DOGE is dead. Following a statement from OPM Director Scott Kupor that the agency “doesn’t exist”, we analyse how Musk’s “chainsaw” approach failed to survive Washington.

If T.S. Eliot were covering the Trump administration, he might note that the Department of Government Efficiency (DOGE) ended not with a bang, but with a bureaucrat from the Office of Personnel Management (OPM) politely telling a reporter, “That doesn’t exist.”

Today, November 24, 2025, marks the official, unceremonious end of the most explosive experiment in modern governance. Eight months ahead of its July 2026 deadline, the agency that promised to “delete the mountain” of federal bureaucracy has been quietly dissolved. OPM Director Scott Kupor confirmed the news this morning, stating the department is no longer a “centralised entity.”

It is a fittingly chaotic funeral for a project that was never built to last. DOGE wasn’t an agency; it was a shock therapy stunt that mistook startup velocity for sovereign governance. And as of today, the “Deep State” didn’t just survive the disruption—it absorbed it.

The Chainsaw vs. The Scalpel

In January 2025, Elon Musk stood on a stage brandishing a literal chainsaw, promising to slice through the red tape of Washington. It was great television. It was terrible management.

The fundamental flaw of DOGE was the belief that the U.S. government operates like a bloatware-ridden tech company. Musk and his co-commissioner Vivek Ramaswamy applied the “move fast and break things” philosophy to federal statutes that require public comment periods and congressional oversight.

For a few months, it looked like it was working. The unverified claims of “billions saved” circulated on X (formerly Twitter) daily. But you cannot “bug fix” a federal budget. When the “chainsaw” met the rigid wall of administrative law, the blade didn’t cut—it shattered. The fact that the agency is being absorbed by the OPM—the very heart of the federal HR bureaucracy—is the ultimate irony. The disruptors have been filed away, likely in triplicate.

The Musk Exodus: A Zombie Agency Since May

Let’s be honest: DOGE didn’t die today. It died in May 2025.

The moment Elon Musk boarded his jet back to Texas following the public meltdown over President Trump’s budget bill, the soul of the project evaporated. The reported Trump-Musk feud over the “Big, Beautiful Bill”—which Musk criticized as a debt bomb—severed the agency’s political lifeline.

For the last six months, DOGE has been a “zombie agency,” staffed by true believers with no captain. While the headlines today focus on the official disbanding, the reality is that Washington’s immune system rejected the organ transplant half a year ago. The remaining staff, once heralded as revolutionaries, are now quietly updating their LinkedIns or engaging in the most bureaucratic act of all: transferring to other departments.

The Human Cost of “Efficiency”

While we analyze the political theatre, we cannot ignore the wreckage left in the wake of this experiment. Reports indicate over 200,000 federal workers have been displaced, either through the aggressive layoffs of early 2025 or the “voluntary” buyouts that followed.

These weren’t just “wasteful” line items; they were safety inspectors, grant administrators, and veteran civil servants. The federal workforce cuts impact will be felt for years, not in money saved, but in phones that go unanswered at the VA and permits that sit in limbo at the EPA.

Conclusion: The System Always Wins

The absorption of DOGE functions into the OPM and the transfer of high-profile staff like Joe Gebbia to the new “National Design Studio” proves a timeless Washington truth: The bureaucracy is fluid. You can punch it, scream at it, and even slash it with a chainsaw, but it eventually reforms around the fist.

Musk’s agency is gone. The Department of Government Efficiency news cycle is over. But the regulations, the statutes, and the OPM remain. In the battle between Silicon Valley accelerationism and D.C. incrementalism, the tortoise just beat the hare. Again.

Frequently Asked Questions (FAQ)

Why was DOGE disbanded ahead of schedule?

Officially, the administration claims the work is done and functions are being “institutionalized” into the OPM. However, analysts point to the departure of Elon Musk in May 2025 and rising political friction over the aggressive nature of the cuts as the primary drivers for the early closure.

Did DOGE actually save money?

