By Futurist Thomas Frey
You buy a product online for $100. The manufacturer sells it to the distributor for $35. The distributor sells it to the wholesaler for $50. The wholesaler sells it to the retailer for $70. The retailer sells it to you for $100. At each step, someone takes a cut for moving the product from one place to another—often without adding any meaningful value.
This is the supply chain, and AI analysis is revealing that it has evolved into something far more parasitic than most people realize. What should be a relatively efficient system for moving goods from manufacturers to consumers has become a multi-layered extraction economy where middlemen have inserted themselves at every possible point, each taking their percentage while making the entire system slower, more expensive, and less transparent.
The awakening in supply chain and logistics isn’t about the necessity of distribution—goods do need to move from factories to customers. It’s about revealing how many unnecessary intermediaries have positioned themselves in that flow, how much they’re extracting, and how technology could eliminate most of them while delivering better service at a fraction of the cost.
The Distributor Markup Multiplication
In theory, distributors add value by buying in bulk from manufacturers and selling in smaller quantities to retailers, smoothing supply and demand. In practice, AI analysis reveals that many distributors add little value beyond taking their markup and passing products along.
Here’s what AI discovered by analyzing millions of transactions: a product that costs $20 to manufacture often goes through 3-5 intermediary hands before reaching consumers, with each intermediary adding 15-35% markup. By the time it reaches the consumer, the price might be $75-100, with more than half of that representing distribution markup rather than manufacturing cost or retail overhead.
Even more troubling: AI has identified that many of these intermediaries are owned by the same parent companies or have exclusive arrangements that eliminate competition. The appearance of a competitive market masks what’s actually a controlled pipeline where each intermediary has monopoly power at their layer.
One industry analyzed by AI—industrial supplies—showed that products passing through established distribution channels cost 200-400% more than identical products purchased directly from manufacturers, even when shipping costs are included. The distribution “value add” often consists of nothing more than having an existing relationship with buyers and making direct purchase difficult.
The medical supply chain provides an even more egregious example. AI analysis found that basic medical supplies—gloves, masks, syringes—sell to hospitals at 300-800% markups over manufacturing costs, passing through 2-4 distributors who each take 20-40% margins. During the COVID-19 pandemic, these markups became even more extreme, with AI documenting cases of 1,000-2,000% markups on critical supplies.
The “Just-in-Time” Vulnerability Trade-Off
Just-in-time inventory management is celebrated as an efficiency breakthrough—companies don’t tie up capital in excess inventory, they order exactly what they need exactly when they need it. AI analysis reveals this efficiency comes at a hidden cost: increased vulnerability and higher total system costs.
By analyzing supply chain disruptions and their economic impacts, AI has calculated that just-in-time systems save approximately 5-10% in inventory holding costs while increasing supply chain fragility costs by 15-30%. The savings are visible and measurable—lower warehouse costs, less obsolete inventory. The costs are hidden and diffuse—production shutdowns when supplies don’t arrive, rush shipping fees, lost sales from stockouts.
Even more problematic: just-in-time systems increased supply chain complexity, requiring sophisticated logistics coordination that generates enormous costs. AI analysis shows that companies using just-in-time systems spend 40-60% more on logistics management, expediting, and supply chain software than companies using more traditional inventory approaches.
The pandemic exposed this brutally: industries using just-in-time principles faced months-long disruptions because they had no inventory buffers. Companies scrambled to secure supply, paying premiums of 200-500% for expedited delivery. AI analysis estimates that supply chain disruptions from 2020-2022 cost the U.S. economy approximately $1.5-2.0 trillion—far exceeding the inventory carrying costs that just-in-time was supposed to eliminate.
The Third-Party Logistics (3PL) Markup
Companies increasingly outsource their logistics to third-party logistics providers (3PLs) who handle warehousing, shipping, and distribution. AI analysis reveals that 3PLs often cost substantially more than in-house logistics while providing minimal additional capability.
Here’s the pattern AI identified: A company pays a 3PL to manage their warehousing and shipping. The 3PL doesn’t own warehouses or trucks—they subcontract to warehouse operators and freight carriers, adding a 25-40% markup. The warehouse operators and freight carriers are the same ones the company could contract with directly, but the 3PL has positioned itself as the “simplifying” intermediary.
