In mid-2023 we laid out the case for an AI investment cycle reaching 2% of annual GDP, where 1) an initial surge in hardware investment necessary to train AI models and run AI queries would ultimately fade as compute costs decline while 2) AI software investment increases steadily over time as end-user adoption increases. This thesis has largely borne out so far, albeit in a more frontloaded manner than we expected.
在 2023 年中，我们提出了一个关于人工智能投资周期的论点，认为其将达到年 GDP 的 2%。其中 1）为了训练人工智能模型和运行人工智能查询，初期对硬件投资的激增最终会随着计算成本的下降而减退；2）随着最终用户采用率的提高，人工智能软件投资将随着时间的推移稳步增加。这个论点到目前为止基本得到了验证，尽管其前期表现比我们预期的更为明显。
This weekend’s reports that DeepSeek obtained similar AI model performance at a fraction of the cost of existing models raises questions around whether the cost disinflation stage of the AI investment cycle is also arriving sooner than expected, whether large AI investments are sustainable, and whether AI infrastructure companies will be able to capture an outsized revenue share. These are valid questions regarding the distribution of profits, but the macro implications of DeepSeek’s breakthrough are more limited and most likely positive.
本周末的报告显示，DeepSeek 以现有模型成本的一小部分获得了类似的 AI 模型性能，这引发了人们对 AI 投资周期的成本去通胀阶段是否也会比预期更早到来的质疑，关于大型 AI 投资是否可持续，以及 AI 基础设施公司是否能够获得超额收入份额。这些都是关于利润分配的有效问题，但 DeepSeek 突破的宏观影响更为有限，并且最有可能是积极的。
The main near-term risk to GDP is that more efficient model training and declining compute costs could lower AI-related capex. We are less concerned from a macro perspective since AI-related investment has so far had little impact on official GDP measures, thereby limiting downside even if investment does slow. Furthermore, our equity analysts note that DeepSeek could catalyze higher levels of real hardware spending if it pushes incumbents to invest more to maintain their lead.
近期对 GDP 的主要风险是更高效的模型训练和计算成本的下降可能会降低与 AI 相关的资本支出。从宏观角度来看，我们的担忧较小，因为迄今为止，与 AI 相关的投资对官方 GDP 指标的影响有限，因此即使投资放缓，向下的风险也受到限制。此外，我们的股票分析师指出，如果 DeepSeek 促使现有企业增加投资以保持领先地位，可能会催化更高水平的实际硬件支出。
DeepSeek’s breakthrough could raise macroeconomic upside over the medium-term if its cost reductions help increase competition around the development of platforms and applications. Limited adoption is still the main bottleneck to unlocking AI-related productivity gains, and adoption would benefit from competition-induced acceleration in the buildout of AI platforms and applications. That said, the near-term adoption impact is probably limited since cost itself is not currently the main barrier to adoption.
DeepSeek 的突破如果能够通过降低成本来促进平台和应用的开发竞争，可能会在中期提高宏观经济的上行空间。有限的采用仍然是释放与人工智能相关的生产力提升的主要瓶颈，而采用将受益于竞争引发的人工智能平台和应用的加速建设。也就是说，短期内的采用影响可能有限，因为成本本身目前并不是采用的主要障碍。
The emergence of a credible global competitor to US-based AI leaders also raises upside risk to global adoption and productivity through two channels. First, the potential automation and productivity gains from generative AI are similar across major economies, and foreign adoption (particularly in major EMs) would benefit from the emergence of non-US based platforms and applications. Second, increased global competition could prompt governments to coordinate investments or lower regulatory barriers in efforts to spur adoption.
出现一个可信的全球竞争者来挑战美国的人工智能领导者，也通过两个渠道提高了全球采用和生产力的上行风险。首先，生成性人工智能带来的潜在自动化和生产力提升在主要经济体之间是相似的，外国采用（特别是在主要新兴市场）将受益于非美国平台和应用的出现。其次，全球竞争的加剧可能促使各国政府协调投资或降低监管壁垒，以推动采用。
We have argued in a series of publications over the last two years that generative artificial intelligence (AI) could raise labor productivity and global growth, primarily from its ability to automate a large share of work tasks. Our baseline estimates imply 15% cumulative gross upside to US labor productivity and GDP levels (assuming the capital stock evolves to match increased labor potential) following widespread adoption of the technology.