It is disputed. While the agency claimed to identify hundreds of billions in savings, OPM Director Scott Kupor and other officials have admitted that “detailed public accounting” was never fully verified. The long-term costs of severance packages and rehiring contractors may offset initial savings.

What happens to DOGE employees now?

Many have been let go. However, select high-level staff have been reassigned. For example, Joe Gebbia has reportedly moved to the “National Design Studio,” and others have taken roles at the Department of Health and Human Services (HHS).

NVDA earnings beat expectations, fueling AI momentum, but Nvidia stock price shows investor caution.

Nvidia’s latest earnings report has once again underscored its central role in the global AI revolution. The chipmaker, whose GPUs power everything from generative AI models to advanced data centers, posted blockbuster results that exceeded Wall Street expectations. Yet, despite the strong NVDA earnings, the Nvidia stock price slipped, reflecting investor caution amid sky-high valuations and intense competition. According to Yahoo Finance, the company’s results remain one of the most closely watched indicators of AI’s commercial trajectory.

Key Earnings Highlights

For the fourth quarter of fiscal 2025, Nvidia reported record revenue of $39.3 billion, up 78% year-over-year. Data center sales, driven by surging demand for AI infrastructure, accounted for $35.6 billion, a 93% increase from the prior yearNVIDIA Newsroom. Earnings per share came in at $0.89, up 82% year-over-year.

On a full-year basis, Nvidia delivered $130.5 billion in revenue, more than doubling its performance from fiscal 2024. This growth cements Nvidia’s dominance in the AI hardware market, where its GPUs remain the backbone of large language models, autonomous systems, and enterprise AI adoption.

Expert and Market Reactions

Analysts on Yahoo Finance’s Market Catalysts noted that while Nvidia consistently beats estimates, its stock often reacts negatively due to lofty expectations. Antoine Chkaiban of New Street Research emphasized that five of the past eight earnings beats were followed by declines in Nvidia stock, as investors reassess valuations.

Investor sentiment remains mixed. On one hand, Nvidia’s results confirm its unrivaled position in AI infrastructure. On the other, concerns about sustainability, competition from rivals like AMD, and potential regulatory scrutiny weigh on market psychology.

NVDA Stock Price Analysis

Following the earnings release, NVDA stock price fell nearly 3%, closing at $181.08, down from a previous close of $186.60. Despite the dip, Nvidia shares remain up almost 28% over the past yearBenzinga, reflecting long-term confidence in its AI-driven growth story.

The volatility highlights a recurring theme: Nvidia’s earnings power is undeniable, but investor sentiment is sensitive to valuation risks. With a trailing P/E ratio above 50, the stock is priced for perfection, leaving little margin for error.

Forward-Looking AI Implications

Nvidia’s earnings reaffirm that AI is not just a technological trend but a revenue engine reshaping the semiconductor industry. The company’s GPUs are embedded in every layer of AI innovation—from cloud hyperscalers to startups building generative AI applications.

Looking ahead, analysts expect Nvidia’s revenue to continue climbing, with consensus estimates projecting EPS growth of more than 40% next year. However, the company must navigate challenges including supply chain constraints, intensifying competition, and geopolitical risks tied to chip exports.

Outlook

Nvidia’s latest earnings report demonstrates the company’s unmatched leverage in the AI economy. While NVDA earnings continue to impress, the Nvidia stock price reflects investor caution amid high expectations. For long-term shareholders, the trajectory remains promising: Nvidia is positioned as the indispensable supplier of AI infrastructure, a role that will likely define both its market value and the broader tech landscape.

In the months ahead, Nvidia’s ability to balance innovation with investor confidence will determine whether its stock can sustain momentum. As AI adoption accelerates globally, Nvidia’s role as the sector’s bellwether remains unchallenged.

The trillion-dollar LLM race is over. The true disruption will be Agentic AI—autonomous, goal-driven systems—a trend set to dominate TechCrunch Disrupt 2025.

