AWS in Orbit, Cisco G300, and AI Washing

Starcloud plans to send AWS Outposts hardware into orbit this October, aiming to make Amazon Web Services’ on-prem cloud platform operational in space. The company previously launched an Nvidia H100 GPU aboard its Starcloud-1 satellite in 2025, signaling that orbital compute is moving beyond theory.

Others, including Guoxing Aerospace, Starlink, and Google, are exploring similar concepts, raising the question of whether future data centers could extend beyond Earth. Not everyone is convinced. AWS CEO Matt Garman has highlighted practical constraints such as launch capacity, cost, and the challenge of scaling anything close to today’s cloud footprint. Engineers also point to concerns around space debris, cooling limitations, hardware failures that cannot be serviced, and latency.

The concept is ambitious, but whether space becomes a viable layer of cloud infrastructure or remains a high-cost experiment is still uncertain.

Cisco is advancing further into AI infrastructure with its new Silicon One G300 switch chip and a refreshed portfolio of routers and switches designed for large-scale training, inference, and agentic workloads.

The G300 delivers up to 102.4 Tbps of throughput and is built to enhance traffic flow and link efficiency across AI clusters, positioning it against Nvidia’s InfiniBand and Broadcom’s Tomahawk. Manufactured on TSMC’s 3-nanometer process, the chip focuses on performance, programmability, and long-term infrastructure flexibility.

Cisco is pairing it with its 8000 and 8100 Series Secure Routers and C9000 Series Smart Switches, running IOS XE 26 with integrated post-quantum cryptography. The company is also emphasizing liquid-cooled system designs to improve energy efficiency and address the growing power demands of AI data centers.

As AI scales, networking is increasingly becoming a central performance driver.

As with any major technology wave, AI has brought significant hype — and with it, the rise of “AI washing,” where companies overstate how much artificial intelligence powers their products or results. In some cases, AI is cited to justify layoffs, restructurings, or margin pressures, reframing cost-cutting as strategic transformation.

The issue extends beyond marketing language. Inflated AI claims can distort investment decisions, influence workforce planning, and erode trust when promised results fail to materialize. U.S. regulators such as the FTC and SEC have indicated that clearly deceptive AI claims may face scrutiny, though exaggeration is more difficult to regulate than outright fraud.

In a market where AI positioning can drive valuations and signal innovation, the incentive to lean into the narrative remains strong. Ultimately, however, investors and enterprise buyers focus on measurable outcomes and tangible performance improvements.