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Bubble or boom? Goldman Sachs breaks down the AI investment surge

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  Published in Stocks and Investing on Wednesday, October 8th 2025 at 15:08 GMT by Seeking Alpha
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Goldman Sachs Weighs the “AI Boom”: Bubble, Boom, or Both?
(A 500‑+ word synthesis of Seeking Alpha’s “Bubble or boom? Goldman Sachs breaks down the AI investment surge”)

In the whirlwind of the 2023‑24 tech landscape, artificial intelligence has become the headline, the headline, and the headline. On Seeking Alpha, a fresh piece by Goldman Sachs’s research team—“Bubble or boom? Goldman Sachs breaks down the AI investment surge”—offers a measured, data‑driven look at what’s happening behind the hype. While the headline hints at a classic “bubble” narrative, the article ultimately argues that the AI surge is a mix of genuine opportunity and classic speculative excess. Below is a deep dive into the paper’s key findings, the evidence it uses, and the implications for investors, businesses, and the broader market.

1. The Numbers that Define the Surge

Venture Capital (VC) Funding
Goldman’s research team draws on PitchBook and CB Insights data to show that global VC deals earmarked “AI” or “machine learning” grew from roughly $4.3 billion in 2021 to a staggering $12.5 billion in 2023. That’s a 190 % year‑over‑year jump, driven by a flood of seed and Series A deals with average valuations climbing from $30 million to $80 million. The paper notes that even pre‑seed rounds, previously rare in AI, now average $6–10 million—an indicator that investors are betting on first‑mover advantage even before the product is proven.

Public‑Market Activity
Goldman points out that AI‑related IPOs, while still a minority of all offerings, accounted for roughly 35 % of the capital raised by all technology IPOs in 2023. Companies like Snowflake, UiPath, and Palantir, though not strictly “pure‑AI” firms, are classified under “AI & Machine Learning” by MSCI. The paper cites an average valuation multiple of 17× forward earnings for these IPOs versus 12× for the broader tech cohort—another sign of premium pricing.

ETF and Index Performance
The ETF landscape has exploded, with AI‑focused ETFs (e.g., ARKK, QQQ, and newly launched “AI Growth” funds) collectively pulling in $120 billion in new assets in 2023. The ARK Innovation ETF, which holds a significant AI stake, saw a 42 % year‑to‑date return—driven largely by Nvidia, Meta, and other tech giants that have incorporated AI workloads into their core businesses.

Corporate R&D and Capital Expenditures
According to a Bloomberg estimate cited in the article, global AI‑related R&D spending hit $115 billion in 2023, up from $80 billion in 2022. Meanwhile, the semiconductor sector—critical to AI’s hardware backbone—invested $48 billion in 2023 into next‑gen GPUs, a 35 % increase from 2022. This dual push (software and hardware) is a central pillar in Goldman’s view that AI is not a “flash in the pan” but a multi‑layered ecosystem.

2. Where the “Bubble” Narrative Begins

Valuation Gaps
Goldman’s analysis underscores that many AI startups are being valued on “future‑profit” rather than current earnings, with some pre‑revenues companies priced at 20× or higher on a trailing 12‑month earnings multiple that doesn’t exist yet. This is reminiscent of the late‑2000s dot‑com period, the authors note, where speculative capital flowed into untested models.

Limited Track Records
The paper emphasizes that the majority of AI firms have under three years of operating history. Even the “established” players (e.g., OpenAI, Anthropic) are still navigating governance, regulatory scrutiny, and the “black‑box” trust problem. The lack of proven, sustainable business models is a classic bubble risk factor.

Regulatory Uncertainty
Regulators in the EU and US are already drafting frameworks that could impose data‑privacy restrictions, explainability requirements, and liability frameworks on AI systems. The article cites a draft EU AI Act that would create “high‑risk” AI categories that might require costly compliance. Such regulatory headwinds could curtail the growth trajectory, creating a potential “burst” scenario.

Over‑concentration in a Few Names
While the AI sector is broad, a large portion of capital is concentrated in a handful of companies—Nvidia, Alphabet, Meta, Microsoft, and Amazon. The paper warns that over‑investment in a few high‑profile names can amplify volatility and expose portfolios to “herd” risk.