When OpenAI’s massive multimodal models were released in the early 2020s, the entire tech world reset. It felt like a gold rush, where the only currency that mattered was GPU access, trillions of tokens, and a parameter count with enough zeroes to humble a Fortune 500 CFO. For years, the narrative has been monolithic: bigger models, better results. The global market for Large Language Models (LLMs) and LLM-powered tools is projected to be worth billions, with worldwide spending on generative AI technologies forecast to hit $644 billion in 2025 alone.

This single-minded pursuit has created a natural monopoly of scale, dominated by the five leading vendors who collectively capture over 88% of the global market revenue. But I’m here to tell you, as an investor on the ground floor of the next wave, that the era of the monolithic LLM is over. It has peaked. The next great platform shift is already here, and it will be confirmed, amplified, and debated on the hallowed stage of TechCrunch Disrupt 2025.

The future of intelligence is not about the model’s size; it’s about its autonomy. The next billion-dollar companies won’t be those building the biggest brains, but those engineering the most competent AI Agents.

🛑 The Unspoken Truth of the Current LLM Market

The current obsession with ever-larger LLMs—models with hundreds of billions or even trillions of parameters—has led to an industrial-scale, yet fragile, ecosystem. While adoption is surging, with 67% of organisations worldwide reportedly using LLMs in some capacity in 2025, the limitations are becoming a structural constraint on true enterprise transformation.

We are seeing a paradox of power: models are capable of generating fluent prose, perfect code snippets, and dazzling synthetic media, yet they fail at the most basic tenets of real-world problem-solving. This is the difference between a hyper-literate savant and a true executive.

Here is the diagnosis, informed by the latest ai news and deep-drives:

• The Cost Cliff is Untenable: Training a state-of-the-art frontier model still requires a multi-billion-dollar fixed investment. For smaller firms, the barrier is staggering; approximately 37% of SMEs are reportedly unable to afford full-scale LLM deployment. Furthermore, the operational (inference) costs, while dramatically lower than before, remain a significant drag on gross margins for any scaled application.
• The Reliability Crisis: A significant portion of users, specifically 35% of LLM users in one survey, identify “reliability and inaccurate output” as their primary concerns. This is the well-known “hallucination problem.” When an LLM optimizes for the most probable next word, it does not optimise for the most successful outcome. This fundamentally limits its utility in high-stakes fields like finance, healthcare, and engineering.
• The Prompt Ceiling: LLMs are intrinsically reactive. They are stunningly sophisticated calculators that require a human to input a clear, perfect equation to get a useful answer. They cannot set their own goals, adapt to failure, or execute a multi-step project without continuous, micro-managed human prompting. This dependence on the prompt limits their scalability in true automation.

We have reached the point of diminishing returns. The incremental performance gain of going from 1.5 trillion parameters to 2.5 trillion parameters is not worth the 27% increase in data center emissions and the billions in training costs. The game is shifting.

🔮 The TechCrunch Disrupt 2025 Crystal Ball: The Agentic Pivot

My definitive prediction for TechCrunch Disrupt 2025 is this: The main stage will not be dominated by the unveiling of a new, larger foundation model. It will be dominated by startups focused entirely on Agentic AI.

What is Agentic AI?

Agentic AI systems don’t just generate text; they act. They are LLMs augmented with a planning module, an execution engine (tool use), persistent memory, and a self-correction loop. They optimise for a long-term goal, not just the next token. They are not merely sophisticated chatbots; they are autonomous problem-solvers. This is the difference between a highly-trained analyst who writes a report and a CEO who executes a multi-quarter strategy.

