Author name: spacessor.ad@gmail.com

What to expect from AI in 2025: hybrid workers, robotics, expert models More than two years after ChatGPT’s debut, Goldman Sachs Chief Information Officer Marco Argenti says the potential for generative artificial intelligence is coming into focus. The development of increasingly powerful large language models (LLMs), supercharged by advances in robotics will, in Argenti’s view, begin to bring sweeping changes to everything from employment to regulation of the technology in 2025. Argenti, the former vice president of technology of Amazon Web Services, makes five predictions about how AI could evolve and interact with businesses and society in the near future:   The new hybrid workforce: AI systems are becoming more like humans. So why not employ them like humans? The capabilities of AI models to plan and execute complex, long-running tasks on humans’ behalf will begin to mature. This will create the conditions for companies to eventually “employ” and train AI workers to be part of hybrid teams of humans and AIs working together. The question becomes: How best to get humans and AI to work together. Companies will reskill human managers to oversee a hybrid workforce. The role of human resources will evolve into a department for human and machine resources. The first AI “layoffs” could eventually emerge, in which AI models will be replaced by better AI tools or humans if they perform poorly compared to their peers.   The emergence of expert AI: The AI version of PhDs will arrive. Companies will integrate AI with their proprietary data, either with retrieval-augmented generation (RAG) — an architecture that can connect LLMs to external, specialized datasets — or via a process known as fine-tuning, which involves enhanced training of an LLM with a smaller, specialized dataset. As a result, expert AI systems, or large expert models, will gradually emerge with advanced capabilities and industry-specific knowledge — for example, specialized models for medicine, robotics, finance, or material sciences.   Robotic breakthroughs powered by AI: So far, AI models have been trained by reading essentially all the books in the world. What if they’re trained on the world itself? Children learn to walk before they learn to read. In the same way, the intersection of LLMs and robotics will increasingly bring AI into, and enable it to experience, the physical world, which will help enable reasoning capabilities for AI. At the same time, these models will transform commodity hardware into specialized components capable of performing far beyond their default capabilities. Advanced cameras using cheap sensors, studio-quality microphones using low-cost transducers, and off-the shelf mechanical joints capable of performing complex manipulation tasks will drive down costs for combining advanced AI with robotics and will speed up innovation.   Regulation goes from global to local: As the world awaits regulatory clarity, responsible AI principles will take center stage for CEOs and boards.   In addition to (and somewhat separate from) national, state, or sectoral regulations, companies across sectors will continue to see the benefit of implementing proper controls, such as responsible AI principles (i.e., a form of self-regulation). Responsible AI will become an even bigger priority for CEOs and boards of major companies.   Large model consolidation: The Formula One experience arrives for AI. Given the cost and complexity of engine development in the Formula One motorsport competition, there are many cars but only a few engine makers. Likewise, the investment required to train and maintain large frontier models (those that are the largest and most advanced) for AI will eventually result in only a handful of providers. Consolidation will mirror what has taken place in cloud infrastructure, databases, and operating systems, where the total number of companies developing large AI engines will be countable on one hand. Startups that are now “model-centric” will shift towards building solutions that are model-agnostic, focusing instead on other aspects of AI such as compliance, safety, data integration, orchestration, automation, and user experience.

