Tech Firms and AI: Lower costs, more competition and what it means for investors

By Nigel Green

The rapid decline in the cost of generative artificial intelligence (AI) models is reshaping the tech investment landscape, forcing a reassessment of where capital should flow in the sector.

For years now, the dominant narrative has been that AI development requires massive financial backing, with tech giants pouring billions into computational power, model training, and proprietary data infrastructure.

Now, as AI models become more cost-efficient, both startups and established firms must rethink their strategies to remain competitive.

The dramatic fall in the cost of computing and price per token marks a fundamental shift in AI economics. Previously, access to cutting-edge AI required significant investment in high-performance hardware, expensive cloud computing resources, and vast datasets.

This high barrier to entry kept AI innovation concentrated among a handful of major players. However, the emergence of more efficient AI models has significantly lowered these costs, democratizing access to powerful AI systems and enabling smaller firms to compete with industry leaders.

For investors, this means that AI-driven innovation is no longer the exclusive domain of the biggest tech firms.

A broader range of startups can now build and deploy AI applications at a fraction of the cost, leading to increased competition, more diverse business models, and potentially higher returns on investment in AI-powered ventures.

The shift also suggests that some of the astronomical valuations previously assigned to AI-heavy companies might need recalibration, as the cost advantage that justified such valuations erodes.

Implications for tech giants

For the world’s largest tech firms, which have invested billions in AI infrastructure, the falling cost of AI raises critical questions.

One of the biggest concerns is whether the high level of capital expenditure these companies have committed will remain justified. As efficiency improves, the need for costly compute power diminishes, which could lead to a reassessment of investment strategies among the tech elite.

Rather than pouring capital into raw compute power, tech firms may shift their focus toward differentiation through proprietary data, specialized AI applications, and user-centric design. The true competitive advantage may no longer lie in building the largest AI model but rather in fine-tuning AI to offer unique, high-value solutions to businesses and consumers.

With the barrier to entry significantly lowered, a wave of AI startups is likely to emerge. These companies will no longer need to secure vast sums of venture capital just to train models; instead, they can focus on application development, real-world deployment, and commercialization strategies.

This, I expect, will lead to a broader range of AI-driven services across industries, from healthcare and finance to entertainment and logistics.

This represents both an opportunity and a challenge for investors. While the democratization of AI could lead to an explosion of innovation, it also means that competition will be fiercer, making it harder to identify winners.

The focus may shift from backing companies that merely have access to AI to those that can apply it in creative and commercially viable ways. Investors will need to assess which firms are best positioned to leverage AI for sustained profitability rather than simply riding the wave of AI hype.

Another key consequence of falling costs is the increasing efficiency of AI training and deployment.

More efficient models mean that companies can train AI with fewer resources, reducing the time and expense associated with developing new capabilities.

This is particularly significant for industries where adoption has been limited due to cost constraints. From small businesses leveraging AI for automation to healthcare companies using AI-driven diagnostics, lower costs could lead to broader adoption across multiple sectors.

This is going to efficiency translates into more sustainable AI-driven business models. Companies that previously struggled with the capital-intensive nature of AI can now explore new ways to integrate it into their operations.

In addition, industries that have been hesitant to invest in AI due to high initial costs may now become viable markets for AI expansion, opening up new investment opportunities in AI-powered solutions tailored for non-tech sectors.

Future of AI investment

While the declining cost of AI models presents challenges for incumbents, it also signals a broader shift in how AI will be monetized and valued.

The focus may move away from sheer processing power and data accumulation toward the intelligent application of AI in solving real-world problems.

This could redefine what constitutes a valuable AI company, favoring firms that demonstrate clear, practical use cases and revenue-generation capabilities over those that merely showcase technical prowess.

For investors looking ahead, the key will be identifying companies that can effectively translate AI efficiency into market leadership.

As the AI ecosystem matures, the most successful businesses will be those that not only leverage AI cost-effectively but also differentiate themselves through strategic implementation, unique data insights, and strong customer engagement.

Ultimately, the falling cost of AI is not just a tech milestone, it’s an investment inflection point.

(Author is deVere Group CEO and Founder)

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