Stefan Rust argues that official inflation data is lagging and understates real price pressure, especially from energy, food, and supply-chain shocks tied to Iran-related disruptions. He says AI is structurally deflationary long term but inflationary near term because of massive capex and labor demand for data centers, while crypto and prediction markets can be used to price and hedge these trends.
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This Wealthion interview centers on Stefan Rust, CEO of Truflation, explaining why he thinks traditional BLS inflation data is outdated and why his firm’s real-time inflation measures are more useful for markets and policy. He says Truflation was built after the Fed described inflation as “transitory” and after he found the BLS methodology too manual, slow, and opaque for a digitized economy. The discussion then moves into specific category differences, with Rust emphasizing shelter weighting, utility separation, and real-time data capture as key reasons Truflation can diverge from the BLS. A large portion of the conversation focuses on current inflation drivers. Rust says the Iran-related supply shock has not fully shown up yet because shipping lags mean the effects take months to work through inventories and ports. …
Near term, the setup is for continued price pressure in energy and food as the Iran-related shock works through delayed supply chains. Tactical risk is that markets are underpricing the lagged pass-through and get surprised by a second wave of inflation prints.
Over the next few months, the base case is a drawn-out inflation hump that only eases once inventories, shipping, and reserves normalize. Confirmation would come from stabilization in oil, freight, and grocery prices; a faster geopolitical resolution would be the main invalidation.
Structurally, the interview argues that inflation will be measured and traded through faster, more decentralized data systems rather than legacy surveys. The broader regime view is that AI and digital assets create a new monetary and pricing layer where energy, compute, and tokenized assets matter more over time.
The public is complacent and inflationary supply effects have not fully shown up yet.
Rust says people are 'in La La Land' and that the inflation shock 'hasn't kicked in yet.'
Truflation was built because official inflation guidance and BLS methodology were seen as outdated and insufficient.
He ties the project’s origin to the transitory-inflation episode and criticism of BLS data collection.
Truflation can update inflation data daily and therefore faster than the government’s monthly release cycle.
He contrasts daily updates with the BLS’s delay.
Tell us about Truflation — what's the goal, what are you seeking to achieve, and how did this all come together?
Stefan Rust explains that Truflation was founded when Janet Yellen and Jerome Powell called inflation 'transitory.' He looked under the hood at how the BLS calculates inflation and found it antiquated, outdated, intransparent, and not suited for an AI-based world. They built an MVP, got backing, and now position themselves as a source of truth for calculating 'true inflation' — peaking at 12% when BLS said 8%, and currently tracking around 2%+.
Why is the BLS method so flawed? What do they do that's so wrong?
Rust says the BLS tracks data manually — 477 individuals go to grocery stores and clock prices, surveying just 1,000 households monthly. In a digitized world with e-commerce, Truflation captures prices in real time daily instead of waiting one month plus 14 days. They also changed the weightings, extracting utility costs from shelter costs because utility variability is far greater than rent or mortgage payments.
Beyond timing, what are the core differences in components and weightings between BLS and Truflation?
Rust explains that BLS bundles a lot into shelter at ~30-35% weighting, while Truflation takes it below 20% by extracting variables out. They disclose all weightings on their website. He notes oil remains one of the biggest drivers — impacting transportation, heating, cooling, and the whole supply chain.
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