By analyzing 3PL contracts and comparing to direct logistics costs, AI has calculated that companies using 3PLs pay approximately 30-50% more for equivalent services compared to managing logistics in-house or contracting directly with providers. The 3PL markup is justified as providing “expertise” and “coordination,” but AI analysis shows that modern logistics software can provide the same coordination capabilities at a fraction of the cost.
Even more egregiously: some 3PLs have exclusive arrangements with certain warehouse and freight providers, meaning they’re steering clients to higher-cost providers in exchange for kickbacks. AI identified cases where 3PLs chose warehouse and shipping options that cost 20-30% more than alternatives while generating undisclosed referral fees.
One comprehensive analysis estimated that 3PL markups extract approximately $60-90 billion annually from the U.S. economy—money that doesn’t improve service, doesn’t increase efficiency, but simply flows to intermediaries who’ve positioned themselves between companies and the logistics providers who actually move goods.
The Freight Broker Opacity
When companies need to ship freight, many use freight brokers who supposedly find the best carriers at the best prices. AI analysis reveals that freight brokers often operate in ways that maximize their profits at customer expense.
Here’s how it works: A company needs to ship a load. They contact a freight broker who quotes $3,000. The broker then finds a trucking company willing to haul it for $1,800. The broker pockets $1,200—a 67% markup—for making a few phone calls and sending some emails.
AI analysis of freight transactions shows that broker markups typically range from 30-100% of what they pay carriers, with an average around 40-50%. In theory, competitive markets should reduce these margins, but AI has revealed why they don’t: information asymmetry. Shippers don’t know what carriers charge, carriers don’t know what shippers pay, and brokers work hard to keep both sides ignorant.
Even more problematic: AI has documented that brokers often receive volume discounts from carriers that they don’t pass on to shippers. A carrier might give a broker 20% off standard rates for guaranteed volume, but the broker bills shippers at the standard rates and pockets the difference.
Digital freight matching platforms were supposed to solve this by connecting shippers and carriers directly, cutting out broker markups. But AI analysis shows that many of these platforms simply became digital brokers, taking similar margins while providing marginally better service. The technology could eliminate middlemen; instead, it created more efficient middlemen.
One analysis estimated that freight broker markups extract approximately $30-45 billion annually from shippers—costs that ultimately get passed to consumers through higher product prices.
The Import/Export Customs Broker Necessity
International shipping requires navigating complex customs regulations, duties, and documentation. Customs brokers supposedly simplify this process. AI analysis reveals that customs brokerage has become a cartel-like industry charging fees far exceeding the actual work involved.
By analyzing customs broker fees and comparing to the actual time and expertise required, AI has calculated that most customs clearances involve 15-45 minutes of work plus standard forms and digital submissions. Yet brokers charge $50-300+ per shipment for this service—representing hourly rates of $300-1,200.
Why so high? Because the customs process is deliberately complex and opaque, and brokers have successfully positioned themselves as essential gatekeepers. Companies theoretically can do their own customs clearance, but the barrier to entry—understanding regulations, obtaining licenses, and managing paperwork—makes it impractical for most businesses.
AI has also revealed systematic overcharges for “disbursements”—duties and fees that brokers pay on behalf of clients. Brokers often charge 3-8% “handling fees” on duties they simply pass through, plus “service fees” for the electronic submission of payment. A shipment with $5,000 in duties might generate $200-400 in broker fees for processing a payment.
One analysis estimated that customs broker fees total approximately $10-15 billion annually in the U.S. alone—far exceeding what would be necessary if the process were simplified or if digital systems eliminated the need for intermediaries.
The Last-Mile Delivery Inefficiency
“Last-mile delivery”—getting packages from distribution centers to customers’ doors—is the most expensive part of the delivery process. AI analysis reveals it’s also the most inefficient, with massive waste that could be eliminated through better routing and consolidation.
Here’s what AI discovered: Delivery vehicles often travel 40-60% further than necessary because routing is suboptimal. Multiple delivery companies serve the same neighborhoods on the same days, each making partial loads. Delivery windows are wide (often 4-8 hours) because consolidation would require tighter coordination.
By analyzing delivery routes, times, and densities, AI has calculated that optimal routing could reduce last-mile delivery costs by 30-40%. Consolidation—allowing multiple retailers to share delivery infrastructure—could reduce costs by another 20-30%. Combined, last-mile delivery could be 50-60% cheaper while being faster and more reliable.
