# Cost Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Cost", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

After 540,000 Lines of Code, Garry Tan Realizes the Old Game of AI Programming Is Over

YC President Garry Tan's project "Garry's List" involved over 540,000 lines of Rails code, but he concluded this approach is outdated. He argues the software industry is stuck in a "Foxconn factory" mindset: building excessive tests, validators, and control logic to constrain LLMs, which have become cheap and capable enough to work autonomously. The old paradigm treated LLM calls as expensive and code as cheap, leading to complex systems to "manage" the AI. This has now reversed. The future lies in "just-in-time software"—using natural language instructions (Markdown-based "skill packs") and minimal code, letting the AI handle the work. Tan advocates for "skillifying" workflows: after an agent completes a task, it packages the process into a reusable, tested skill pack. This shifts value from writing code to designing capabilities. For example, his agent reviewed 85 hackathon submissions in 30 minutes, a task that previously took days. He emphasizes "tokenmaxxing"—willingly spending on LLM tokens to gain a years-long competitive advantage, as costs are plummeting. The core shift is from measuring output in lines of code to focusing on clarity, taste, and judgment. The best future engineers won't write the most code, but will know what to build and how to unlock the most intelligence with the least code.

marsbit06/02 21:38

After 540,000 Lines of Code, Garry Tan Realizes the Old Game of AI Programming Is Over

marsbit06/02 21:38

Chatbot has been burning money for three years, is it still the 'New Continent' of the AI era?

For years, the AI industry has been guided by a singular "map" — the belief that the AI era's "new continent" would be found in the Chatbot, a super-app akin to the mobile internet's super-apps. This belief was fueled by ChatGPT's explosive 2022 debut. However, three years of heavy investment reveal a different reality: the Chatbot-as-ultimate-entry-point model is struggling. The core issue is economic. Chatbots defy traditional internet economics. Unlike apps with near-zero marginal cost, each AI query consumes significant, expensive compute. More users mean higher costs, not profits. OpenAI, despite ~900M weekly active users, reportedly loses money. The expected network effects and data flywheels that power internet giants are weak in Chatbots, as one user's interactions don't improve another's experience. Monetization is a major hurdle. The subscription model faces low conversion rates, especially in China where users expect AI to be free. The "free + ads" model also struggles. Chatbot interactions often lack commercial intent, and inserting ads compromises the trust essential for an answer engine. Perplexity's minimal ad revenue and subsequent pivot away from ads highlight this difficulty. Switching between Chatbots is easy, making user loyalty low and competition a potential race to the bottom on price. Data suggests the standalone Chatbot's growth is slowing, and user engagement (avg. ~6 mins/day) pales compared to apps like TikTok. The product form itself is limiting; studies show nearly half of interactions are simple Q&A, trapping AI's potential in a passive, single-turn "cage." A contrasting, more successful path is emerging, exemplified by Anthropic. With over 85% of its ~$30B annualized revenue from enterprises, it focuses on AI as a productivity tool, not a companion. The rise of AI Agents (like OpenClaw) and the integration of AI into existing workflows (e.g., Google's AI Overviews, Apple Intelligence in OS) signal a shift. The future may not be a dominant Chatbot app, but AI embedded seamlessly into social apps, operating systems, and hardware — a capability-layer revolution, not a new distribution container. The conclusion is clear: the old "map" centered on a standalone Chatbot super-app is leading to a dead end. To find the true valuable "continent" of the AI era, the industry must update its navigation to prioritize deep integration, practical utility, and sustainable economics over a generic conversation window.

marsbit06/02 10:35

Chatbot has been burning money for three years, is it still the 'New Continent' of the AI era?

marsbit06/02 10:35

After Burning Tens of Billions of Dollars in Tokens, Silicon Valley Giants Start Limiting Employee Token Usage

After burning tens of billions of dollars on AI tokens, major Silicon Valley firms are now restricting employee usage. Companies like Microsoft, Uber, and Salesforce, which heavily promoted AI for "efficiency," are facing a cost crisis. The practice of "tokenmaxxing"—pushing employees to maximize AI tool usage—led to wasteful spending on trivial tasks like checking the weather or writing birthday messages, with studies showing significant hidden costs for bug fixes and code rewrites. The core issue is a misalignment between individual productivity gains and actual business value. While employees use AI to automate tasks they dislike, such as writing reports, this often doesn't translate to increased company revenue or improved core business outcomes. For instance, AI-generated code speeds up development but also sees an 800% increase in "code churn" (code being discarded or rewritten). As a result, only 14% of CFOs report seeing a clear, measurable return on AI investments. Firms are now shifting strategies. Microsoft has revoked most internal licenses for Claude Code, while others are implementing monitoring and cost controls. New tools from companies like Harness and CloudZero aim to track AI spending and tie costs to business results. Some AI vendors, like HubSpot, are moving from token-based pricing to charging based on outcomes, such as "resolved conversations" or "leads generated." This represents a necessary correction in the AI adoption cycle. The challenge now is for companies to move beyond using AI merely to speed up old tasks and instead rethink their workflows and business models fundamentally. The future of enterprise AI depends on proving its value, not just its usage.

