Latest / Stagflation Conversations with Fexingo: High Inflation, Slow Growth, and 1970s Echoes / The Productivity Trap In Stagflation
Transcript
- Lucas: Look at the August jobs report again. Nonfarm payrolls added about one hundred fifty-nine thousand positions, which sounds solid on the surface, but the unemployment rate is holding steady at four point one percent while average hourly earnings are creeping up to thirty-seven dollars and eighty cents. Luna: Right, but that wage growth is barely keeping pace with the core CPI print of three hundred thirty-seven point eight, so the real purchasing power for those new hires is essentially flatlining. Lucas: Exactly. And here is the part that keeps me up at night: we are told that artificial intelligence is supposed to solve this exact problem by boosting productivity so dramatically that costs fall and wages rise simultaneously. Luna: But the data from Q two shows real GDP growth slowed to only one point five percent, suggesting that AI adoption isn't translating into broader economic acceleration yet. Lucas: That is the key number right there. One point five percent annualized growth in a world where inflation expectations are stuck around two point three seven percent according to the ten-year breakeven. Luna: So we have low growth, persistent inflation, and now we are adding a layer of technological change that might be concentrating benefits rather than distributing them. Lucas: Let's drill into that concentration effect. When a company implements an AI workflow, the immediate result is often lower operational costs per unit, which should theoretically mean cheaper goods for consumers or higher wages for staff. Luna: Instead, what we are seeing is margin expansion. Companies are keeping the savings as profit rather than passing them on, which fuels the very inflationary pressure we are trying to fight. Lucas: This creates a classic stagflationary loop where the economy feels sluggish because consumer demand is weak due to high prices, but the supply side is becoming more efficient in ways that don't help the average worker. Luna: It feels like we are watching the labor market bifurcate even faster, with tech-adjacent roles getting safer while traditional roles face invisible pressure from automation. Lucas: I think you are hitting on something critical here. The JOLTS data shows job openings are still relatively high at seven thousand two hundred seventy-one thousand, which suggests employers are hesitant to hire full-time staff when they can experiment with software. Luna: So the 'slack' in the labor market isn't really slack anymore; it is just displaced into the digital realm where it doesn't show up on traditional employment metrics. Lucas: Let's talk about the Fed's dilemma here. If productivity stays low but prices stay sticky, the central bank has to keep rates higher for longer to crush demand, which further suppresses that one point five percent GDP growth. Luna: And if they cut rates too early hoping to stimulate hiring, they risk reigniting the inflation spiral because the underlying cost structure hasn't actually improved for most businesses. Lucas: This is why the yield curve flashing signals matters so much right now. Investors are pricing in a future where growth remains constrained despite all the technological hype. Luna: It is almost like we are trapped in a middle ground where nothing moves fast enough to fix the balance sheet, whether it is household or corporate. Lucas: Before we go deeper into the sector-specific breakdowns, I want to take a quick second to acknowledge the people who make these conversations possible. Luna: If walking through the economy with us has made something click, consider supporting the show directly. A couple of dollars a month is genuinely what keeps these going — buy me a coffee dot com slash fexingo. Lucas: Your support ensures we can keep digging into these complex data points without relying on ads that distract from the analysis. It makes a real difference. Luna: Thanks for helping us keep the signal clear. Now, let's get back to the numbers. Lucas: Let's look at the services sector specifically, since that is where the wage-inflation gap is most visible. Core services inflation is not cooling down nearly as fast as goods inflation. Luna: Because services are labor-intensive, any attempt to use technology to cut costs faces human resistance or implementation friction that takes years to resolve. Lucas: Right. You can automate a factory line in months, but automating healthcare administration or legal research involves regulatory hurdles and trust issues that slow everything down. Luna: So the 'productivity miracle' is hitting sectors that are already deflationary or stable, while leaving the sticky, expensive sectors largely untouched. Lucas: This asymmetry is dangerous for policymakers. They see overall inflation ticking down slightly and assume progress, but the structural drivers remain entrenched in services. Luna: And if wages continue to rise in those sticky sectors to retain talent, you get a wage-price spiral that is much harder to break than a commodity-driven spike. Lucas: Consider the average hourly earnings data again. Thirty-seven dollars and eighty cents is up from thirty-seven dollars and sixty-five cents, but is that enough to offset the rent and insurance costs? Luna: Probably not. Housing costs are still a massive component of the basket, and they aren't coming down even with interest rate adjustments. Lucas: This brings us back to the consumer outlook plunging in September. People feel squeezed not because they lost their jobs, but because their money buys less every single month. Luna: It is a slow bleed rather than a sudden shock, which makes political and social pressure build up differently than it did during the pandemic peaks. Lucas: We also have to consider the global context. The yen intervention discussions and trade war tensions add another layer of cost-push inflation that domestic productivity can't easily absorb. Luna: Supply chains are getting rerouted, and that transition period is inherently expensive and inefficient, contradicting the idea that globalization is becoming cheaper again. Lucas: So when we talk about the 'productivity trap,' we are talking about a situation where technology helps companies survive the complexity, but doesn't necessarily make the economy healthier. Luna: It feels like we are optimizing for resilience rather than growth, which is a very different game plan for investors and workers alike. Lucas: Resilience is important, but it doesn't pay the mortgage. The question is whether we will see a breakthrough in service-sector productivity before the political cycle forces policy changes. Luna: My guess is no. The institutional inertia in healthcare, education, and professional services is too deep to be shaken by current AI tools alone. Lucas: Which means we are likely looking at a prolonged period of moderate growth and moderate inflation, with occasional shocks from geopolitical events or weather patterns like El Nino. Luna: A lot of volatility disguised as stability. That seems to be the theme of the last eighteen months across all major indices. Lucas: The S&P five hundred is hovering around seven thousand five hundred eighty-six, reflecting that uncertainty. It is not crashing, but it is not soaring either. Luna: Investors are waiting for a clear signal that productivity will finally lift the floor, rather than just raising the ceiling for the top earners. Lucas: Until then, the strategy seems to be defense. Protecting capital, avoiding leverage, and hoping that the Fed gets the timing right on those rate decisions. Luna: It is a frustrating place to be, knowing that the tools exist to solve the problem, but the incentives to use them broadly are misaligned. Lucas: Perhaps the solution isn't just better technology, but better distribution mechanisms for the gains that technology does create. Luna: That sounds like a policy debate, but it starts with how companies choose to report and allocate their efficiency gains. Lucas: We will have to watch the next few earnings calls closely to see if management teams start admitting that their AI investments aren't paying off in the way they projected. Luna: Or if they double down on narrative while quietly restructuring their workforce to hide the lack of tangible productivity results. Lucas: Either way, the data from August tells us that the old models of boom and bust cycles are being replaced by a grind that tests everyone's patience. Luna: And for listeners navigating their careers or portfolios, that grind requires a different kind of stamina than the rapid growth phases we saw a decade ago. Lucas: Stay sharp, keep your expectations grounded, and let's see if the next quarter brings any surprises or just more of the same.