Why doesn’t this happen? Because delivery is competitive and proprietary. Each retailer or delivery company optimizes only their own operations, leading to system-wide inefficiency. AI analysis shows neighborhoods receiving 15-25 delivery vehicles per day when 4-6 optimally routed and consolidated vehicles could handle the same volume.
The environmental cost is also substantial. AI estimates that delivery inefficiency generates approximately 3-5 million tons of excess CO2 emissions annually in the U.S. alone—emissions that serve no purpose except maintaining competitive separation between delivery companies.
The Warehouse Real Estate Speculation
E-commerce growth has driven enormous demand for warehouse space. AI analysis reveals that warehouse real estate has become a speculation game where financial engineering extracts value from the logistics system without improving it.
Here’s the pattern: Real estate investors buy warehouse properties, then lease them to logistics companies at rates that reflect speculative appreciation rather than operational value. Logistics companies pass these costs to their customers, who pass them to consumers.
AI analysis of warehouse rental rates versus construction costs shows dramatic disparities. In hot logistics markets, warehouse space rents for 200-300% more than would be justified by construction costs and reasonable returns. The excess represents speculation premium—investors betting that e-commerce growth will drive continued rent increases.
Even more problematic: some warehouse developments are built in suboptimal locations from a logistics perspective but optimal locations from a real estate appreciation perspective. The warehouse is less efficient for moving goods, but more valuable as a real estate asset. The logistics efficiency loss gets socialized across the supply chain while the real estate gain accrues to property owners.
One analysis estimated that speculative warehouse real estate premium adds approximately $25-40 billion annually to U.S. logistics costs—costs that don’t improve service but simply transfer wealth to property investors.
The Container Shipping Oligopoly
International shipping is dominated by a small number of container shipping companies who have systematically consolidated into an oligopoly. AI analysis reveals they coordinate pricing in ways that keep rates artificially high while avoiding explicit cartel behavior that would trigger antitrust action.
By analyzing shipping rates, capacity, and route patterns, AI has identified suspicious patterns. When demand increases, all major carriers raise rates simultaneously by similar amounts. When demand decreases, rates drop slowly and incompletely. Capacity additions are coordinated to prevent supply from exceeding demand significantly.
During 2020-2022, container shipping rates increased 500-800% on major routes while carrier profits reached record levels. Carriers claimed this reflected capacity constraints and demand spikes. But AI analysis of actual capacity utilization shows that constraints were artificial—carriers retired ships and reduced schedules to maintain high rates even as manufacturing and ports could have handled more volume.
The concentrated market structure makes this possible. The top 10 shipping lines control approximately 85% of global container capacity. They cooperate through “alliances” that share vessels and coordinate schedules—arrangements that facilitate implicit price coordination while maintaining technical competition.
AI estimates that oligopoly pricing adds 30-50% to international shipping costs compared to what competitive markets would generate. That’s approximately $90-150 billion annually in excess global shipping costs that ultimately flow to consumers through higher product prices.
The Return Logistics Waste
E-commerce returns have exploded, creating a reverse logistics system that AI analysis reveals is wastefully inefficient. Approximately 20-30% of online purchases get returned, and handling these returns costs far more than most consumers realize.
Here’s what AI discovered: Many returns get destroyed rather than resold. AI analysis suggests that 20-30% of returns—billions of dollars in merchandise—are landfilled or liquidated at pennies on the dollar because processing and restocking them costs more than their remaining value. This is economically “rational” for individual companies but socially wasteful.
The return shipping cost alone is substantial. AI analysis shows that return shipping costs retailers approximately $30-50 billion annually—costs they partially recoup through restocking fees and by not fully refunding original shipping charges, but mostly absorb and spread across all customers through higher prices.
Even more problematic: the environmental impact is enormous. Products shipped twice (original delivery plus return), often destroyed rather than reused, represent massive resource waste. AI estimates that e-commerce returns generate approximately 5-7 million tons of CO2 emissions annually in the U.S. alone, plus solid waste from destroyed returned products.
Alternative models exist—local return consolidation points, secondary markets for returns, better fit predictions to reduce return rates—but individual companies lack incentive to implement them because the costs are distributed while the benefits would be systemic.
