Logistics Arbitrage Hunters: AI and the Mirage of Zero-Cost Shipping

The hyperbole of "zero-cost shipping" populates tech conferences, but the reality of logistics is made up of physical limits and unavoidable costs. AI does not

In the technological fervor surrounding the global supply chain, an increasingly suggestive narrative is gaining ground: Artificial Intelligence, by optimizing routes and finding empty spaces, will allow us to achieve "near-zero cost" shipments. It is an alluring prospect for any logistics director, but profoundly misleading.

AI cannot abolish the laws of physics and economics. It can, however, transform shippers into shrewd "logistics arbitrage hunters," capable of finding and exploiting temporary differences between prices, capacity, and demand on specific routes.

In this in-depth piece for the Scenarios and Reflections column, we will debunk the hyperbole of free shipping. By analyzing economic literature and market tools, we will explore how software attempts to curb the chronic problem of empty trips, how predictive models choose the ideal carrier and, above all, what competitive risks (and risks of collusion) lurk behind markets where the parties negotiating are no longer humans, but algorithms.

1. The Weight of Emptiness: Geography and Logistics Frictions

The Holy Grail of logistics is not traveling faster, but ceasing to transport air. A meta-analysis published by the World Bank ("Geographic Imbalance, Search Frictions, and Regulation: Causes of Empty Miles in Freight Trucking") examined decades of data across 27 countries, estimating that on average about 29% of the kilometers traveled by trucks occur empty [1908].

This massive inefficiency is the true prairie in which logistics software operates. The cause of empty trips, however, is not only poor human organization, but lies in rigid geographic imbalances (there are regions that produce a lot and consume little, and vice versa) and in complex "search frictions."

If a carrier has just delivered a load in a region with low shipping demand, an AI-based system can intercept this anomaly and match it with a return load (the so-called backhaul). The algorithm offers the carrier a lower rate to spare it a costly empty return. This is true logistics arbitrage. But, as the World Bank study implicitly reminds us, the trip will still have fuel costs, wear and tear, tolls, and labor hours that must be compensated: the trip does not become free, it merely becomes less inefficient.

2. Dynamic Routing: Predicting the Rate, Securing Capacity

The qualitative leap of AI manifests in the shift from simple comparison of quotes (who offers less today?) to dynamic forecasting.

According to the analysis "Why dynamic routing guides are essential for freight shippers and carriers" by Transporeon, modern machine learning models do not merely evaluate current offers, but estimate the probabilities of acceptance by carriers based on historical data and current conditions [1892]. This enables the creation of dynamic routing guides: instead of sending requests in cascade to a fixed list of carriers, the system updates the order of carriers for each individual shipment, proposing the load to those who statistically have the highest probability of accepting it under the best conditions at that specific moment.

But the hunt for arbitrage is not just a race to the bottom. As also specified by Transporeon ("The Power of Predicting Spot Market Freight Rates to Secure Capacity"), blindly chasing the lowest nominal rate on the spot market is a losing strategy if it results in failed pickups and delays [1893]. The algorithm calculates the equilibrium point between immediate savings and the guarantee that the carrier actually shows up at the loading dock.

The use of transparent indices, such as the Freightos Baltic Index (FBX) for ocean container rates, provides a real-time reference on market trends, making it clear why an arbitrage opportunity depends intrinsically on the route and timing, and is never a fixed and universal price list [1906].

3. The Dark Side: Concentration and Algorithmic Collusion

What happens when everyone, simultaneously, starts using the same artificial intelligence tools to hunt for the same opportunities?

A recent simulated study available as a preprint on arXiv ("When Shippers Become Algorithms") observed that when carrier selection is delegated to agents based on Large Language Models (LLMs), preferences tend to concentrate rapidly on very few operators, creating bottlenecks and distorting the market [1891]. The authors demonstrate that only by imposing high transparency on carriers' residual capacity is it possible to curb this dangerous algorithmic concentration.

To this is added an even deeper risk, highlighted by the famous study "Artificial Intelligence, Algorithmic Pricing, and Collusion" published in the American Economic Review [1684]. In repeated markets, pricing algorithms quickly learn that triggering price wars is counterproductive. Instead, they tend to develop forms of tacit collusion, accommodating prices upward without ever explicitly communicating with each other. If entire networks of shippers and carriers were to react algorithmically to the same price variations, we might witness not a collapse in rates, but an artificial alignment that neutralizes the benefits of competition.

Key Operational Takeaways

• Evaluate Total Cost of Ownership (TCO): Do not confuse simple quote comparison with true logistics arbitrage. Software can find a highly advantageous initial rate, but the real calculation must include carrier reliability, transit times, any penalties for delays, and surcharges.
• Manage Carrier Loyalty: Systematically exploiting carriers' vulnerability on return trips (backhaul) by maximizing spot savings can destroy long-term relationships. When the market flips and capacity becomes scarce, carriers will remember who squeezed them on prices.
• Monitor AI Bias: As demonstrated by recent studies, fully delegating routing to algorithms can lead to excessive concentration on very few suppliers. Companies must maintain governance rules to ensure a distribution of load that keeps the entire supplier ecosystem healthy.

Conclusions: The Asphalt Remains

The idea that Artificial Intelligence can make transport "near-zero cost" is an optical illusion created by those who confuse bytes with fuel.

Predictive software and dynamic routing guides are formidable tools for healing inefficiencies, combating that systemic waste of 29% empty kilometers, and surgically matching those who desperately need cargo space with those who urgently need to fill it. But the game of logistics arbitrage always collides, inevitably, with the limits of the physical world: asphalt, tolls, driver fatigue, and diesel.

The algorithm can calculate the perfect route and find the best price, but at the end of the calculation, a forty-ton truck will still have to start its engine. And moving matter, in a universe governed by physics and geopolitics, will never be free.

Bibliographic References and Sources

• Geographic Imbalance, Search Frictions, and Regulation: Causes of Empty Miles in Freight Trucking — World Bank (2024) [1908]
• Why dynamic routing guides are essential for freight shippers and carriers — Transporeon [1892]
• The Power of Predicting Spot Market Freight Rates to Secure Capacity — Transporeon [1893]
• Freightos Baltic Index (FBX) — Baltic Exchange and Freightos [1906]
• When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets — arXiv (2026) [1891]
• Artificial Intelligence, Algorithmic Pricing, and Collusion — American Economic Review [1684]

Article by the Editorial Staff of La Bussola dell'IA