{"id":20600,"date":"2026-01-26T18:51:02","date_gmt":"2026-01-26T18:51:02","guid":{"rendered":"https:\/\/nationalgunowner.org\/index.php\/2026\/01\/26\/goldman-sachs-cio-7-ways-a-i-will-reshape-the-global-economy-in-2026\/"},"modified":"2026-01-26T18:51:05","modified_gmt":"2026-01-26T18:51:05","slug":"goldman-sachs-cio-7-ways-a-i-will-reshape-the-global-economy-in-2026","status":"publish","type":"post","link":"https:\/\/nationalgunowner.org\/index.php\/2026\/01\/26\/goldman-sachs-cio-7-ways-a-i-will-reshape-the-global-economy-in-2026\/","title":{"rendered":"Goldman Sachs CIO: 7 Ways A.I. Will Reshape the Global Economy in 2026"},"content":{"rendered":"
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Marco Argenti outlines seven predictions showing how A.I. agents, power constraints and mega-partnerships will redefine capitalism. Cash Macanaya\/Unsplash<\/span><\/figcaption><\/figure>\n

A.I. models are becoming more than just chatbots\u2014an important step in their evolution that will have repercussions for the global economy in 2026 and beyond, says Marco Argenti<\/a>, Goldman<\/span>\u00a0Sachs<\/span>\u2019 chief information officer.<\/p>\n

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\u201cIn my 40 years in technology, 2025 saw the biggest changes I have seen in my career,\u201d Argenti says. \u201cAnd what\u2019s crazy is we haven\u2019t seen anything yet\u2014in fact, I predict 2026 will be an even bigger year for change.\u201d<\/p>\n

A.I. has emerged as a critical driver for financial markets and potentially for the broader economy. Wall Street analysts, who have consistently underestimated the amount of investment going into A.I., expect the largest hyperscale cloud computing companies to pour more than half a trillion dollars into capital expenditures in 2026. The seven biggest tech companies now account for more than 30 percent of the S&P 500\u2019s market capitalization and roughly one quarter of the index\u2019s earnings, according to Goldman<\/span>\u00a0Sachs<\/span>\u00a0Research.<\/p>\n

Argenti, the former vice president of technology of Amazon Web Services<\/a> (AWS), says A.I. is rewiring everything from the traditional workforce to the traditional software stack.<\/p>\n

He makes seven predictions about how A.I. could evolve in the near future:<\/p>\n

A.I. models will be the new operating system<\/b><\/h3>\n

The traditional paradigm for software engineering is changing: Rather than functioning as one-dimensional applications, A.I. models are becoming operating systems that independently access tools in order to perform tasks.<\/p>\n

In turn, computing is evolving from static, hard-coded logic to outcome-based assistants that reprogram themselves. This makes A.I. agents much more capable of handling complex problems. As a result, those who own the models will own the new operating systems that power A.I. agents.<\/p>\n

Context is the new frontier<\/b><\/h3>\n

A.I. engineers\u2019 focus will shift from building \u201clarger models\u201d to \u201cbetter memory.\u201d Think of it this way: The models have been built from vast pools of data\u2014they\u2019ve scoured essentially the entire internet and then some in the form of synthetic data for model-training purposes. However, the immediate context available to models\u2014what they remember from previous discussions and tasks\u2014is relatively tiny. Already, some newer models are able to reason and inject much larger contexts into processes to provide far more bespoke, customized responses.\u00a0\u00a0<\/p>\n

The rise of the personal agent<\/b><\/h3>\n

A.I. personal agents will arrive, which is something companies have been chasing with varying degrees of success. What we do now with apps\u2014manually, and in piecemeal fashion\u2014will be done automatically soon. For example, if a flight is cancelled because of the weather, an AI agent will know to rebook the flight, reschedule meetings, and order food for afterwards (since restaurants will be closed). This is very possible with AI with agentic capabilities.<\/p>\n

The agent-as-a-service economy<\/b><\/h3>\n

Companies will shift from deploying human-centric staff to tackle tasks to deploying human-orchestrated fleets of specialized multi-agent teams. Instead of calculating billing by hours worked, these hybrid teams of humans and machines will charge clients by the amount of tokens\u2014the units of data used by A.I. models\u2014that are consumed.\u00a0\u00a0<\/p>\n

Learning becomes the most important skill<\/b><\/h3>\n

The workers who thrive will be the ones with expertise who are also the most willing to adapt.<\/p>\n

For those workers, the single biggest differentiator will be their ability to reimagine\u2014in an age where AI will help them to do their job\u2014something they\u2019ve been doing for many years. There\u2019s recent precedent for this: With the introduction of computers, people had to rethink many aspects of their work. AI is generating a change of that magnitude, which makes learning the most important skill.<\/p>\n

Winner-takes-most mega partnerships<\/b><\/h3>\n

AI is a game of scale, and there are going to be network effects from the very large upstream and downstream partnerships that are forming. Headline partnerships and strategic alliances of unprecedented scale will reshape the A.I. landscape. These networks will create a self-reinforcing cycle where only a handful of major players are capable of competing. In this way, A.I. may come to resemble complex major industries like aerospace that are characterized by duopolies.<\/p>\n

Power is the new capital<\/b><\/h3>\n

Scaling to meet the A.I. demand will hinge not just on capital, but on access to the utility grid: Goldman<\/span>\u00a0Sachs<\/span>\u00a0Research\u2019s base case is that power consumption from data centers will jump 175 percent by 2030<\/a> from 2023 levels (our analysts\u2019 previous forecast was for an increase of 165 percent). Capacity constraints, from access to new gas turbine power plants to electrical grid connectivity, mean access to electrical power will require the right set of relationships.\u00a0<\/p>\n

The sheer scale of the infrastructure necessary for A.I. data centers, the multi-year lead time to bring new power facilities online, and the rapid evolution of A.I. models will exacerbate the need for power in 2026, resulting in a gigawatt ceiling. Companies will obsess over allocating every megawatt of power to activities with the highest return.<\/p>\n

\u00a0<\/p>\n

The original version of this article appeared here<\/a>.\u00a0<\/em><\/p>\n

\t\t\t\t\"Goldman<\/p><\/div>\n