The Cold Chain Markup
Temperature-sensitive products—food, pharmaceuticals, biologics—require “cold chain” logistics maintaining specific temperatures throughout shipping and storage. AI analysis reveals that cold chain services charge premiums far exceeding the actual incremental cost of temperature control.
By analyzing cold chain pricing versus standard shipping costs, AI has calculated that cold chain services typically charge 200-400% premiums over non-temperature-controlled alternatives. The actual incremental cost of refrigeration and monitoring: approximately 30-60% more than standard logistics.
The gap represents extraction enabled by specialized requirements and limited competition. Cold chain certification and equipment requirements create barriers to entry, allowing established providers to maintain high prices. Meanwhile, pharmaceutical companies and food distributors have little negotiating leverage because cold chain compliance is regulatory-mandatory.
AI has also revealed systematic overbilling in cold chain services. Temperature monitoring that costs pennies per shipment gets billed at $15-30. “Validation” and “documentation” that consists of automated data logging gets billed at $50-200 per shipment. The services are presented as highly specialized, but AI analysis shows they’re largely automated with minimal labor.
One estimate: cold chain premium pricing extracts approximately $8-12 billion annually beyond reasonable costs in the U.S. market alone—costs that get passed to consumers through higher prices for food and medicine.
The Port and Terminal Inefficiency
U.S. ports are significantly less efficient than top international ports, and AI analysis reveals that much of this inefficiency is structural rather than technical—deliberate choices that benefit specific interests at the expense of overall system efficiency.
By analyzing port throughput, labor practices, and technology adoption, AI has identified that U.S. ports operate at 40-60% of the efficiency of leading Asian and European ports. Container dwells (time containers sit at ports waiting to be picked up) are 2-4 times longer. Gate operations are slower. Equipment utilization is lower.
Why? Labor agreements prohibit automation that would dramatically increase efficiency. Port authorities prioritize revenue per container over throughput. Terminal operators lack incentive to invest in efficiency because they operate under long-term leases with little competition.
The most damning finding: AI analysis shows that if U.S. ports operated at the efficiency level of the top 10 global ports, total logistics costs would decrease by approximately $40-60 billion annually. That efficiency exists—it’s been proven elsewhere—but U.S. ports deliberately choose not to implement it.
Even more specifically, AI has revealed that port labor contracts include provisions requiring unnecessary labor for tasks that are fully automated elsewhere. Ships take 40-60% longer to unload at U.S. ports than at comparable Asian ports, not because of equipment limitations but because of contractual requirements for specific manning levels regardless of automation.
The Cross-Docking Illusion
Cross-docking—receiving goods and immediately shipping them out without warehousing—is presented as an efficiency breakthrough. AI analysis reveals it often just adds another handling step and another intermediary taking their cut.
Here’s the reality: Products arrive at a cross-dock facility, get unloaded, sorted, and reloaded onto different trucks. This process costs money—facility costs, labor costs, handling equipment. It only makes sense if it creates sufficient value through consolidation or route optimization.
But AI analysis shows that many cross-dock operations create minimal value. Products get touched multiple times—unloaded from one truck, moved to a sorting area, loaded onto another truck—adding time and cost without meaningful consolidation. In some cases analyzed by AI, products moving through cross-dock facilities cost 15-25% more to ship than direct delivery would cost, with no improvement in speed or reliability.
Why does this persist? Because cross-dock operators and logistics companies make money on each transaction. Every time goods get touched, someone charges a fee. Direct delivery eliminates those fees, so the intermediaries encourage “solutions” that maximize touches rather than minimizing cost.
The Track and Trace Theater
Modern supply chains offer “visibility” and “track and trace” capabilities supposedly allowing companies to monitor shipments in real-time. AI analysis reveals that much of this visibility is theater—providing data without meaningful utility while generating subscription and service fees.
By analyzing track and trace platforms and their actual use, AI has discovered that most shipment location updates provide no actionable information. Knowing your container is “in transit” somewhere in the Pacific Ocean doesn’t help—you can’t make decisions based on that information because there are no alternatives.
Real visibility would include predictive analytics: “your shipment will be delayed 3 days, recommend switching to expedited air for critical components.” Most track and trace systems don’t provide this. They provide location data without analysis or alternatives.
Yet companies pay billions for these systems. AI estimates that supply chain visibility platforms and services generate approximately $15-25 billion in annual revenue globally—much of which delivers minimal value beyond reporting information that was already available through standard logistics communications.