Is nuclear energy the answer to AI data centers’ power consumption? Nuclear power will be a key part of a suite of new energy infrastructure built to meet surging data-center power demand driven by artificial intelligence. But nuclear can’t meet all of the increased data-center power needs. Natural gas, renewables, and battery technology will also have a role to play, according to Goldman Sachs Research. Several big tech companies looking for low carbon, round-the-clock energy signed contracts for new nuclear capacity in the last year, and there could be more such deals ahead. Those efforts come as electricity usage by data centers is expected to more than double by 2030, according to reports led by Brian Singer, Jim Schneider, and Carly Davenport.  In total, the team forecasts 85-90 gigawatts (GW) of new nuclear capacity would be needed to meet all of the data center power demand growth expected by 2030 (relative to 2023). But well less than 10% will be available globally by 2030.   As power needs ramp up, the efficiency gains of data center infrastructure are beginning to slow, according to Davenport, a US utilities research analyst in Goldman Sachs Research. “Growth from AI, broader data demand, and a deceleration of power efficiency gains is leading to a power surge from data centers,” she writes.   How much is AI power consumption expected to rise? Power demand from data centers is on track to grow more than 160% by 2030, compared to 2023 levels, Goldman Sachs Research projects. A scenario in which 60% of that increased demand was met by thermal sources such as natural gas would lead to an expected emissions increase of 215-220 million tons globally, equivalent to 0.6% of the world’s energy emissions. While renewables have the potential to meet most of the increased power needs from data centers at some times of day, they don’t produce power consistently enough to be the only energy source for data centers, explains Schneider, a digital infrastructure analyst in Goldman Sachs Research. “Our conversations with renewable developers indicate that wind and solar could serve roughly 80% of a data center’s power demand if paired with storage, but some sort of baseload generation is needed to meet the 24/7 demand,” Schneider writes. He adds that nuclear is the preferred option for baseload power, but the difficulty of building new nuclear plants means that natural gas and renewables are more realistic short-term solutions. Nuclear energy has almost zero carbon dioxide emissions — although it does create nuclear waste that needs to be managed carefully. But the scarcity of specialized labor, the challenges of obtaining permits, and the difficulty of sourcing sufficient uranium all pose a challenge to the development of new nuclear power plants. By the 2030s, though, new nuclear energy facilities and developments in AI could start to bring down the overall carbon footprint of AI data centers. In the meantime, companies trying to supply energy for new data centers are likely to focus on a mix of power sources, writes Singer, global head of GS SUSTAIN in Goldman Sachs Research. “Our outlook on power demand growth warrants an ‘and’ approach, not an ‘or’ approach, as we see ample opportunities for generation growth across sources,” he writes.   How much will nuclear power increase? Recent contracts for nuclear energy facilities along with signs of countries’ greater appetite for nuclear power suggest a significant increase of investment in the next five years, and a corresponding rise in power supply in the 2030s. The proliferation of AI data centers has boosted investor confidence in future growth in electricity demand at the same time as big tech companies are looking for low-carbon reliable energy. This is leading to the de-mothballing of recently retired nuclear generators, as well as consideration for new larger-scale reactors. In the US alone, big tech companies have signed new contracts for more than 10 GW of possible new nuclear capacity in the last year, and Goldman Sachs Research sees potential for three plants to be brought online by 2030. Meanwhile, governments are also broadly more supportive of nuclear power. Switzerland is reconsidering the use of nuclear generators for its electricity supply, while nuclear power enjoys bipartisan support in the US, and the Australian opposition party has put forward plans to introduce nuclear reactors. Participants at the COP28 conference in late 2023, an annual summit convened by the UN to address climate change, agreed to triple global nuclear capacity by 2050.   Building the ‘green’ data center Green energy sources are also receiving considerable investment from AI providers. The team forecasts that 40% of the new capacity built to support increased power demand from data centers will be renewables. The supply cost of renewable energy sources is cheaper than generating electricity from natural gas, before taking into account transmission considerations and filling gaps when the sun isn’t shining and the wind isn’t blowing. Analysis by Goldman Sachs Research shows that, at face value, the average cost of energy for onshore wind hosted on the site of a data center is $25 per megawatt hour in the US, while solar energy costs $26/MWh, and combined cycle natural gas (the most fuel-efficient type of gas-fired power plant) costs $37/MWh before accounting for the cost of carbon capture. In practice, though, utility-scale solar plants only run around 6 hours per day on average, while wind plants run for an average of 9 hours per day. There is also day-to-day volatility in the capacity of these sources, depending on the radiance of the sun and the strength of the wind. Transmission costs are also a consideration for data center operators. Because renewable energy sources often take up a much greater land footprint than natural gas or nuclear, they are more likely to be located away from big cities, where much of the energy that they generate is used. As a result, the energy they generate may have to travel further before it is used. On the other hand, thermal plants — such as those powered by nuclear reactors