Here are three fictional, yet highly plausible, startup concepts poised to launch this narrative at TechCrunch Disrupt’s Startup Battlefield:

1. Stratagem

• The Pitch: “We are the first fully autonomous, goal-seeking sales development agent (SDA) for B2B SaaS.”
• The Agentic Hook: Stratagem doesn’t just write cold emails. A human simply inputs the goal: “Close five $50k+ contracts in the FinTech vertical this quarter.” The Agentic AI then autonomously:
  • Reasons: Breaks the goal into steps (Targeting $\rightarrow$ Outreach $\rightarrow$ Qualification $\rightarrow$ Hand-off).
  • Acts: Scrapes real-time financial data to identify companies with specific growth signals (a tool-use capability).
  • Self-Corrects: Sends initial emails, tracks engagement, automatically revises its messaging vector (tone, length, value prop) for non-responders, and books a qualified meeting directly into the human sales rep’s calendar.
  • The LLM is now a component, not the core product.

2. Phage Labs

• The Pitch: “We have decoupled molecular synthesis from human-led R&D, leveraging multi-agent systems to discover novel materials.”
• The Agentic Hook: This startup brings the “Agent Swarm” model to material science. A scientist inputs the desired material properties (e.g., “A polymer with a tensile strength 15% higher than Kevlar and 50% lighter”). A swarm of specialised AI Agents then coordinates:
  • The Generator Agent proposes millions of novel molecular structures.
  • The Simulator Agent runs millions of physics-based tests concurrently in a cloud environment.
  • The Refiner Agent identifies the 100 most promising candidates, and most crucially, writes the robotics instructions to synthesise and test the top five in a wet lab.
  • The system operates 24/7, with zero human intervention until a successful material is confirmed.

3. The Data-Moat Architectures (DMA)

• The Pitch: “We eliminate the infrastructure cost of LLMs by orchestrating open-source models with proprietary data moats.”
• The Agentic Hook: This addresses the cost problem head-on. The core technology is an intelligent Orchestrator Agent. Instead of relying on a single, expensive, trillion-parameter model, the Orchestrator intelligently routes complex queries to a highly efficient network of smaller, specialized, open-source models (e.g., one for code, one for summarization, one for RAG queries). This dramatically reduces latency and inference costs while achieving a higher reliability score than any single black-box LLM. By routing a question to the most appropriate, fine-tuned, and low-cost model, they are fundamentally destroying the Big Tech LLM moat.

🏆 Why TechCrunch is the Bellwether

The shift from the LLM race to Agentic AI is a classic platform disruption—and a debut at Tech Crunch is still the unparalleled launchpad. Why? Because the conference isn’t just about technology; it’s about market validation.

History is our guide. Companies that launched at TechCrunch Disrupt didn’t just have clever tech; they had a credible narrative for how they would fundamentally change human behaviour, capture mindshare, and dominate a market. The intensity of the Startup Battlefield 200, where over 200 hand-selected, early-stage entrepreneurs compete, forces founders to distil their vision into a five-minute pitch that is laser-focused on value.

This focus is the very thing that the venture capital community is desperate for right now. Investors are no longer underwriting the risk of building a foundational LLM—that race is lost to a handful of giants. They are now hunting for the applications that will generate massive ROI on top of that infrastructure. When a respected publication like techcrunch.com reports on a debut, it signals to the world’s most influential VCs—who are all in attendance—that this isn’t science fiction; it’s a Series A waiting to happen.

The successful TechCrunch Disrupt 2025 startup will not have a “better model.” It will have a better system—a goal-driven Agent that can execute, self-correct, and deliver measurable business outcomes without constant human hand-holding. This is the transition from AI as a fancy word processor to AI as a hyper-competent, autonomous employee.

Conclusion: The Era of Doing

For years, the LLM kings have commanded us with the promise of intelligence. We’ve been wowed by their ability to write sonnets, simulate conversations, and generate images. But a truly disruptive technology doesn’t just talk about solving a problem; it solves it.

The Agentic AI revolution marks the transition from the Era of Talking to the Era of Doing.

The biggest LLM is now just a powerful but inert, brain—a resource to be leveraged. The true innovation is in the nervous system, the memory, and the self-correction loop that transforms that raw intelligence into measurable, scalable, and autonomous value.

Will this new era, defined by goal-driven, Agentic AI, be the one that finally breaks the LLM monopoly and truly disrupts Silicon Valley? Let us know your thoughts below.