The platforms aren’t useless, but they’re overpriced relative to utility. AI analysis suggests similar functionality could be delivered for 40-60% less if the market were more competitive and if visibility platforms didn’t extract monopoly rents from their position in logistics information flows.
The Regulatory Complexity Exploitation
Supply chains face numerous regulations—safety standards, environmental rules, trade policies, tax requirements. AI analysis reveals that complexity has been deliberately maintained and exploited by intermediaries who profit from helping companies navigate it.
Here’s the pattern: Regulations that could be simplified or standardized remain complex because simplification would eliminate intermediary revenue. Customs brokers lobby against customs simplification. Compliance consultants oppose streamlined environmental reporting. Tax advisors fight against supply chain tax transparency.
AI has analyzed regulatory structures and identified numerous cases where complexity serves no public policy purpose but generates substantial intermediary revenue. Documentation requirements that could be automated remain manual. Standards that could be harmonized remain fragmented. Compliance that could be simplified through technology remains labor-intensive.
One estimate: if supply chain regulations were simplified to the level of best international practices, compliance costs would decrease by approximately $30-50 billion annually in the U.S. alone. That doesn’t mean eliminating necessary regulation—it means eliminating unnecessary complexity that serves only to maintain intermediary businesses.
The “Free Shipping” Hidden Cost
E-commerce often advertises “free shipping,” but AI analysis reveals this is accounting fiction. Shipping costs don’t disappear—they get embedded in product prices and distributed across all customers.
By analyzing pricing patterns between retailers offering free shipping versus those charging separately for shipping, AI has calculated that “free shipping” typically increases base product prices by 8-15% compared to equivalent products where shipping is charged separately. All customers pay for shipping through higher prices, whether they need shipping or not.
This particularly disadvantages local customers and those who would choose slower, cheaper shipping. They pay for expedited shipping embedded in prices even if they’d prefer to pick up locally or wait longer for cheaper delivery. The inefficiency is redistributive rather than total-cost, but it’s substantial—AI estimates approximately $20-30 billion in misallocated shipping costs annually as customers pay for shipping services they didn’t choose and don’t receive.
What Happens Next
The supply chain has become a complex web of intermediaries, each extracting value while adding minimal service. AI is revealing the magnitude of this extraction and pointing toward radical simplification.
Direct-to-consumer models are already cutting out traditional retail and wholesale intermediaries. Vertical integration is reducing reliance on 3PLs and freight brokers. Digital platforms are connecting shippers directly with carriers. Automation is eliminating the need for many manual logistics processes.
The resistance will be intense. Every intermediary has revenue to protect and relationships to leverage. Labor unions will fight automation. Brokers will emphasize the “expertise” that technology could easily replicate. Terminal operators will resist efficiency improvements that reduce per-unit revenue.
But the economic pressure is overwhelming. Companies that cut out intermediaries can offer better prices or higher margins. Consumers increasingly expect Amazon-level efficiency. Technology makes traditional intermediaries increasingly unnecessary.
Final Thoughts
The awakening in supply chain and logistics isn’t about eliminating distribution—goods must move from manufacturers to consumers. It’s about revealing how many unnecessary hands touch goods during that movement, how much each hand extracts, and how technology could eliminate most of them.
A product that costs $20 to manufacture shouldn’t cost $100 when it reaches consumers, with most of that difference going to intermediaries who add minimal value. A shipment that could move from factory to customer in 5 days shouldn’t take 15 days passing through multiple warehouses and distribution centers. A process that could be automated shouldn’t require manual intervention at every step.
The supply chain that emerges from The Awakening will be faster, cheaper, and more efficient—but it will employ far fewer intermediaries. The companies that adapt toward genuine value creation will survive. Those defending extraction models will face the same fate as travel agents after online booking and taxi dispatch after Uber.
The age of supply chain opacity and middleman extraction is ending. What replaces it will depend on whether incumbents adapt or whether disruption sweeps them aside. Either way, the hidden middleman economy is becoming visible—and indefensible.
In our next column: Energy and Utilities—The Regulated Monopoly Inefficiency.
Related Articles:
Harvard Business Review – The Hidden Costs of Supply Chain Complexity
McKinsey & Company – What Freight Rate Transparency Could Mean for Shippers and Carriers
The Wall Street Journal – How Amazon’s Logistics Machine Gives It an Edge
