The next step in AI cost saving: treating frontier models like expensive consultants. Aug 3, 2026, 9:01 PM PT Getting good returns on AI spending is the hottest enterprise topic of the day, and AI consultants have a neat cost-saving hack. Business Insider has reported extensively about how users should route their tasks to the right AI models to get the best bang for their buck. But AI consultants are going one step further in the AI efficiency discussion, saying that the most expensive frontier models should be treated like the expensive consultants and advisors they are. "You wouldn't use the most expensive lawyer for filing some routine things that less expensive lawyers can," said Ameya Kanitkar, the CTO of the San Francisco-based AI measurement platform Larridin. Speaking to Business Insider in July, Kanitkar said models like Fable 5, the most powerful and token-burning model from Anthropic, should be used to create a road map of workflows, while cheaper and smaller models should execute them. "The advisory model basically plans things, breaks down the problems into smaller sets, and has the complete context of how everything's going to work," he said. "And then sub-tasks are delegated to cheaper models." Larridin advises companies on their AI use, giving them visibility into how effectively their employees are using AI tools and how to achieve greater ROI from their AI spend. Lightweight models should do the heavy-lifting. In the same spirit, Michael Murphy, a partner at the Sydney-based AI transformation consultancy Adaptovate, said it's not a great use of company dollars to send "the most powerful model out there to do something that's replacing a Google check or transcribing meeting minutes or creating a creative brief." Murphy said frontier models should be used to strategize, create the initial builds of a new app or website, or tasks that require complex thinking. Then, companies should figure out which "lightweight flashlight models" are most appropriate for day-to-day tasks, he said. It's not just consultants who are vouching for this tactic - industry leaders have also talked about the idea of using frontier models as advisors. In an X post in June, Coinbase CEO Brian Armstrong said he anticipated that "80% of workloads will be running on 99% cheaper models within 12-18 months." The best models should be kept for "IQ maxxing," he said, such as scientific breakthroughs or agent orchestration. AI ROI is a hot priority for executives. Cost-saving tactics like these are becoming more popular as companies become increasingly concerned about not getting proportional returns on their AI spending. Many have abandoned tokenmaxxing, a trend in which companies gave their employees free rein to experiment with AI, urging them to burn as many tokens as possible. Some, like Duolingo, even made AI usage a performance metric. Now, companies are being more conservative, thinking about how to get the most value out of their spend. Several budget hacks have emerged, such as model routing or using open-source Chinese models like Moonshot AI's Kimi K3 or Z.ai's GLM-5.2. A new wave of startups is cashing in on AI ROI, becoming a C-suite priority. The startups, known as AI-routing companies that help steer developers toward different AI models and monitor for overspending, are becoming investor favorites. New York-based startup OpenRouter announced in May that it had raised $113 million, valuing the company at $1.3 billion. And Business Insider was the first to report that OpenRouter's competitor, Concentrate AI, had secured more than $5 million in funding.

Concentrate AI, a New York City-based developer of multi-model routing and token cost management software, has raised $5 million in pre-seed funding. The round was backed by True Ventures and RRE Ventures. The company develops a cross-model task routing platform that monitors infrastructure overspending, optimises token allocation costs and maintains enterprise application uptime by dynamically shifting workloads during model provider outages. Concentrate AI has commercialised its optimisation middleware within its first year of operation and deployed multi-region fallback guardrails across prominent open and closed-source model families. The funding will accelerate product development, expand its engineering team and scale its real-time API load-balancing infrastructure. The company has initiated strategic pilot programmes with high-volume software developers.