marsbit06/01 04:06

After Burning Tens of Billions of Dollars in Tokens, Silicon Valley Giants Start Limiting Employee Token Usage

marsbit06/01 04:06

When Tokens Cost More Than People, 'AI Narrative' Runs Into Trouble

Title: When Tokens Cost More Than People, the "AI Narrative" Hits Trouble The economic sustainability of corporate AI adoption is under scrutiny as token consumption soars while measurable business value remains elusive. Major companies like Uber and Microsoft report struggling to justify rising AI costs, with executives coining terms like "tokenmaxxing" to describe wasteful usage. Data reveals a stark picture: for every dollar spent on AI tokens, only 18 cents translates to user-facing value, with the rest consumed by bug fixes, rework, and friction. The debate splits into bullish and bearish camps. Bulls, like Goldman Sachs analysts, see current inefficiencies as growing pains, predicting a 24-fold increase in token demand by 2030 and a shift towards healthier metrics like "cost per effective action." They point to indicators of real productivity gains and argue current tech valuations are not in bubble territory. Bears, however, highlight an unsustainable model where value is heavily concentrated in semiconductor companies like Nvidia, funded by cloud giants taking on massive debt. Studies show 95% of firms investing in generative AI see zero return. A deeper concern is the circular financial structure between cloud providers (hyperscalers) and AI labs like OpenAI and Anthropic. Billions in cloud service commitments are tied to these labs, which are partly funded by the hyperscalers' own investment. This creates a loop where cloud revenue depends on labs securing continuous external funding to pay their compute bills, which in turn relies on end-corporates willing to pay ever-higher token costs. The sustainability of this cycle is now in question. While not a classic bubble—AI technology is real and delivers productivity for power users—the central issue has shifted. The focus is no longer just on technological capability but on economics: whether the savings AI generates for businesses can outpace the soaring costs and justify the valuations of labs and cloud providers. The era of equating rising token usage with successful AI transformation is over. The bill for AI has arrived, but who ultimately pays remains uncertain.

marsbit05/29 01:44

When Tokens Cost More Than People, 'AI Narrative' Runs Into Trouble

marsbit05/29 01:44

Microsoft to Discontinue Using Claude: Too Expensive, or a Calculated Move?

Microsoft is discontinuing internal access to the AI coding assistant Claude Code for its engineers, citing high costs and transitioning them to its own GitHub Copilot CLI. Reports indicate that Claude Code's usage-based pricing model led to significant expenses, with estimates of $500-$2000 per engineer monthly, prompting cost reviews even at large firms like Uber. While Microsoft officially attributes the move to cost, analysis suggests it may also be a strategic move. Microsoft allowed internal use of Claude Code for six months to benchmark it against Copilot, gathering feedback to improve its own product. This "experiment" highlights deeper challenges: Microsoft lacks a competitive in-house general-purpose AI model, heavily relying on external partners like OpenAI and Anthropic. Claude Code has gained substantial market traction, surpassing Copilot in features like context window size and task completion rates, and is eroding Microsoft's control over the developer ecosystem. The shift signals broader industry concerns over AI pricing models and the strategic risks of dependency on external, rapidly evolving AI tools.

marsbit05/25 10:28

Microsoft to Discontinue Using Claude: Too Expensive, or a Calculated Move?

marsbit05/25 10:28

$67 Billion! AI Boom Fuels Largest Energy Merger in U.S. History

In a landmark deal, NextEra Energy announced a $67 billion acquisition of Dominion Energy on May 18, 2026, marking the largest U.S. utility merger. The primary driver is the insatiable power demand from AI data centers, concentrated in Virginia's "Data Center Alley," where Dominion is the key supplier holding over 51 GW of data center contracts. This acquisition highlights a fundamental shift. While global electricity demand grew 3% in 2025, data center demand surged 17%, driven by AI. This strain is causing irreversible price spikes, like the 76% increase in the PJM grid, and grid instability, as seen in a 2025 Virginia incident where 60 data centers disconnected simultaneously. NextEra, a major renewable energy provider, bets that combining its clean power and storage expertise with Dominion's strategic access to data centers will be transformative. However, the deal raises critical questions about cost distribution. Analysts warn that up to $700 billion in infrastructure costs may be passed to residential consumers through higher bills, creating a disparity where private AI profits rely on publicly subsidized grid upgrades. This merger signals just the beginning of AI's profound reshaping of the energy landscape.

marsbit05/21 08:07

$67 Billion! AI Boom Fuels Largest Energy Merger in U.S. History

marsbit05/21 08:07

3 People with 100 AI Programmers, Burning Through $1.3 Million a Month! OpenAI: I'll Foot the Bill

In a striking demonstration of AI-powered development, Peter Steinberger (creator of OpenClaw) shared that his three-person team spent $1.3 million in one month to run approximately 100 AI agents (primarily Codex instances). OpenAI covered the cost. The expenditure consumed 6.03 trillion tokens across 7.6 million requests. Steinberger argues that, with "fast mode" disabled, the cost falls below that of a single engineer while providing significantly greater output. This "cloud programmer army" handles core but tedious software engineering tasks: reviewing pull requests, finding security vulnerabilities, deduplicating issues, fixing bugs, monitoring benchmarks, and even generating PRs after meetings. This shifts AI's role from merely writing code to maintaining the entire collaborative fabric of a project. Steinberger's tool, CodexBar (a macOS menu bar app), tracks usage and costs across various AI coding services, highlighting how token consumption is becoming a key metric—a new "means of production." The experiment poses a profound question: if token cost ceases to be a barrier, how will software development transform? As model prices fall, the capability for small teams to leverage large numbers of AI agents could become commonplace, fundamentally altering the scale and speed of development. The future, Steinberger suggests, is arriving rapidly.

