A lot has happened since my last update. Between summer distractions and a stubborn case of writer's block, I skipped a few posts I meant to share. But as the saying goes, better late than never! Here is the long-awaited update.
The tech ecosystem has officially entered its “reckoning” phase. If the last two years were defined by unconstrained optimism, massive capital deployments, and a collective fear of missing out, today's reality is dictated by unit economics, margin calls, and questions about return of investments. From corporate boardrooms to hedge fund desks, the blind faith in raw optimism is giving way to hard accountability.
Here is a breakdown of the structural shifts reshaping the current landscape.
1. From TokenMaxxing to Tokenomics
The Shift: Moving from metric-less AI consumption to rigorous optimization and ROI.
Earlier this year, tech culture birthed a bizarre internal performance metric: "TokenMaxxing." Spurred by remarks from tech leaders - that software engineers should be judged by how many hundreds of thousands of dollars in tokens they consume—enterprises began tracking raw AI consumption as a proxy for productivity. Meta employees famously built internal leaderboards ranking token usage, and companies gave digital badges to workers burning the most API bandwidth and tokens
The result was predictable: unconstrained agentic loops, massive prompt slop, and astronomical monthly token bills.
The industry has hit a wall, shifting violently from TokenMaxxing to strict Tokenomics.
- Corporate Throttling: Companies like Microsoft, Amazon, Meta, and Uber have aggressively reined in internal AI budgets. Microsoft issued internal directives stating that maximizing raw token usage is no longer a corporate objective.
- The ROI Crisis: Financial teams are utilizing specialized cost-intelligence platforms to audit where tokens are going. Spending millions on frontier models to have an agent loop infinitely in a code repository without shipping a feature is no longer tolerated.
- The New Playbook: As stated by Uber's CTO, the goal has evolved into treating efficiency as an engineering problem rather than a budget restriction—leveraging advanced prompt caching, open-weight models, and smaller, specialized default models to do more with less. Army of startups are born to go after Tokenomics aspects of AI..
2. The End of the SaaSocalypse
The Shift: The stabilization and painful evolution of software-as-a-service models.
For eighteen months, the "SaaSocalypse"—the narrative that generative AI and agentic workflows would entirely obliterate seat-based SaaS software—terrorized public tech valuations. Critics argued that if an AI agent can do the work of ten people, companies would slash their Salesforce, ServiceNow, or Workday seat counts to zero, starving legacy software giants of revenue.
We are now witnessing the End of the SaaSocalypse, not because legacy SaaS won, but because the alternative proved too chaotic to deploy natively.
- The Failure of DIY Agent Orchestration: Enterprises that tried to build their own agent layers from scratch using raw LLM APIs ran into massive deployment, security, and cost hurdles including unpredictability..
- The Return of the Work Graph: Legacy SaaS companies successfully defended their territory by embedding AI directly into where the data already lives. It turns out enterprise buyers prefer paying a predictable premium to an established vendor over managing an unconstrained, multi-million dollar token budget for a custom-built bot.
- The Value Shift: Seat-based pricing is steadily morphing into usage-based or outcome-based pricing, signaling that SaaS isn't dying; it is simply adapting to the cognitive era.
3. The "Situational Awareness" Blowup
The Shift: The spectacular downfall of the market's most levered AI macro trade.
Leopold Aschenbrenner’s Situational Awareness manifesto predicted a rapid, trillion-dollar industrial mobilization toward AGI, a narrative he parlayed into a massive hedge fund. However, operating with staggering leverage, the fund proved highly vulnerable when semiconductor and cloud computing stocks pulled back.
A chain reaction of margin calls forced the fund to rapidly liquidate its public tech holdings. While the fund preserved its private startup equity, this public blowup marked the exact moment the market stopped trading AI on pure narrative and began demanding actual cash flow. The Situational Awareness blowup echoes the 1998 collapse of Long-Term Capital Management (LTCM) as both firms relied on massive leverage to back high-conviction, concentrated theses that ultimately triggered severe margin calls when markets turned. LTCM had 25-to-1 leverage and was started by two Nobel Laureates and former Fed Vice-Chairman. But markets continued the momentum and eventually crashed in 2000-2001...if similar timelines hold, I am predicting at repeat of correction in 2028-29 (which would coincide with 100 years of famous 1929 crash)
4. Warshnomics: Monetary Policy Meets the Tech Reset
The Shift: The end of the "easy money" Fed era and its immediate impact on tech valuations.
As tech companies struggle with internal token economics, they face an equally harsh macro environment driven by Federal Reserve Chairman Kevin Warsh. The arrival of "Warshnomics" represents a fundamental restructuring of US monetary policy that could have impact on high-multiple tech landscape.
- The Death of Forward Guidance: Under Chairman Warsh, the Fed has aggressively dismantled the predictable, slow-moving "forward guidance" era. Instead, the central bank operates with data-dependent agility, keeping markets on edge and eliminating the safety net tech investors used to rely on.
- Uncompromising 2% Target: Warshnomics places a hawkish focus on a strict 2% inflation mandate and an aggressive reduction of the Fed's balance sheet.
- The Valuation Squeeze: Cheap capital is no longer available exactly when trillions of $ are needed for AI infrastructure buildout. Markets are asking for return on investment and discipline in capex spends and rewarding companies like Microsoft and punishing companies like Google and SpaceX just based on their Capex spending plans. That means many of these companies would adopt "no capex increase" tune over next couple of quarters.
The Bottom Line
The era of easy money, narrative-driven leverage, and vanity metrics in tech is over. Whether you are an engineer optimizing prompt caches to protect corporate margin, a SaaS vendor rewriting contract terms, or a growth investor navigating the new realities of Warshnomics, the directive is simple: Show me the return on investment (Capex, Tokens consumed or margin loans)
History reminds us that groundbreaking technology cannot bypass the grueling 10-to-20-year timeline required for widespread enterprise and societal adoption. We witnessed this long, evolutionary crawl with the personal computer, the internet, the smartphone, and cloud computing—none of them transformed global infrastructure overnight. Like Odysseus enduring his brutal ten-year journey back to Ithaca, the tech ecosystem is realizing that the shortcut to a magical AI destination (including AGI) may take at least a decade...personally my yardstick is simple...Cure of Cancer expedited with help of AI..and I am really hoping that with trillions of $ investment going in, we can see that happening in next decade - preferably before 10th anniversary of ChatGPT!
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