IBM’s acquisitions of HashiCorp and DataStax mark a decisive step in its push to lead enterprise AI and hybrid cloud. The USD 6.4B HashiCorp deal that got finalised this year, brings Terraform, a top-tier infrastructure-as-code tool that streamlines multi-cloud deployments – key to integrating IBM’s Red Hat OpenShift and Watsonx AI. Embedding Terraform enhances automation, making hybrid cloud infrastructure more efficient and AI-ready.

The DataStax acquisition strengthens IBM’s AI data strategy. With AstraDB and Apache Cassandra, IBM gains scalable NoSQL solutions for AI workloads, while Langflow simplifies AI app development. Together, these moves position IBM as an end-to-end AI and cloud powerhouse, offering enterprises seamless automation, data management, and AI deployment at scale.

MongoDB’s RAG Focus

MongoDB’s USD 220M acquisition of Voyage AI signals a strategic push toward enhancing AI reliability. At the core of this move is retrieval-augmented generation (RAG), a technology that curbs AI hallucinations by grounding responses in accurate, relevant data.

By integrating Voyage AI into its Atlas cloud database, MongoDB is making AI applications more trustworthy and reducing the complexity of RAG implementations. Enterprises can now build AI-driven solutions directly within their database, streamlining development while improving accuracy. This move consolidates MongoDB’s role as a key player in enterprise AI, offering both scalable data management and built-in AI reliability.

Google’s 1B Bet on Anthropic

Google’s continued investment in Anthropic reinforces its commitment to foundation model innovation and the evolving GenAI landscape. More than a financial move, this signals Google’s intent to shape the future of AI by backing one of the field’s most promising players.

This investment aligns with a growing trend among cloud giants securing stakes in foundation model developers to drive AI advancements. By deepening ties with Anthropic, Google not only gains access to cutting-edge AI research but also strengthens its position in developing safe, scalable, and enterprise-ready AI. This solidifies Google’s long-term AI strategy, ensuring its leadership in GenAI while seamlessly integrating these capabilities into its cloud ecosystem.

ServiceNow’s AI Automation Expansion

ServiceNow’s USD 2.9B acquisition of Moveworks completed this year, marking a decisive push into AI-driven service desk automation. This goes beyond feature expansion – it redefines enterprise support operations by embedding intelligent automation into workflows, reducing resolution times, and enhancing employee productivity.

The acquisition reflects a growing shift: AI-powered service management is no longer optional but essential. Moveworks’ AI-driven capabilities – natural language understanding, machine learning, and automated issue resolution – will enable ServiceNow to deliver a smarter, more proactive support experience. Additionally, gaining Moveworks’ customer base strengthens ServiceNow’s market reach.

Data Acquisition Surge: Fuelling Digital Transformation

Data has transcended its role as a byproduct of operations, becoming the lifeblood that fuels digital transformation. This fundamental shift has triggered a surge in strategic acquisitions focused on enhancing data management and storage capabilities.

Lenovo Scaling Enterprise Storage

Lenovo’s USD 2B acquisition of Infinidat strengthens its position in enterprise storage as data demands surge. Infinidat’s AI-driven InfiniBox delivers high-performance, low-latency storage for AI, analytics, and HPC, while InfiniGuard ensures advanced data protection.

By integrating these technologies, Lenovo expands its hybrid cloud offerings, challenging Dell and NetApp while reinforcing its vision as a full-stack data infrastructure provider.

Databricks Streamlining Data Warehouse Migrations 

Databricks’ USD 15B acquisition of BladeBridge accelerates data warehouse migrations with AI-driven automation, reducing manual effort and errors in migrating legacy platforms like Snowflake and Teradata. BladeBridge’s technology enhances Databricks’ SQL platform, simplifying the transition to modern data ecosystems.

This strengthens Databricks’ Data Intelligence Platform, boosting its appeal by enabling faster, more efficient enterprise data consolidation and supporting rapid adoption of data-driven initiatives.

Cybersecurity Consolidation: Fortifying the Digital Fortress

The escalating sophistication of cyber threats has transformed cybersecurity from a reactive measure to a strategic imperative. This has fuelled a surge in M&A aimed at building comprehensive and integrated security solutions.

Turn/River Capital’s Security Acquisition

Turn/River Capital’s USD 4.4 billion acquisition of SolarWinds underscores the enduring demand for robust IT service management and security software. This acquisition is a testament to the essential role SolarWinds plays in enterprise IT infrastructure, even in the face of past security breaches.

This is a bold investment, in the face of prior vulnerability and highlights a fundamental truth: the need for reliable security solutions outweighs even the most public of past failings. Investors are willing to make long term bets on companies that provide core security services.

Sophos Expanding Managed Detection & Response Capabilities

Sophos completed the acquisition of Secureworks for USD 859M significantly strengthens its managed detection and response (MDR) capabilities, positioning Sophos as a major player in the MDR market. This consolidation reflects the growing demand for comprehensive cybersecurity solutions that offer proactive threat detection and rapid incident response.