我们在过去两年的一系列出版物中提出，生成性人工智能（AI）可能会提高劳动生产率和全球增长，主要是因为它能够自动化大量工作任务。我们的基线估计表明，在广泛采用该技术后，美国劳动生产率和 GDP 水平将有 15%的累计总上行（假设资本存量随着劳动潜力的增加而演变）。
We also laid out the case in mid-2023 for an AI investment cycle reaching 2% of annual GDP, where 1) hardware investment necessary to train AI models and run AI queries initially surges but then fades as compute costs decline while 2) AI software investment increases steadily over time as end-user adoption increases (Exhibit 1).
我们还在 2023 年中期提出了一个案例，认为人工智能投资周期将达到年 GDP 的 2%，其中 1）用于训练人工智能模型和运行人工智能查询的硬件投资最初激增，但随着计算成本的下降而逐渐减弱；2）随着最终用户采用的增加，人工智能软件投资将随着时间的推移稳步增加（见图 1）。
The investment boom we forecasted has largely borne out so far, although admittedly in a much more frontloaded manner than we anticipated. AI hardware provider revenues surged by over $200bn (annualized rate) as of 2024Q4, and are projected to rise by another $125bn by end-2025.
我们预测的投资热潮到目前为止基本上得到了验证，尽管确实比我们预期的更为前置。到 2024 年第四季度，人工智能硬件供应商的收入激增超过 2000 亿美元（年化率），预计到 2025 年底将再增长 1250 亿美元。
This weekend’s news that DeepSeek’s R1 AI model compares favorably in terms of performance to current leading models despite using less advanced hardware and costing much less to develop (left chart, Exhibit 2) has raised questions around whether the next stage in the investment cycle—where cost savings on compute costs lower the nominal hardware investment necessary to train and use generative AI models—is also arriving earlier than expected.
本周末的消息显示，尽管 DeepSeek 的 R1 AI 模型使用的硬件不如当前领先模型先进且开发成本低得多（左侧图表，展品 2），但其性能表现仍然相当，这引发了人们对投资周期下一个阶段的质疑——即计算成本的节省是否会降低训练和使用生成性 AI 模型所需的名义硬件投资，并且这一阶段是否也会比预期更早到来。
As noted by our equity analysts, there are valid reasons to question whether DeepSeek’s reported $5.6mn training cost fully reflects the cost of development or the hardware it was trained on. That said, DeepSeek has demonstrated that using novel computational techniques for model inferencing (i.e., multi-layer attention (MLA) and Mixture Of Experts (MoE)) and more efficient model training make it possible to produce highly capable AI models with more limited resources and at lower costs than previously believed. This breakthrough has potentially changed the competitive landscape for generative AI by challenging the widely held view that prohibitive investment costs are a barrier to entry at the foundational model level and raising the prospect that compute costs could continue to fall in line with historical trends (right chart, Exhibit 2).
正如我们的股票分析师所指出的，有充分的理由质疑 DeepSeek 报告的 560 万美元培训成本是否完全反映了开发成本或其所使用的硬件成本。尽管如此，DeepSeek 已经证明，使用新颖的计算技术进行模型推理（即多层注意力（MLA）和专家混合（MoE））以及更高效的模型训练，使得在资源更有限和成本更低的情况下，生产出高能力的人工智能模型成为可能。这一突破可能改变生成性人工智能的竞争格局，挑战了普遍认为的高昂投资成本是基础模型层面进入壁垒的观点，并提高了计算成本可能继续按照历史趋势下降的前景（右图，展览 2）。
From a macro perspective, the emergence of DeepSeek’s lower-cost models does not affect our view that the largest aggregate economic gains will come from the productivity boost enabled by generative AI.
从宏观角度来看，DeepSeek 低成本模型的出现并未影响我们对最大整体经济收益将来自生成性人工智能所带来的生产力提升的看法。
If our longer-run estimates are correct and AI-enabled task automation raises the level of aggregate productivity by 15% over roughly 10 years, generative AI would unlock around $4½tn (=15% * $29.3tn US GDP) in annual value for the US economy (in 2024 dollars). This economic surplus will be split among all agents in the US economy, including (following the framework developed by our equity portfolio strategists) Phase 1 and 2 hardware and infrastructure providers, Phase 3 “enablers” that will develop AI platforms and applications, Phase 4 “AI productivity companies” that will use generative AI to improve productivity and realize efficiency gains, workers, and consumers.
