A new technology trend, ‘vibe coding’, is reviving the logistics technology build vs. buy question.
Modern AI (artificial intelligence) tools, known as vibe coding, have developers and non-developers alike simply describing the software they want in plain language while AI generates the underlying code.
In the last few years, these AI coding assistants have gotten better, making it easier than ever to create software, even for those people who have never written code before.
For freight forwarders exploring new technology, this raises an intriguing possibility. If AI can build software so quickly, should logistics companies start building their own systems? Could it be that effortless? Well, as expected, there’s a lot more to it than what one might initially consider.
For logistics companies that have long sought software tailored precisely to their operations, vibe coding represents a compelling shift, but one that warrants careful consideration. The appeal is clear: AI can generate working prototypes far faster than traditional development cycles, lowering the barrier to experimentation. Freight forwarders often manage highly specific workflows, specialized documentation requirements, and nuanced regional compliance standards that off-the-shelf platforms may not immediately address. In that context, AI-powered tools make it easier to test and iterate on new ideas without committing months of development resources.
Yet while these advantages make the build path more accessible than ever, they also risk oversimplifying what it takes to develop, scale, and sustain a truly production-ready logistics platform.
Beyond simply generating code
There is a fundamental distinction between generating code and taking ownership of a mission-critical logistics system. Building reliable, scalable logistics technology still requires deep expertise and sustained investment.
AI can certainly accelerate development, but it does not eliminate the need for sound architecture, rigorous security practices, comprehensive testing, or long-term lifecycle management. What begins as a fast prototype must ultimately perform in a high-stakes, real-time logistics environment where delays, errors, or downtime have direct operational and financial consequences.
When freight forwarders choose to build their own systems with AI assistance, they assume responsibility for far more than code creation. They are committing to the ongoing performance, resilience, and evolution of that platform. This includes ensuring that:
- Integrations with carriers, customs authorities, ports, and partners remain stable and continuously updated as external systems change.
- Workflows and documentation processes evolve in step with shifting global trade regulations and compliance requirements.
- Security frameworks are robust enough to protect sensitive shipment, customer, and financial data against increasingly sophisticated threats.
- Infrastructure can scale reliably with shipment volumes, peak seasons, and geographic expansion.
- Users across operations, finance, and customer service are consistently trained and supported as the system evolves.
- Issues are diagnosed and resolved quickly when disruptions occur, often under time-sensitive conditions.
Beyond these core responsibilities, forwarders must also consider additional factors that are less visible at the outset but critical over time:
- Total cost of ownership: Initial development may appear efficient, but ongoing maintenance, upgrades, and staffing can significantly exceed expectations.
- The long view: Rapid prototyping does not guarantee long-term performance, stability, or usability at scale.
- Talent dependency: Internal systems rely on a small group of software developers, creating risk if key tech personnel leave a company.
- Time and resources spent building technology can divert focus from core logistics operations and customer service.
- Keeping pace with innovation and evolving technologies, such as AI, automation, and visibility tools require continuous reinvestment.
In this light, while vibe coding lowers the barrier to entry, it does not reduce the complexity of ownership. The decision to build is no longer just about whether software can be created, it’s about whether it can be continuously operated, secured, and improved at the level global logistics demands.
For freight forwarders evaluating AI, the question is not whether AI can build software, but where it delivers the most value within a broader technology strategy. The real opportunity lies in applying AI to enhance, not replace, the core systems that already underpin operations.
AI can play a powerful role in automating repetitive, manual tasks, accelerating development cycles, and enabling faster experimentation with new features. It can streamline data entry, improve document processing, surface insights from shipment data, and support more responsive customer interactions. In these areas, AI frees up teams to focus on higher-value activities.
At the same time, logistics is inherently exception-driven and high-stakes, which makes human oversight essential. A human-in-the-loop approach ensures that AI-generated outputs, whether operational decisions, documentation, or customer communications, are validated, contextualized, and aligned with real-world conditions. Forwarders must determine where automation can be trusted to run independently, and where human expertise remains critical to manage risk, ensure compliance, and maintain service quality.
In practice, this means integrating AI into established platforms where governance, data integrity, and operational workflows are already proven, rather than relying on AI to replace them. The most effective strategies will combine the speed and scalability of AI with the judgment, accountability, and domain expertise that only experienced logistics professionals can provide.
A well-established commercial logistics technology platform shaped by thousands of freight forwarders and logistics providers evolves continuously through real-world operational feedback, regulatory changes, and the daily complexities of global supply chains. That depth of experience is not easily replicated. It informs smarter architecture, more resilient integrations, and faster adaptation to what’s happening in the real world.
In an era of rapid AI advancement, that real-world foundation matters more, not less. The most effective technology strategies will build on proven platforms, using AI to enhance capabilities while relying on logistics industry expertise to ensure innovations perform where it counts, in the day-to-day execution of global logistics.
Source: Ed Rusch, Chief Marketing Officer, Magaya
