and learn” sessions can help new employees gain the cultural and historical context they’d otherwise accumulate in junior roles.

3. Redefining Career Progression

• Competency-Based Pathways. Instead of relying on job titles (e.g. Level 1 Support), organisations can define career progression through skill mastery. Employees progress once they demonstrate competencies – through  projects, assessments, or peer review – rather than simply ticking time-based boxes.
• Continuous Upskilling. Given the rapid evolution of AI, companies should encourage a culture of lifelong learning. Subsidised courses, conference attendance, and online platforms help maintain an agile, future-ready workforce.