After Last Year’s AI Funding Frenzy, Investors Double-Down on Productivity

2025 was a record-breaking year for AI investment, exceeding even the most confident predictions. 

A total of $202.3 billion was invested into the AI sector, per year-end figures from Crunchbase. Other notable milestones included a $500 billion commitment from SoftBank and Oracle (ORCL) to build AI supercomputers, while Nvidia (NVDA) launched a $100 billion fund to secure U.S. leadership in advanced chips. 

Two of the largest companies in the space, OpenAI and Anthropic, alone captured 14% of global venture investment. This saw OpenAI end the year on an unprecedented high following its $500 billion dollar valuation, while companies such as AI-powered industrial maintenance company Fracttal closed a $35M round with Riverwood Capital and other investors this week and KKR’s Ness Digital Engineering launched its Mexico office to support AI innovation.

Investments in AI infrastructure have also been offering near-term gains. The average stock in Goldman Sachs (GS) Research’s basket of infrastructure companies - which includes semiconductors, hyperscalers, technology hardware providers, and power companies - returned 44% year-to-date, according to a December report from the firm. 

Meanwhile, Nvidia made history as the first company to hit $5 trillion in market value thanks to its role as the leading manufacturer of advanced processing chips that fuel the AI industry, while international companies including AI workplace startup Deep Care, advertising management platform ADvendio, and AI prospecting enterprise Planno announced their expansion plans into the U.S. 

Looking ahead, heavy investments are expected to continue in 2026. However, after last year’s funding frenzy, investors are expected to take a more selective approach to AI, with a particular focus on productivity.

The landscape of opportunities has already begun to evolve notably since last year. Goldman Sachs Research analyst Ryan Hammond noted that “the combination of continued corporate AI adoption and growing concerns about the AI infrastructure complex has increased recent investor focus on the next beneficiaries of the ever-expanding AI trade.” 

From AI-native engineering platforms, digital transformation and software consultancy, here are three areas of value creation emerging from the AI sector that we expect to see maturing for investors in 2026. 

The rise of engineering productivity as a north star creates investment opportunity for emerging investors

The potential surrounding AI technologies has not only sparked record-breaking levels of investment, but also catalyzed a frenetic race between CEOs and leaders to get ahead of the pack with AI adoption. 

Even so, the majority of enterprises still remain in the experimenting or piloting stages. Approximately one-third reported that their companies have begun to scale their AI programs. While this may appear to be a positive indicator, the ability to scale integrative AI tools across enterprise-wide workflows isn’t a guaranteed marker of success when it comes down to true value creation for investors and capital markets. 

The foundational stages of the past couple of years represent critical building blocks for the future of the AI industry. However, this has been a highly capital-intensive process. In turn, it creates something of a chicken-and-egg scenario. Wide-scale adoption is key to stabilizing the next stage of maturation for AI. However, gains through the scaling law will rapidly begin to plateau or become cost-prohibitive due to the expenses associated with each computing request across the supply chain.  

According to S&P Global, “the physical limitations of deployment are becoming the dominant risk to AI’s trajectory. Existing constraints in data center buildouts--specifically power availability and chip supply--will likely persist as significant challenges through 2026 and beyond.” 

This is in part why, according to Ness CEO Dr. Ranjit Tinaikar, engineering productivity represents the real north star metric for AI-enabled value creation in 2026. AI delivers value only when engineering systems, teams, and decisions are designed for productivity not experimentation, said the executive. 

The approach makes best use of available computational resources, from developer teams and data center capacity through to software and hardware. Only then can companies enter a virtuous cycle where improved productivity both removes capacity constraints and expands business value.

We’ve already begun to see traces of this transformation from last year. According to Tinaikar, “CPOs and CTOs are increasingly accountable not just for delivery, but for engineering economics. By 2026, product engineering maturity will be evaluated using AI-ready scorecards, with engineering productivity tied directly to valuation and capital allocation decisions.”

Better productivity frees up resources to modernize technology platforms and implement business-improving capabilities. Modernized platforms further increase productivity, which then helps to reduce technical debt. The result is a higher ROI from enterprise technology investment, which leads to larger budgets and even greater value creation. 