China’s AI development could speed up AI adoption The launch of three new Chinese generative artificial intelligence models sent shockwaves through the tech sector. The share price of a basket of AI-related stocks in the US dropped 10% in the first two days of this week. The development highlights the sheer amount of capital that’s been invested in AI by US technology giants so far, as well as the investment that will be needed to scale the technology going forward, according to Goldman Sachs Research. It remains to be seen exactly how some of the new models were trained, with emerging questions on sources of data. But there are also signs that the developments in China could lower the cost of running chatbot apps, which can be used for everything from coding software to writing sonnets, and make them more widely available. “What’s clear to us is that lowering the cost of AI models will drive much higher adoption, as it would make the models much cheaper to use in future,” says Ronald Keung, the head of Goldman Sachs Research’s Asia internet team. “Some of these Chinese models have driven the industry to focus not just on raising the performance, but also on lowering the cost.” We spoke with Keung about his take on the new generative AI models in China, the cost savings they may provide, and breakthroughs they may enable. As costs decline and AI models become smarter, Keung says, we may be a small step closer to reaching artificial general intelligence — an AI that displays excellence across all human fields of knowledge.   What is important about the recent AI developments in China? Three Chinese AI models were launched last week, as well as two multi-modal text-to-image models this week. And while most of the attention has been on DeepSeek’s new model, the other models are at around the same level in terms of performance and cost per token (a token is a small unit of text). The cost of inferencing (the stage that comes after training, when an AI model works with content that it has never seen before) has fallen by more than 95% in China over the past year. We expect this much lower inferencing cost to drive a proliferation of generative AI applications. Some of the models launched over the past week are focused on deep thinking modes or reasoning. That means that the chat bot goes through each of its steps when you ask a question, telling you what it’s thinking before it arrives at an answer. That takes around 5-20 seconds for every question. The process makes sense when you look at how human beings interact — if you ask me a question, and then I give you an immediate answer in milliseconds, then the chance is that I might not have thought it through. These models think before they speak. The performances of these models seem to have improved a lot as a result. It’s mostly because they assess their own answers before giving a final output.   Are these developments likely to change the way that capital is invested in AI? Chinese players have been focused on driving the lowest cost, and also maybe trying to use minimal chips in doing the same tasks. I think over the last week, there’s also been more focus on whether edge computing is becoming more popular, which could allow smaller AI models to run on your phone or computer without connecting to mega data centers. I think these are all questions that investors have on how the landscape will evolve. What is clear to us is that lowering the cost of AI models will drive much higher adoption, as it would make the models much cheaper to use in future. Both our research teams in China and our US teams expect this year to be the year of AI agents and applications. The good news is that some of these Chinese models have pushed the industry to focus not just on raising the performance, but also on lowering the cost. That should drive higher and higher adoption of artificial intelligence.   How much cheaper are the AI models in China relative to the incumbent AI providers in the US? When it comes to how much the companies charge per use of the model, which is measured on a per-token basis, the charges are significantly lower. As of last weekend, a Chinese AI model’s pricing was 14 cents per million input tokens. That’s only a single-digit percentage of the amount that an equivalent reasoning model from a large US technology company charges. It’s clear that prices are starting to come down as a result. Already, we’ve seen some US big tech companies adjust their pricing, including making some of their paid models free. So I think there will be a continuing race on efficiencies.   Your colleagues from the US tech team have said that the market reaction is being driven by questions around the amount of capital expenditure needed to scale AI, the return on investment on the money already spent, and the pace of investment going forward. What is your take on those questions as it relates to Chinese firms? I think that applies a lot more to US companies. The major Chinese internet giants increased capital expenditure by 61% last year — but that was from a low base. The listed Chinese companies have not been spending as much on capex over the last two years, in aggregate. Instead, they’ve been very focused on shareholder returns. Spending has only just started to pick up for these companies in 2024. However, in absolute terms, Chinese Internet companies have been spending just a fraction of what their global counterparts have been spending, so there are fewer questions on the return-on-investment that they can expect from high spending on AI.   Investor focus on AI has been concentrated on the US. Do you think this will mean more investor interest in Chinese companies in the