如果我们的长期估计是正确的，且人工智能驱动的任务自动化在大约 10 年内将整体生产力水平提高 15%，那么生成性人工智能将为美国经济（以 2024 年美元计）释放约 4.5 万亿美元（=15% * 29.3 万亿美元美国 GDP）的年度价值。这一经济盈余将分配给美国经济中的所有参与者，包括（根据我们股票投资组合策略师开发的框架）第一和第二阶段的硬件和基础设施提供商、第三阶段的“赋能者”将开发人工智能平台和应用程序、第四阶段的“人工智能生产力公司”将利用生成性人工智能提高生产力并实现效率提升、工人和消费者。
The emergence of DeepSeek’s lower cost models raises valid questions around the distribution of the economic surplus created by generative AI across these various stakeholders. The distribution of economic rents depends on a number of factors, including market concentration, intellectual property rights, scalability, and ultimately competition. It is still too early to have confidence in the effect that DeepSeek's reported training innovations will have on the broader ecosystem. However, if expensive hardware and AI infrastructure are less essential to realize the economic potential of generative AI, then companies engaged in the buildout of physical infrastructure would likely capture a smaller share of the overall economic gains as profits. This view is reflected by the equity market’s reaction over the last few days.
DeepSeek 低成本模型的出现引发了关于生成性人工智能所创造的经济剩余在各个利益相关者之间分配的合理质疑。经济租金的分配取决于多个因素，包括市场集中度、知识产权、可扩展性，以及最终的竞争。现在还为时已晚，无法对 DeepSeek 报告的培训创新对更广泛生态系统的影响充满信心。然而，如果昂贵的硬件和人工智能基础设施对实现生成性人工智能的经济潜力不再那么重要，那么参与物理基础设施建设的公司可能会在整体经济收益中获得更小的利润份额。这一观点在过去几天的股市反应中得到了体现。
To provide a historical example of how the economic value created by technological change is distributed, in Exhibit 3 we report the estimated contributions of software—reflecting both changes in investment and productivity—to output growth from 1981 to 2012 based on an approach developed by researchers at the Federal Reserve Board.[1] We then compare those contributions to those from software spending, which represent the economic value that accrued to software companies. This analysis suggests that software spending accounted for about a quarter of the economic gains from software adoption, as the significant market power of a few leading “superstar” firms[2] enabled them to capture a large share of the overall economic value. This dynamic is a main reason why average software spend has risen to around $4,500 per US worker in 2024.
为了提供一个关于技术变革所创造的经济价值如何分配的历史例子，在展览 3 中，我们报告了 1981 年至 2012 年间软件对产出增长的估计贡献——反映了投资和生产力的变化，基于美联储委员会研究人员开发的方法。[1] 然后，我们将这些贡献与软件支出的贡献进行比较，后者代表了流入软件公司的经济价值。这项分析表明，软件支出约占软件采用带来的经济收益的四分之一，因为少数领先的“超级明星”公司的显著市场力量[2]使它们能够捕获整体经济价值的很大一部分。这一动态是导致 2024 年美国每位工人的平均软件支出上升至约 4,500 美元的主要原因。
While uncertainty around the distribution of the economic surplus created by generative AI is clearly important from the perspective of equity investors, it is less relevant for the macroeconomic outlook since GDP (i.e., the value of production) does not depend on who benefits specifically. As a result, we see the macroeconomic implications of DeepSeek’s breakthrough as somewhat limited, and most likely positive on net.
尽管关于生成性人工智能所创造的经济盈余分配的不确定性对于股权投资者来说显然很重要，但对于宏观经济前景而言，这一点的相关性较低，因为 GDP（即生产的价值）并不依赖于具体受益者。因此，我们认为 DeepSeek 的突破在宏观经济方面的影响有限，并且最有可能是净正面的。
The main near-term risk to GDP is that more efficient model training and declining compute costs could lower AI-related capex, which as noted above, equity analysts project will rise to $325bn by 2025Q4. However, we view this risk as limited for two reasons.
GDP 的主要短期风险在于，更高效的模型训练和计算成本的下降可能会降低与人工智能相关的资本支出，正如上述所提到的，股票分析师预计到 2025 年第四季度这一数字将上升至 3250 亿美元。然而，我们认为这一风险是有限的，原因有二。
First, AI-related investment has so far had limited impact on real investment growth in official GDP accounts (Exhibit 4) despite reported increases in AI-related capex at the firm level, likely for several reasons. First, public company revenues are generally reported in nominal terms, which have been boosted by cost inflation despite more moderate increases in the quantity of shipments (which are more relevant for real GDP calculations). Second, publicly reported AI revenues reflect global, not just US, spending. Third, difficulties in identifying semiconductors imported as intermediate vs. final investment goods could lead to some undermeasurement. Fourth, timing differences in the recording of national accounts investment (which is measured at the time of delivery) may be limiting the measured AI impact. Regardless, the lack of a positive investment impulse thus far limits GDP downside even if AI-related investment does slow.
