CoreWeave: Company Profile and the Importance of Its Infrastructure for Artificial Intelligence

Despite the growing availability of artificial intelligence models, turning them into reliable commercial products remains a complex task, especially when enterprises face fragmented data infrastructure.

At the TechSparks 2026 event, Michael Cronin, Vice President of Sales for Asia Pacific, Japan and India at Couchbase, participated in a discussion with Shivani Mutanna, Senior Director of Strategic Content and Video at YourStory. They discussed the transition of AI from the experimentation stage to full production, management challenges, customer experience, and the increasing role of India in AI innovation.

For decades, large corporations have implemented various databases and highly specialized solutions to solve specific problems. While each solved an immediate issue, the result was often a fragmented data layer composed of multiple systems that interact poorly with each other.

Cronin considers this fragmentation a serious obstacle to AI adoption. He cites industry estimates suggesting that 70–80% of AI development projects never reach the production stage, and MIT research showed that 95% of such projects do not become profitable.

Couchbase's operational data platform is designed to address this problem by helping companies consolidate their data infrastructure. Instead of trying to fix everything at once, Cronin advised companies to start small, recommending that founders and enterprises struggling with data layer fragmentation first choose the most critical use cases.

He cited the example of UK retailer Tesco, which has a large Global Capability Center (GCC) in Bengaluru. Many of this retailer's systems negatively impacted the customer experience, especially during peak shopping periods like Black Friday and weekends. Tesco began consolidating its data layer, starting with one instance and one use case before expanding efforts across its entire technology stack. This consolidation allowed the retailer to process millions of transactions per second with less than a millisecond latency. In Cronin's view, this example demonstrates how companies can begin solving the fragmentation problem through one use case before scaling changes across their entire operation.

The pressure associated with developing AI products has prompted many enterprises to prioritize models and applications. However, pilot projects are often conducted on small datasets in controlled environments, which does not prepare organizations for real-world production demands. Cronin pointed to the growing scale of machine traffic. He stated: 'In eighteen months, machine traffic surpassed human traffic on the internet. That is the scale of AI. That is the scale of machine speed at which businesses must operate.'

When AI applications encounter real-world workloads, fragmented data systems can prevent them from functioning reliably at scale. The increasing use of AI agents exacerbates this issue. As agents become more autonomous and take on decision-making functions, enterprises must ensure they can access the necessary data within appropriate governance structures.

Fragmented systems can hinder communication and management, limiting agents' ability to act autonomously. Cronin also urged enterprises to reconsider their approach to allocating AI investment. While models evolve rapidly, corporate data remains a critical asset. He emphasized: 'Models can be rented. The best model today is yesterday's newspaper. The true asset for an enterprise is data.'

As enterprises began to realize that fragmented data was slowing down their AI initiatives, many responded by adding APIs and connectors to link existing systems. However, Cronin warned that this method could create additional complexity instead of solving the core problem. Every extra connection increases latency, risk, and creates another potential point of failure.

Instead, he recommended consolidating the data layer so that AI applications and agents could interact with it more directly, bringing data closer to the customers. According to Cronin, many enterprises, including large GCCs in India, are currently re-architecting to eliminate data fragmentation. Without these changes, further investments in AI development may lead to the same problems.

Data consolidation can also improve customer experience by helping systems handle growing demand, remain available, and respond quickly. As customers expect more from AI-based services, having data at the edge is becoming increasingly important.

Cronin noted that business cultures vary across the Asia Pacific region, but fragmented data infrastructure remains a common challenge for AI adoption. At the same time, India is emerging as a vital hub for AI innovation and decision-making. He noted that 80% of Couchbase's top 50 global customers have a significant presence in India, with much of their AI innovation and leadership based in the country. Couchbase is also expanding its presence in India, moving its Asia Pacific headquarters to Bengaluru. Among Couchbase's corporate clients in India are Air India, Airtel, BigBasket, and Mahindra Finance. In Cronin's opinion, India's role in global business extends beyond cost savings; innovation, leadership, and important business decisions are increasingly happening there. He mentioned Air India's next-generation entertainment platform, which is built on Couchbase. This platform aims to provide passengers with a personalized shopping and entertainment experience even without network connectivity on board the aircraft. For Cronin, this project illustrates how Indian enterprises are using technology to enhance customer experience in real-world operations.

A recent incident involving the largest theft in the crypto industry has revealed a critical weakness in the security mechanisms of crypto exchanges. Although the attack on Binance, which amounted to 6.3 billion US dollars, was not based on artificial intelligence, the development of AI calls into question the ability of any crypto exchange to guarantee the safety of client funds.

Binance, the world's largest cryptocurrency exchange, temporarily suspended withdrawal operations. The exchange promised to cover all losses from its own reserves and from the Secure Asset Fund for Users (SAFU), which serves as an emergency fund. Representatives of Binance Africa reported on Friday that 'user funds are safe.'

According to data from the analytical firm TRM Labs, the hacker moved funds from Binance wallets to 10 different blockchains on Thursday, October 29. Most of these transactions were made approximately 20 minutes after 22:05 South African time. TRM Labs estimates the total amount stolen at $6.36 billion, which is about four times the $1.5 billion stolen from Bybit in February 2025. Binance estimates the amount to be around $6.3 billion.

As of October 30, TRM calculated that about $2.7 billion was frozen, mainly in stablecoins, while Binance states this figure is around $3 billion. However, the attacker still controls about $2.4 billion in ether and bitcoin, which no issuing body can freeze. This amount is more than twice the $1 billion that Binance stated in January it had preserved in the SAFU fund.

Hendrik de Bruijn, Head of Security Consulting for Africa at Check Point Software Technologies, noted that 'the attackers did not hack the encryption or steal private keys. They seized the system that tells the keys what to sign.' Initial findings from Binance suggest that the attack may have originated from a compromised DevOps engineer's laptop—one of the employees responsible for developing and operating the exchange's software systems—which gave the attacker access to the service allowing withdrawal requests to be sent. Binance claims it found no evidence of private key leakage.