AI Publications

1. AI will boost productivity, but not lead to an immediate boom in growth across the entire economy.

AI is undoubtedly a powerful driver of productivity, especially in industries where automation, data processing, and predictive modeling can yield direct efficiency gains. However, the overall economy is far too complex for these gains to translate into an immediate, economy-wide boom. Growth requires not only technological breakthroughs but also complementary changes in management, infrastructure, and culture.

Many sectors—such as healthcare, education, and government services—remain resistant to rapid transformation due to regulation, legacy systems, and human-intensive processes. While AI can revolutionize tasks like coding, logistics, and content creation, its diffusion across slower-moving institutions will take time. The economic uplift will therefore unfold unevenly, appearing first in scalable industries and only gradually affecting the broader economic landscape.

2. The key bottlenecks to AI-enabled growth are human, institutional, and organisational — not just compute or…

🔗https://shre.ink/Wharton-GenAI-adoption-into-the-Enterprise

1. Context

The report arrives at a pivotal moment: After years of experimentation and hype around generative AI (Gen AI), enterprises are now moving into the deployment and value-capture phase.

The study’s authors note that organizations are no longer just testing tools—they are integrating Gen AI into core workflows, scaling up investment, tracking returns, and facing the real challenge of aligning people, processes and trust. In this sense, the report positions 2025 as the year of “accountable acceleration” rather than mere promise.

2. Study Objectives and Methodology

The core goals of the research were to take the pulse of enterprise Gen AI adoption—building on Wharton’s prior waves in 2023 and 2024—and to focus on usage, perception, investment, governance and human-capital dimensions.

🔗https://shre.ink/Google-Space-AI-Infrastructure

1. Introduction

Google frames AI as a foundational technology that will reshape many domains — science, business, society — and asks: where can we go to unlock its fullest potential?. Their answer: perhaps the best place to scale AI compute is space.

They point out that the Sun emits more power than 100 trillion times humanity’s total electricity production, and that in certain orbits a solar panel can receive up to eight times more annual energy than on Earth. With this in mind, their “moonshot” initiative, called Project Suncatcher, envisions constellations of solar-powered satellites, each carrying high-performance TPUs (Tensor Processing Units), connected by free-space optical links — in effect, a data-centre in orbit.

2. System design and key challenges

2.1 Achieving data centre-scale inter-satellite links

Cryptocurrency and AI: A Converging Future

Cryptocurrency and AI might seem like two worlds apart but increasingly they are converging. Companies that once focused solely on mining Bitcoin are now eyeing High-Performance Computing (HPC) and AI infrastructure as the next frontier for growth.

Take Bitfarms (NASDAQ: BITF) as a prime example. Known for powering Bitcoin transactions in Quebec, Canada, the company has pivoted to leverage its data centers, energy assets, and U.S. power campuses to provide HPC and AI infrastructure services. Essentially, Bitfarms is transforming its crypto mining assets into the computing backbone for AI, a market exploding in demand.

Why Crypto Players Are Jumping In

Crypto miners are natural candidates for AI infrastructure:

They already operate large scale data centers optimized for continuous high intensity workloads.