Analysis of Goldman Sachs research comparing costs of AI agents vs humans across coding, support, and data entry, with projections of token consumption growth and falling inference costs. Discusses productivity gains, job displacement, and opportunities in healthcare.
**Totally agree—this Goldman Sachs analysis nails a key dynamic in the AI rollout.** The token economics for agents are shifting fast, and it creates very different implications across job types.04 **The Goldman Sachs Take on Costs** Recent GS research compares daily costs for AI agents vs. humans: **Coding agent**: \~$13–$14/day vs. \~$300 for a human equivalent → massive arbitrage, explaining the coding boom. **Call center/support agent**: \~$93/day vs. \~$90 for human → much closer, especially with voice/context overhead. **Data entry**: Even more favorable for AI as token intensity drops.4 They project token consumption exploding (up to 24x by \~2030) while inference costs fall 60-70% per year, setting up gross margin gains for providers and hyperscalers.0 This matches broader trends: Ramp’s data showed average enterprise token costs dropping \~75% in a year (from \~$10/M to $2.50/M by early 2025), and usage is surging anyway.11 **Productivity Uplift vs. Job Displacement** Your point on **software engineers and radiologists** is spot-on. History (and early AI data) shows these tools amplify capable humans rather than fully replace them: Coding: GS itself is deploying thousands of autonomous agents (e.g., with Devin/Cognition or Claude) alongside its \~12k developers, targeting **3-4x productivity gains**. It’s handling legacy modernization, technical debt, etc.—freeing humans for architecture, judgment, and complex systems. Junior/entry-level roles face pressure, but overall output (and software market size) expands.272 Radiology: AI excels at pattern-matching scans but needs human oversight for nuance, liability, and integration with patient context. It reduces drudgery (e.g., routine reads) and error rates, letting radiologists handle more complex cases or volume. **Data entry / routine tasks** will see the biggest headcount shifts because they’re highly automatable and low-judgment. But as costs plummet, we’ll likely see **more of these things done**—deeper analytics, personalization, compliance checks, etc.—rather than pure elimination. Jevons paradox in action: cheaper “compute labor” increases demand for it. Customer support sits in the middle: agents handle Tier 1 well and scale infinitely, but complex/escalated cases stay human (or hybrid). Net effect so far: More augmentation than mass unemployment. Tech unemployment has ticked up in spots (especially juniors), but broader knowledge work productivity is rising, and new roles emerge around AI orchestration.20 **Healthcare & Life Sciences: The Big Opportunity** This is where the upside feels most exciting and needed. US healthcare is famously inefficient (high admin burden, clinician burnout, slow R&D). AI agents could deliver exactly the productivity boost you mention: **Life sciences**: McKinsey/Deloitte estimates gen AI could unlock $60–110B+ annually via faster drug discovery, clinical trials, manufacturing optimization, and marketing. AI agents already automate repetitive workflows, literature synthesis, molecule design, etc.3234 **Healthcare delivery**: Ambient documentation (cutting note-taking time dramatically), prior auth/coding automation, diagnostics support, patient experience tools. Early ROI reports show strong returns on productivity, security, and care quality.38 From clinicians’ POV: Less burnout, more time with patients. From patients’: Faster access, better personalization, lower costs long-term. Regulatory and data privacy hurdles are real, but the need is acute—US healthcare could use a 20-30%+ efficiency lift. **Bottom line**: Falling token costs + capable agents = asymmetric productivity explosions in token-light/high-value domains first (coding), then broader diffusion. We’ll get more output, new capabilities, and some task shifts—not a zero-sum job apocalypse. Healthcare/life sciences stand to gain enormously if adoption accelerates responsibly. The “do way more of these things” scenario feels like the optimistic (and likely) path. Curious what specific GS chart or angle stood out most to you?
The article discusses how AI agent workflows are shifting optimization focus from pure inference costs to broader challenges like latency, orchestration overhead, and reliability. It highlights a trend toward hybrid architectures and dynamic model routing to address these multi-step workflow complexities.
The article argues AI agents represent a major productivity shift by moving from answering questions to completing tasks, and discusses current use cases and bottlenecks.
Goldman Sachs predicts AI agent token use will multiply 24 times by 2030, citing cost concerns as Uber and Microsoft rethink expensive agent usage, highlighting a key challenge for the AI boom.
The article analyzes the economics of AI, focusing on the war for GPU resources, contrasts human inference spikes with agentic continuous workloads, and argues that current infrastructure is optimized for human usage, not agentic inference, which is more demanding.
An analysis of AI coding agent costs reveals that agentic workflows can use up to 3,500x more tokens than a simple ChatGPT call, with most waste coming from redundant context loading. The article suggests tracking repeated file actions and using efficient models to cut costs.