What advanced AI means for China’s economic outlook The explosion in interest in generative artificial intelligence has resulted in an arms race to develop the technology, which will require many high-density data centers as well as much more electricity to power them. Goldman Sachs Research forecasts global power demand from data centers will increase 50% by 2027 and by as much as 165% by the end of the decade (compared with 2023), writes James Schneider, a senior equity research analyst covering US telecom, digital infrastructure, and IT services, in the team’s report.   Recent Chinese developments, and particularly the AI model known as DeepSeek, have raised concern about the returns on current and projected AI investment. Still, several questions remain about DeepSeek’s training, infrastructure, and ability to scale. “In the long run, if we see efficiency driving lower capex levels (from either hyperscalers or new investment plans from new players), this would mitigate the risk of long-term market oversupply we see in 2027 and beyond – which we think is an important consideration that could drive more durability and less cyclicality in the data center market,” says Schneider. On the demand side for data centers, large “hyperscale” cloud providers and other corporations are building large language models (LLMs) capable of natural language processing and understanding. These models must be trained on vast amounts of information, using power-intensive processors. On the supply side, hyperscale cloud companies, data center operators, and asset managers are deploying large amounts of capital to build new high-capacity data centers. Taken together, the balance of data center supply and demand is forecast by Goldman Sachs Research to tighten in the coming years. The occupancy rate for this infrastructure is projected to increase from around 85% in 2023 to a potential peak of more than 95% in late 2026. That will likely be followed by a moderation starting in 2027, as more data centers come online and AI-driven demand growth slows.   How much power will data centers require for AI? At present, Goldman Sachs Research estimates the power usage by the global data center market to be around 55 gigawatts (GW). This is comprised of cloud computing workloads (54%), traditional workloads for typical business functions such as email or storage (32%), and AI (14%). By modeling future demand for each of these workload types, our analysts project power demand will reach 84 GW by 2027, with AI growing to 27% of the overall market, cloud dropping to 50%, and traditional workloads falling to 23%. This baseline scenario could, however, be affected by a deceleration in usage by AI — for example, if the transition to AI-driven work and AI monetization doesn’t develop as quickly as anticipated. In such muted scenarios, demand could diverge from the baseline estimate by 9-13 GW.   The global landscape of data center supply The current global market capacity of data centers is approximately 59 GW. Roughly 60% of this capacity is provided by hyperscale cloud providers and third-party wholesale data center operators (these providers usually have a small number of very large enterprise customers). The remaining belongs to more traditional corporate and telecom-owned data centers. The AI-dedicated data center is an emerging class of infrastructure. Although very few exist so far, they’re designed for the unique properties of AI workloads — high absolute power requirements, higher power density racks, and the additional hardware (such as liquid cooling) that comes with it. They’re usually owned by hyperscalers or wholesale operators. Regionally, Asia Pacific and North America have the most data center power and square footage online today — most notably in regions such as Northern Virginia, Beijing, Shanghai, and the San Francisco Bay Area. These places have high compute and data traffic as well as robust corporate campus demand.   How data center supply is rising around the world Goldman Sachs Research estimates that there will be around 122 GW of data center capacity online by the end of 2030. The mix of this capacity is expected to skew even further towards hyperscalers and wholesale operators (70% versus 60% today). Although Asia Pacific has added the most supply over the past ten years by a wide margin, North America has the most scheduled capacity coming online over the next five years. Given the higher processing workloads demanded by AI, the density of power use in data centers is likely to grow as well, from 162 kilowatts (kW) per square foot to 176 kW per square foot in 2027. (These figures exclude power overheads such as cooling or other functions related to data center infrastructure.) “Data center supply — specifically the rate at which incremental supply is built — has been constrained over the past 18 months,” Schneider says. These constraints have arisen from the inability of utilities to expand transmission capacity because of permitting delays, supply chain bottlenecks, and infrastructure that is both costly and time-intensive to upgrade.   Power demand from data centers will require additional utility investment As data centers contribute to a growing need for power, the electric grid will require significant investment. Goldman Sachs Research estimates that about $720 billion of grid spending through 2030 may be needed. “These transmission projects can take several years to permit, and then several more to build, creating another potential bottleneck for data center growth if the regions are not proactive about this given the lead time,” Schneider says. In Europe too, a data center-led surge in power demand is under way, after 15 years of decline in the power sector. Having surveyed utilities across the continent, Goldman Sachs Research found that the number of connection requests received by power distribution operators (a leading indicator of future demand) has risen exponentially over the past couple of years, mostly driven by data centers. “We estimate a potential 10-15% boost to Europe’s power demand, over the coming 10-15 years,” Alberto Gandolfi, head of the pan-European utilities team for Goldman Sachs Research, writes in a separate report. Goldman Sachs Research estimates a data center pipeline for Europe amounting to about 170 GW,