By integrating Secureworks’ XDR products, Sophos enhances its ability to provide end-to-end protection for its customers, addressing the evolving threat landscape with advanced security technologies.

Cisco’s Security Portfolio Expansion

Cisco completed the USD 28B acquisition of SnapAttack further expanding its security business, building upon its previous acquisition of Splunk. This move signifies Cisco’s commitment to creating a comprehensive security portfolio that can address the diverse needs of its enterprise customers.

By integrating SnapAttack’s threat detection capabilities, Cisco strengthens its ability to provide proactive threat intelligence and incident response, solidifying its position as a leading provider of security solutions.

Google’s Cloud Security Reinforcement

Google’s strategic acquisition of Wiz, a leading cloud security company, for USD 32B demonstrates its commitment to securing cloud-native environments. Wiz’s expertise in proactive threat detection and remediation will significantly enhance Google Cloud’s security offerings. This move is particularly crucial as organisations increasingly migrate their workloads to the cloud.

By integrating Wiz’s capabilities, Google aims to provide its customers with a robust security framework that can protect their cloud-based assets from sophisticated cyber threats. This acquisition positions Google as a stronger competitor in the cloud security market, reinforcing its commitment to enterprise-grade cybersecurity.

The Way Ahead

The M&A trends of 2025 underscore the critical role of AI, data, and security in shaping the technology landscape. Companies that prioritise these core areas will be best positioned for long-term success. Strategic acquisitions, when executed with foresight and agility, will serve as essential catalysts for navigating the complexities of the evolving digital world. 

In my previous Ecosystm Insight, I spoke about Web3’s key initiatives in 2024 that caught my attention. As we navigate through the rapidly evolving tech landscape, I’ve chosen to highlight a few trends in the ecosystem that truly excite me – though this list is far from exhaustive. Interestingly, some of the most hyped trends, like the memecoin frenzy and AI agent platforms, didn’t pique my interest enough to dive deeper into.

Here are 7 trends I’ll be keeping an eye on in 2025.

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Click here to download “Web3 Evolution: 2025 Trends To Watch” as a PDF.

1. Stablecoins: The Bridge Between Fintech and Crypto

In 2024, a significant shift occurred in how global fintechs view crypto, driven by stablecoins. What started as a collateral tool for crypto trading has now evolved into a proven solution for cross-border payments and remittances. Companies like Stripe, Revolut, Robinhood, and Nubank are expanding their role as crypto gateways, offering on and off-ramps alongside stablecoin-enabled payments. With global payment revenues projected to reach USD 3.3 trillion by 2031, traditional systems still face challenges like high fees, slow settlement times, and inefficiencies – issues that stablecoin rails are now set to address!

Expect more launches and M&A in this space as every web2 fintech becomes “crypto-ready”!

“Whether intentionally or because of their ability to support third-party apps, every fintech will become a crypto gateway. Fintechs will grow in prevalence and may perhaps rival smaller centralised exchanges in crypto holdings.” – PAUL VERADITTAKIT

2. Bringing Real-World Assets to DeFi: Simplifying Complexities

Over 12,500 DeFi pools currently serve around 7 million users, facing challenges such as onboarding, price discovery, liquidity management, and safeguards against arbitrage. It is expected that decentralised secondary marketplaces for trading real-world assets will be launched, aiming to simplify these complexities and potentially attract new users to DeFi.

3. Smart DePINs: The Rise of AI-Driven Coordination

More DePIN projects are expected to integrate AI and agentic computation to automate coordination, optimise demand and supply, and enhance interoperability. AI may also be used for tasks like node selection, choosing light nodes for accessibility and switching to heavier nodes for network reliability and redundancy. Nvidia’s embrace of DePINs like IPFS Filecoin could be a game-changer, with the company recently sharing potential approaches to leveraging decentralised data structures.

4. Web2.5: The Secret to Scaling Web3 Adoption

Tell me it’s crypto without telling me it’s crypto!

Prediction markets hit their stride in 2024, particularly with Polymarket and the elections, where most users didn’t even realise they were using blockchains. This could be the key to scaling web3 – enter web2.5. However, what is more exciting is the rise of the Telegram mini app ecosystem, the Worldcoin app store, and the Solana phone app store. These simple and intuitive web2-like interfaces are slated to bring more new and first-time users to web3 than some of the louder narratives like the AI-driven memecoin frenzy.

5. Proof-of-Humanity: Securing the Digital Self

While Tools for Humanity faced early criticism for scanning irises, the project, which now has over 20 million users, will gain more traction as people recognise the importance of proof-of-humanity. With the rise of AI-generated content and deepfakes, proof-of-humanity is becoming crucial – not just for combating Sybil attacks and frauds. Projects like SpaceID, Sign, and Mocaverse are also developing universal identity systems that enable users to access multi-chain services with a single private key or ID. Verifiable identity and credentialing via blockchains will be one of the most compelling use cases for the technology.

