Two reports illustrate how business leaders are thinking about and budgeting for generative AI.
The CEO’s Guide to Generative AI: Cost of compute
The IBM Institute for Business Value uses data-driven research and expert analysis to deliver thought-provoking insights to leaders on the emerging trends that will determine future success.'
Built on Shared Knowledge: What the World Wants from AI Wealth
AI labs and policymakers are focusing on AI dividends to address economic insecurity. We asked 1,041 people across 64 countries what they actually want from AI wealth.
Why We Fear AI: On the Interpretation of Nightmares — Common Notions Press
Industry insiders Hagen Blix and Ingeborg Glimmer dive into the dark, twisted world of AI to demystify the many nightmares we have about it. They combine expertise in cognitive science and machine learning with political and economic analyses to cut through the hype and technobabble to show how fear
Visualising AI spending: How does it compare with history’s mega projects?
AI spending is projected to reach $2.5 trillion in 2026, surpassing the largest scientific and infrastructure projects.
AI overuse could spark "brain fry," new research finds
The mental strain associated with AI carries "significant costs," researchers find.
Microsoft reports are exposing AI's real cost problem: Using the tech is more expensive than paying human employees | Fortune
Companies are racing to incentivize employees to use AI. But as some companies are finding, the more employees that use the technology, the heavier the bill.
AI-Generated “Workslop” Is Destroying Productivity
Despite a surge in generative AI use across workplaces, most companies are seeing little measurable ROI. One possible reason is because AI tools are being used to produce “workslop”—content that appears polished but lacks real substance, offloading cognitive labor onto coworkers. Research from BetterUp Labs and Stanford found that 41% of workers have encountered such AI-generated output, costing nearly two hours of rework per instance and creating downstream productivity, trust, and collaboration issues. Leaders need to consider how they may be encouraging indiscriminate organizational mandates and offering too little guidance on quality standards. To counteract workslop, leaders should model purposeful AI use, establish clear norms, and encourage a “pilot mindset” that combines high agency with optimism—promoting AI as a collaborative tool, not a shortcut.
Why are workers so worried about AI? Listen to how business leaders talk about it
Concerns about AI pushing humans out of jobs may be a bit overstated, but the anxiety being felt by workers related to technology is real and nuanced.
Irresponsible AI: big tech's influence on AI research and associated impacts
The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tech in the AI field. This trend has been accompanied by growing ethical concerns and intensified societal and environmental impacts. This position paper argues that irresponsible AI development is strongly driven by big tech's influence and involvement in the field. First, we examine the growing and disproportionate influence of big tech in AI research and argue that its drive for scaling and general-purpose systems is fundamentally at odds with the responsible, ethical, and sustainable development of AI. Second, we review key current environmental and societal negative impacts of AI and trace their connections to big tech's influence. Third, we discuss the underlying economic forces driving big tech's actions. Finally, as a call to action, we invite AI researchers to counter big tech's influence in irresponsible AI development through strategies that build on the responsibility of implicated actors and collective action.
Irresponsible AI: big tech's influence on AI research and associated impacts
The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tech in the AI field. This trend has been accompanied by growing ethical concerns and intensified societal and environmental impacts. This position paper argues that irresponsible AI development is strongly driven by big tech's influence and involvement in the field. First, we examine the growing and disproportionate influence of big tech in AI research and argue that its drive for scaling and general-purpose systems is fundamentally at odds with the responsible, ethical, and sustainable development of AI. Second, we review key current environmental and societal negative impacts of AI and trace their connections to big tech's influence. Third, we discuss the underlying economic forces driving big tech's actions. Finally, as a call to action, we invite AI researchers to counter big tech's influence in irresponsible AI development through strategies that build on the responsibility of implicated actors and collective action.
AI is Creating Peak Software, Media is the Best Analogy
Let's learn more about the world's most important manufactured product. Meaningful insight, timely analysis, and an occasional investment idea.
Statement from Dario Amodei on our discussions with the Department of War
A statement from our CEO on national security uses of AI
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Irresponsible AI: big tech’s influence on AI research and associated impacts
The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tech in the AI field. This trend has been accompanied by growing ethical concerns and intensified societal and environmental impacts. This position paper argues that irresponsible AI development is strongly driven by big tech’s influence and involvement in the field. First, we examine the growing and disproportionate influence of big tech in AI research and argue that its drive for scaling and general-purpose systems is fundamentally at odds with the responsible, ethical, and sustainable development of AI. Second, we review key current environmental and societal negative impacts of AI and trace their connections to big tech’s influence. Third, we discuss the underlying economic forces driving big tech’s actions. Finally, as a call to action, we invite AI researchers to counter big tech’s influence in irresponsible AI development through strategies that build on the responsibility of implicated actors and collective action.
Loss of Oversight: How AI systems may become harder to audit, monitor, and investigate
The safety of advanced AI systems increasingly depends on the ability to oversee them: to audit models for concerning behaviours before deployment, monitor their activity during operation, and investigate incidents after they occur. This report maps the landscape of AI oversight and assesses how it is likely to change. Drawing on 25 expert interviews across frontier AI developers, government, NGOs, and academia, together with a literature review and internal analysis, we examine five sources of oversight signal: model behaviour, chain-of-thought reasoning, internals activations and circuits, memory architectures, and honesty training. For each source, we identify the properties that current oversight relies on, the pathways by which these properties could degrade, and the technical levers available to preserve them. Our central finding is that literature and expert opinion support the conclusion that current oversight rests on foundations that are likely to erode, absent effective intervention. We recommend that developers track and report shifts in oversight-relevant properties, preserve oversight affordances by design, and invest in emerging oversight techniques as fallbacks against continued degradation of current methods.
Embracing Gen AI at Work
Today artificial intelligence can be harnessed by nearly anyone, using commands in everyday language instead of code. Soon it will transform more than 40% of all work activity, according to the authors’ research. In this new era of collaboration between humans and machines, the ability to leverage AI effectively will be critical to your professional success. This article describes the three kinds of “fusion skills” you need to get the best results from gen AI. Intelligent interrogation involves instructing large language models to perform in ways that generate better outcomes—by, say, breaking processes down into steps or visualizing multiple potential paths to a solution. Judgment integration is about incorporating expert and ethical human discernment to make AI’s output more trustworthy, reliable, and accurate. It entails augmenting a model’s training sources with authoritative knowledge bases when necessary, keeping biases out of prompts, ensuring the privacy of any data used by the models, and scrutinizing suspect output. With reciprocal apprenticing, you tailor gen AI to your company’s specific business context by including rich organizational data and know-how into the commands you give it. As you become better at doing that, you yourself learn how to train the AI to tackle more-sophisticated challenges. The AI revolution is already here. Learning these three skills will prepare you to thrive in it.