首先，尽管在公司层面报告了与人工智能相关的资本支出增加，但到目前为止，与人工智能相关的投资对官方 GDP 账户中的实际投资增长的影响仍然有限（见图表 4），这可能有几个原因。首先，上市公司的收入通常以名义金额报告，尽管出货量的增长较为温和，但由于成本通胀的影响，名义收入有所增加（出货量的变化对实际 GDP 计算更为相关）。其次，公开报告的人工智能收入反映的是全球而不仅仅是美国的支出。第三，识别作为中间品与最终投资品进口的半导体的困难可能导致某些低估。第四，国家账户投资的记录时间差（在交付时进行测量）可能限制了人工智能的测量影响。无论如何，迄今为止缺乏积极的投资冲动，即使与人工智能相关的投资确实放缓，也限制了 GDP 的下行风险。
Second, our equity analysts do not expect companies to significantly adjust capital allocation on the back of the recent DeepSeek news. While they acknowledge some risk that the build-out of AI infrastructure could be negatively affected should current leaders reassess their forward capex plans, they also highlight that DeepSeek could catalyze higher levels of real hardware spending if it pushes incumbents to invest more to maintain their lead in AI capability.
其次，我们的股票分析师并不预计公司会因最近的 DeepSeek 消息而显著调整资本配置。虽然他们承认，如果当前领导者重新评估其未来的资本支出计划，AI 基础设施的建设可能会受到负面影响，但他们也强调，如果 DeepSeek 促使现有企业增加投资以维持其在 AI 能力上的领先地位，这可能会催化更高水平的实际硬件支出。
More fundamentally, if the novel computational techniques employed by DeepSeek’s models indeed increase competition and lower costs, they could catalyze a faster buildout of AI platforms and applications that have so far posed a bottleneck to adoption and the impact on productivity, thereby raising macroeconomic upside.
更根本地说，如果 DeepSeek 模型所采用的新计算技术确实增加了竞争并降低了成本，它们可能会催化 AI 平台和应用的更快建设，而这些平台和应用迄今为止一直是采用和生产力影响的瓶颈，从而提高宏观经济收益。
As discussed above, the main macroeconomic impact from generative AI will come from the efficiency gains from AI-driven automation as companies incorporate the technology into regular production. So far, any aggregate productivity uplift from generative AI has been extremely limited, mostly because very few companies have adopted the technology. As shown in Exhibit 5, the Census Bureau’s Business Trends and Outlook Survey reports that only 6% of companies report using AI for regular production today, only a slight uptick from the 4% adoption rate when Census began collecting data in late 2023.
如上所述，生成性人工智能对宏观经济的主要影响将来自于企业将该技术纳入常规生产所带来的效率提升。目前，生成性人工智能带来的整体生产力提升非常有限，主要是因为采用该技术的公司非常少。如图 5 所示，人口普查局的商业趋势和展望调查报告显示，目前只有 6%的公司报告在常规生产中使用人工智能，这仅比 2023 年底人口普查开始收集数据时的 4%的采用率略有上升。
We have long expected that adoption rates would rise in the medium-term, mostly because the types of work tasks automatable by generative AI would result in several thousands of dollars of cost savings per worker per year (Exhibit 6). Given that potential cost savings from generative AI are large and the marginal cost of deployment once applications are developed will likely be very small, we see adoption of generative AI as more of question of “when” rather than “if”.
我们早已预期，中期内采用率将会上升，主要是因为生成性人工智能能够自动化的工作任务类型每位员工每年将节省数千美元的成本（见图 6）。考虑到生成性人工智能的潜在成本节省巨大，并且一旦应用程序开发完成，部署的边际成本可能非常小，我们认为采用生成性人工智能更多是一个“何时”的问题，而不是“是否”的问题。
The potential for a faster buildout of AI platforms and applications—which we continue to see as the necessary step to facilitate adoption across a wide swath of companies—raises the prospect of a more optimistic adoption and productivity boost timeline. Our forecasts currently assume that US adoption reaches levels necessary to impact aggregate productivity statistics in 2027 with a peak impact in the early 2030s, with other DMs and major EMs lagging this timeline by a few years. The recent DeepSeek reports suggest adoption could happen sooner, thereby reinforcing our equity analysts’ and portfolios strategy team’s prior views that investors should increasingly focus on the “Phase 3” platform and application companies that will benefit from AI-enabled revenues.