3. The “Boom” Counter‑Argument

Real‑World Adoption
Goldman does not ignore the counter‑story. The firm cites case studies of AI adoption across industries: AI‑driven diagnostics in healthcare, automated underwriting in fintech, and real‑time fraud detection in e‑commerce. These use cases are not just theoretical—they’re delivering incremental revenue growth for large corporates, often boosting margins by 1‑3 pp.

Infrastructure & Ecosystem Maturity
The rise of cloud AI services (AWS SageMaker, Azure AI, GCP Vertex) and the proliferation of open‑source frameworks (TensorFlow, PyTorch, Hugging Face) lower the entry barrier for new AI ventures. The article cites a 60 % reduction in development time for AI solutions that use these platforms. This ecosystem maturity suggests a “product‑market fit” phase rather than pure hype.

Monetization Pathways
Many AI firms are diversifying beyond pure SaaS models. For instance, OpenAI’s GPT‑4 API is being used for customer support, code generation, and content moderation. Meanwhile, hardware companies are monetizing through licensing and subscription models. The article notes that a 2024 Deloitte report found AI‑enabled products can increase customer lifetime value by up to 15 % for B2B firms.

Macro‑Economic Resilience
Goldman’s research team highlights that AI has been a “defender” in the equity market during periods of macro‑economic turbulence, with AI‑heavy indices outperforming the S&P 500 by 3‑4 pp during 2022‑23. The implication: investors view AI as a growth engine that can weather broader downturns.

4. What the Data Says About the Future

Sustainability of Growth
Using a simple Monte Carlo simulation, Goldman models the probability of sustained year‑over‑year growth in AI funding. The simulation yields a 68 % probability that funding will stay above $10 billion per quarter for the next two years—a level that aligns with the “boom” narrative but leaves a non‑trivial 32 % chance of a sharp correction.

Interest Rate Sensitivity
The article stresses that rising interest rates, as the Fed’s policy has shown, could erode the “discount rate” that justifies the inflated valuations. In a 3 % rate‑hike scenario, the present value of a 20‑year growth trajectory for a high‑growth AI company drops by 12 %. For speculative firms without revenue, this could mean a rapid re‑pricing.

Geopolitical Risk
The US‑China tech war is another factor that could alter the trajectory. While China has become a major AI R&D player (with the “China 2030” plan), the supply chain for high‑end GPUs remains heavily concentrated in Taiwan and the US. The article references a recent WTO dispute over technology transfer that could delay the entry of new AI chips, tightening supply.

5. Bottom‑Line Takeaways for Investors

1. Diligence is Key – Beyond the headline, investors should scrutinize a company’s revenue trajectory, margin profile, and the “real‑world” use cases it claims to solve.
2. Diversify Across the AI Ecosystem – Rather than a bet on a single AI startup, a balanced allocation that includes hardware, software, services, and even AI‑heavy public‑market ETFs may reduce tail risk.
3. Watch for Regulatory Signals – The EU AI Act and potential U.S. AI regulations could reshape profitability for high‑risk AI applications, so monitor policy developments closely.
4. Beware of Over‑Concentration – A few large names dominate the narrative, but the ecosystem is still maturing. Over‑exposure to “AI hype” may lead to volatility spikes.
5. Patience and Long‑Term Horizon – AI adoption is an evolutionary process; early adopters who can translate technology into sustainable business models will be rewarded over the long term.

6. Conclusion: Bubble, Boom, or Both?

Goldman’s Seeking Alpha article paints a nuanced picture. The AI investment surge is undeniably significant, buoyed by massive VC inflows, public‑market activity, and corporate R&D spend. Yet the same data reveals over‑valuation, speculative concentration, and regulatory uncertainty—classic hallmarks of a bubble. The authors conclude that the AI sector is in a transitional phase: a “growth bubble” that has the potential to burst, but also a genuine technology boom that could underpin the next wave of productivity.

For the research journalist, this underscores a timeless lesson: every technological wave starts with hype, but its longevity depends on the fundamentals that underlie it. Whether the AI surge turns into a sustained boom or a correction, the next few years will be critical for investors, policymakers, and entrepreneurs alike. The story is far from finished, but the data, as Goldman shows, offers a compass for navigating the turbulence ahead.