TL;DR

Algorhythm Holdings — a former in-car karaoke company with a $6m market cap — sparked a massive sell-off across global trucking and logistics stocks after announcing its SemiCab AI platform could scale freight volumes by 300-400% without increasing headcount. The Russell 3000 Trucking Index fell 6.6%.

David Versus Goliath

Algorhythm’s announcement about its SemiCab platform sent its own shares up nearly 30%, but the ripple effects across the sector were severe. CH Robinson Worldwide plunged 15% (having been down as much as 24% intraday), Landstar System dropped 16%, RXO fell 20.5%, and JB Hunt and XPO each lost around 5%.

The sector decline was the worst since Donald Trump’s tariff trade war in April 2025.

“The level of paranoia is category 5,” said Joseph Shaposhnik, a portfolio manager at Rainwater Equity. “It’s not something that we’ve seen in quite a long period of time.”

Algorhythm’s CEO Gary Atkinson was himself surprised: “Never in my wildest dreams would I ever have imagined a day like today. It’s almost like David versus Goliath.”

The AI Fear Trade Goes Global

European logistics stocks followed Wall Street down. DHL Group fell 4.9%, DSV dropped 11%, and Kuehne+Nagel plunged 13%. Drug distribution companies McKesson and Cardinal Health also fell around 4%.

Trucking became the latest sector hit by the widening AI disruption sell-off that started with legal software and publishing companies after Anthropic released new capabilities for Claude, then spread to insurance, price comparison sites, wealth management, and commercial property.

“We can see a broad AI fear trade taking place and it’s touching all corners except those that are immune to disruptions — materials, energy, staples,” said Neil Wilson, investor strategist at Saxo UK.

Looking Forward

Algorhythm’s background — it sold its Singing Machine karaoke business for $4.5m in 2025 before pivoting to AI freight — raises questions about whether markets are reacting to genuine capability or to hype. For UK logistics firms, the practical question is how quickly AI tools can actually match these claimed efficiency gains at scale.