AI 平台和应用程序的更快建设潜力——我们继续认为这是促进广泛公司采用的必要步骤——提高了更乐观的采用和生产力提升时间表的前景。我们目前的预测假设美国的采用在 2027 年达到影响整体生产力统计所需的水平，峰值影响将在 2030 年代初期出现，而其他发达市场和主要新兴市场将在这一时间表上滞后几年。最近的 DeepSeek 报告表明，采用可能会更早发生，从而加强了我们股票分析师和投资组合策略团队之前的观点，即投资者应越来越关注将从 AI 驱动收入中受益的“第三阶段”平台和应用公司。
At the same time, we caution that the near-term impact will remain limited until the anticipated application buildout becomes a reality. Few companies report costs as a primary barrier to adoption (Exhibit 7), suggesting that most companies in the US are waiting for a “plug and play” solution that facilitates easy automation of existing business practices. Until available, we expect that adoption rates (and associated macroeconomic upside) will remain low.
与此同时，我们警告说，短期影响将保持有限，直到预期的应用建设成为现实。很少有公司将成本报告为采用的主要障碍（见图 7），这表明美国大多数公司正在等待一种“即插即用”的解决方案，以便于现有业务实践的轻松自动化。在此之前，我们预计采用率（及相关的宏观经济上行空间）将保持低位。
We also see DeepSeek’s reported breakthrough as posing upside risk to global GDP. The emergence of a credible competitor to US-based AI leaders could provide an uplift to global adoption and productivity through two channels.
我们还看到 DeepSeek 报告的突破对全球 GDP 构成了上行风险。对美国人工智能领导者的一个可信竞争者的出现可能通过两个渠道提升全球的采用率和生产力。
First, the potential automation and productivity gains from generative AI are generally similar across major economies, reflecting similarities in industry composition of employment (Exhibit 8). While we still expect that the US will adopt AI more quickly than other countries given its leadership in AI model development, the emergence of non-US based platforms and applications could accelerate the adoption timeline elsewhere (particularly in major EMs).
首先，生成性人工智能带来的潜在自动化和生产力提升在主要经济体之间通常是相似的，这反映了就业行业构成的相似性（见图 8）。虽然我们仍然预计美国将在人工智能模型开发方面的领导地位下比其他国家更快地采用人工智能，但非美国本土平台和应用的出现可能会加速其他地方的采用时间表（特别是在主要新兴市场）。
Second, global governments could see the recent breakthroughs of China-based AI models as raising the importance of developing domestic AI capabilities for geopolitical purposes. If so, increased global competition could prompt governments to coordinate investments or lower regulatory barriers to encourage AI development and adoption. Along these lines, the US recently announced a $500bn private sector funding scheme to raise AI infrastructure investment, while China has set a strategic objective to become the global leader in AI. Increased focus and support from an AI arms race could similarly lead to an accelerated adoption timeline.
其次，全球各国政府可能会将中国基于 AI 模型的最新突破视为提升发展国内 AI 能力以满足地缘政治目的的重要性。如果是这样，全球竞争的加剧可能促使各国政府协调投资或降低监管壁垒，以鼓励 AI 的发展和应用。在这方面，美国最近宣布了一项 5000 亿美元的私营部门融资计划，以提高 AI 基础设施投资，而中国则设定了成为全球 AI 领导者的战略目标。AI 军备竞赛的关注和支持增加也可能导致加速采用的时间表。
Taken together, the recent emergence of DeepSeek’s low-cost AI models have prompted a rethink of the overall AI investment thesis. While we are sympathetic to the view that these developments raise company-level micro risk, they have also, if anything, added to our confidence that AI-enabled productivity gains will be a major macroeconomic story in coming years.
综合来看，DeepSeek 最近出现的低成本 AI 模型促使人们重新思考整体 AI 投资论点。虽然我们对这些发展提高公司层面微观风险的观点表示理解，但它们在某种程度上也增强了我们对 AI 驱动的生产力提升将在未来几年成为一个重要宏观经济故事的信心。
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