marsbit05/17 06:20

3 People with 100 AI Programmers, Burning Through $1.3 Million a Month! OpenAI: I'll Foot the Bill

marsbit05/17 06:20

What Makes XRP Unique? Ripple CEO Explains

Ripple CEO Brad Garlinghouse explains the unique properties of XRP, focusing on its origin and design for efficient payments rather than as a general-purpose blockchain. He highlights the XRP Ledger's key features: over 4 billion transactions processed, settlement times of 3-5 seconds, and transaction costs of less than a penny, emphasizing speed, low cost, and scalability. Garlinghouse also credits the dedicated long-running community, or "XRP family," as a core component of its identity and endurance. He concludes that this combination of technical performance, longevity, and strong community support positions XRP for future success.

bitcoinist05/14 17:01

What Makes XRP Unique? Ripple CEO Explains

bitcoinist05/14 17:01

The Essence of AI Layoffs: Why More AI Adoption Leads to More Corporate Anxiety?

The author, awaiting potential inclusion on an 8000-person layoff list, analyzes the true nature of recent "AI-driven" layoffs. They argue that while AI use, particularly tools like Claude for code generation, has skyrocketed and boosted developer output (e.g., 2-5x more code commits), this has not translated into proportional business growth or revenue. The core issue is a misalignment between increased "Input" (code) and tangible "Outcomes" (user value, revenue). AI acts as a costly B2B SaaS, inflating operational expenses without guaranteed returns. Two key problems emerge: 1) The friction that once filtered out bad ideas is gone, as AI allows cheap pursuit of even weak concepts. 2) Organizational "alignment tax"—the difficulty of coordinating across teams—becomes crippling when development velocity outpaces consensus-building. Thus, layoffs serve two immediate purposes: 1) To offset ballooning AI costs (Token consumption) and maintain cash flow, as rising input costs without outcome growth destroys unit economics. 2) To reduce organizational bloat and alignment friction by simply removing teams, thereby speeding up execution in the short term. Therefore, these layoffs are fundamentally caused by AI, even if AI doesn't directly replace roles. They represent a painful correction until companies learn to convert AI-driven productivity into real business outcomes and streamline organizational coordination to match the new pace of work. The cycle will continue until this learning curve is mastered.

marsbit05/12 10:23

The Essence of AI Layoffs: Why More AI Adoption Leads to More Corporate Anxiety?

marsbit05/12 10:23

AI Relay Stations: The Hidden Pitfalls Behind Low Costs, How to Screen and Avoid Them?

AI Relay Stations: The Hidden Risks Behind Low Costs and How to Avoid Pitfalls AI relay stations are becoming a popular gateway to various models, offering lower prices, a wider selection, and a unified interface for tools like Claude Code and Cursor. However, their appeal masks significant risks. Users may unknowingly surrender prompts, code, business documents, customer data, and even full project contexts. The demand is driven by genuine needs: cost savings compared to expensive official APIs (e.g., GPT, Claude), easier access amid regional restrictions, and the push from AI-powered development tools. But not everyone needs a relay station. Light users should exhaust free official quotas first. Heavy users, like developers, can adopt a layered approach, using top models for critical tasks and cheaper local models for routine work. If a relay station is necessary, follow a careful selection and usage protocol: 1. **Verify First:** Test model authenticity, latency, and stability before purchasing credits. Check the quality of provided documentation. 2. **Isolate Configuration:** Use unique API keys for each service, manage them via environment variables, and set usage limits to control costs and potential damage from leaks. 3. **Classify Your Data:** Develop a habit of data grading before sending requests. Only send non-sensitive, public information directly. Desensitize semi-sensitive data (e.g., internal documents) by removing names and specifics. Never send highly sensitive data like passwords, private keys, or confidential customer information. 4. **Handle AI Coding Tools Separately:** Tools like Cursor can send extensive project context (file contents, directory structures, error logs). Use relay stations only for independent, non-core code tasks. For sensitive projects, switch back to official APIs or local models. 5. **Monitor and Prepare an Exit:** Regularly check billing statements, follow platform updates and community feedback, and always have a backup provider. Ensure your setup uses standard OpenAI-compatible APIs for easy migration. Ultimately, relay stations are tools, not default solutions. Their value lies in solving access needs at a controlled cost, but maintaining that control requires proactive risk management through verification, isolation, data classification, and continuous monitoring.

marsbit05/09 10:16

AI Relay Stations: The Hidden Pitfalls Behind Low Costs, How to Screen and Avoid Them?