6. NFTs Reimagined: A New Era of Digital Assets

Story Protocol, which raised USD 80 million at a USD 2.25 billion valuation, aims to tokenise the world’s IP, placing originality at the heart of creative exploration and supporting creators. NFTs can be used not only for ID transactions, transfers, ownership, and memberships but also to represent and value assets. We can expect the emergence of many such NFT use cases beyond profile pictures, particularly in loyalty programs, brand memberships, and token-gated experiences. The second coming of NFTs is set for 2025!

7. Web3 Gateways: Wallets Evolve into Comprehensive Platforms

Similar to how browsers serve as gateways to the internet, web3 wallets like Metamask and Phantom are becoming essential entry points to the web3 experience. These wallets will evolve into all-encompassing platforms, integrating dApps and decentralised applications into their feature set. Along with enhanced security, leading wallets will soon offer services such as trading, gaming, minting, and token swapping, all directly within the wallet interface.

Ecosystm Opinion

It’s a reminder of how far we’ve come – and how much further we have to go. Yuval Noah Harari once pointed out that early use cases of the printing press were often conspiracy theories, and early Internet days were filled with chatrooms and adult content. We’re in the early stages of web3, and with each passing day, new use cases emerge.

This space is still unfolding, and it will be fascinating to see where it leads!

A lot has been written and spoken about DeepSeek since the release of their R1 model in January. Soon after, Alibaba, Mistral AI, and Ai2 released their own updated models, and we have seen Manus AI being touted as the next big thing to follow.

DeepSeek’s lower-cost approach to creating its model – using reinforcement learning, the mixture-of-experts architecture, multi-token prediction, group relative policy optimisation, and other innovations – has driven down the cost of LLM development. These methods are likely to be adopted by other models and are already being used today. 

While the cost of AI is a challenge, it’s not the biggest for most organisations. In fact, few GenAI initiatives fail solely due to cost. 

The reality is that many hurdles still stand in the way of organisations’ GenAI initiatives, which need to be addressed before even considering the business case – and the cost – of the GenAI model. 

Real Barriers to GenAI 

• Data. The lifeblood of any AI model is the data it’s fed. Clean, well-managed data yields great results, while dirty, incomplete data leads to poor outcomes. Even with RAG, the quality of input data dictates the quality of results. Many organisations I work with are still discovering what data they have – let alone cleaning and classifying it. Only a handful in Australia can confidently say their data is fully managed, governed, and AI-ready. This doesn’t mean GenAI initiatives must wait for perfect data, but it does explain why Agentic AI is set to boom – focusing on single applications and defined datasets. 

• Infrastructure. Not every business can or will move data to the public cloud – many still require on-premises infrastructure optimised for AI. Some companies are building their own environments, but this often adds significant complexity. To address this, system manufacturers are offering easy-to-manage, pre-built private cloud AI solutions that reduce the effort of in-house AI infrastructure development. However, adoption will take time, and some solutions will need to be scaled down in cost and capacity to be viable for smaller enterprises in Asia Pacific. 

• Process Change. AI algorithms are designed to improve business outcomes – whether by increasing profitability, reducing customer churn, streamlining processes, cutting costs, or enhancing insights. However, once an algorithm is implemented, changes will be required. These can range from minor contact centre adjustments to major warehouse overhauls. Change is challenging – especially when pre-coded ERP or CRM processes need modification, which can take years. Companies like ServiceNow and SS&C Blue Prism are simplifying AI-driven process changes, but these updates still require documentation and training. 

• AI Skills. While IT teams are actively upskilling in data, analytics, development, security, and governance, AI opportunities are often identified by business units outside of IT. Organisations must improve their “AI Quotient” – a core understanding of AI’s benefits, opportunities, and best applications. Broad upskilling across leadership and the wider business will accelerate AI adoption and increase the success rate of AI pilots, ensuring the right people guide investments from the start. 

• AI Governance. Trust is the key to long-term AI adoption and success. Being able to use AI to do the “right things” for customers, employees, and the organisation will ultimately drive the success of GenAI initiatives. Many AI pilots fail due to user distrust – whether in the quality of the initial data or in AI-driven outcomes they perceive as unethical for certain stakeholders. For example, an AI model that pushes customers toward higher-priced products or services, regardless of their actual needs, may yield short-term financial gains but will ultimately lose to ethical competitors who prioritise customer trust and satisfaction. Some AI providers, like IBM and Microsoft, are prioritising AI ethics by offering tools and platforms that embed ethical principles into AI operations, ensuring long-term success for customers who adopt responsible AI practices. 

GenAI and Agentic AI initiatives are far from becoming standard business practice. Given the current economic and political uncertainty, many organisations will limit unbudgeted spending until markets stabilise. However, technology and business leaders should proactively address the key barriers slowing AI adoption within their organisations. As more AI platforms adopt the innovations that helped DeepSeek reduce model development costs, the economic hurdles to GenAI will become easier to overcome. 

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.