This will inevitably lead to new investment opportunities and ROI for investors in 2026. Added Nate MacLeitch, CEO of QuickBlox, which provides tools to build improved communication experiences, "AI use is no longer about experimentation alone; it’s about deployment with intent, especially in areas that relieve everyday work and improve access to care."

Examples of publicly traded companies to keep an eye on include AppLovin Corporation (APP), which reported $3.83 million in revenue per employee, and the streaming giant Netflix ((NFLX), which reported $2.98 million in revenue per employee.

New investment opportunities created as Digital Transformation evolves into AI Transformation  

The digital transformation market was estimated in 2025 to be approximately $1 trillion USD. At the same time, while the AI Transformation market was still catching up at $400 billion, the latter is also growing exponentially.

We expect this growth to continue, as the need for productivity signals a broader shift across the technological landscape that underpins the AI sector. 

In 2026 the race will move away from a focus on who can provide the best LLM, and turn into a battle for durable advantages in execution. This will require massive capital investments by enterprises and governments, creating opportunities for investors. 

We’ve already begun to see this play out at the start of this year. In January the venture capital firm NovaWave Capital, led by Ali Diallo, together with its anchor Limited Partner LG Electronics, announced a collaboration with Arizona Commerce Authority to launch a “WaveX” AI venture studio. The new studio is looking to support new ventures in AI across Arizona.

Given the importance of productivity as a metric for performance and value creation, we also expect to see growth in demand for tools and platforms that can plug this specific need. 

When it comes to the creation of these products, publicly traded companies to watch include Salesforce (CRM) , SoundHound (SOUN), and Snowflake (SNOW). 

Aligned to this transition towards AI transformation, we  also expect investment opportunities to arise in education. Coursera (COUR) offers AI and machine learning courses, while IvySchool.ai provides Ivy League courses to learn AI with expert instructors.

Consulting firms powered by AI to drive value creation 

High-performing organizations are asking, according to Ness CEO Ranjit Tinaikar, not “how much did we build?” but “how effectively did AI amplify our teams?” 

As such, we can expect to see a fresh wave of investment opportunities associated with specialist consultancies. 

These expert partners provide the expertise needed to realize AI projects across industries, making best use of available tools and the latest methodologies to ensure that initiatives can scale sustainably by optimizing engineering productivity. 

Without the presence of these stakeholders, the AI sector is already showing instability. Since June, the average stock price correlation across the large public AI hyperscalers has declined from 80% to just 20%, with the dispersion driven by the degree of investor confidence that AI investments are generating revenue benefits.

As corporate AI adoption acts as the true test for how much value AI technologies can truly generate in the real-world, firms that can offer expert guidance stand to establish a powerful position. 

Some forward-thinking companies have already begun to advance, with tech enterprise Prezent, led by Rajat Mishra, raising $30 million last year to acquire AI services firms, according to TechCrunch. The company is now valued at $400M USD.

According to Arda Ecevit, CEO of NexStrat, which is the first enterprise-grade AI management consultant, “The traditional consulting workflow hasn’t kept pace with the speed and complexity of modern business.” Added the founder, his company “allows business teams and consultants to deliver board-ready strategies and action plans within days instead of months, freeing them to focus on what humans do best."

When it comes to this space, some publicly traded consulting companies to watch include Booz Allen Hamilton (BAH)  and Marsh & McLennan (MRSH).

The next phase of AI maturation 

For investors, the AI build-out is generating massive opportunity, however it is also introducing earnings volatility across the technology sector. Depending on where a company sits in the supply chain, AI demand can either amplify upside or expose structural downside.

In 2026, the need for productivity is likely to boost the share value of direct platform providers and consultancies, creating new opportunities for investors while also underpinning the sustainability of the AI sector as a whole. 

Looking ahead, the long-term payoff will require the market to expand via higher IT spending as a percentage of GDP. As such, investment opportunities in this space will certainly increase, offering a new raft of emerging options.