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The Silicon Valley AI Reality Check Tokenmaxxing was the hottest trend in Silicon Valley earlier this year, with CEOs encouraging employees to push AI usage as far as it would go. Then the bill came due. Uber reportedly blew through its annual AI budget in a few months, some companies cut Claude licenses for parts of their org, and Meta killed its internal leaderboard. Tiffany Luck's AI Investment Philosophy This tension between hype and ROI is exactly where NEA partner Tiffany Luck lives these days. She got her start convincing companies that e-commerce was the future, and now she's all in on AI, especially when it comes to the possibilities for "magic moments" in the consumer business. The Future of Personal Agents On this episode of TechCrunch's Equity podcast, Luck joins Rebecca Bellan to talk about the future of personal agents, her thoughts on this year's AI IPOs, and how startups are stepping in to help enterprises track return on AI spend. Where AI Investment is Heading As companies continue to grapple with the balance between AI innovation and practical ROI, Luck's perspective offers valuable insights into where the industry is heading and how businesses can navigate this new technological landscape.

The AI ROI Conundrum Enterprises are still struggling to determine the return on investment (ROI) for their AI initiatives. This challenge was highlighted by the rapid adoption of AI usage earlier this year, which has since slowed down as companies face budget constraints and difficulties in measuring the effectiveness of their AI investments. The Hype and Reality of AI Adoption The trend of 'tokenmaxxing' was briefly popular in Silicon Valley, with CEOs pushing employees to maximize AI usage. However, this enthusiasm was short-lived, as companies like Uber reportedly exhausted their annual AI budgets in just a few months. Some organizations even cut back on their AI licenses, and Meta discontinued its internal leaderboard. Expert Insights from NEA's Tiffany Luck NEA partner Tiffany Luck, who has experience convincing companies of the potential of e-commerce, is now focused on AI, particularly in the consumer business. She believes AI can create 'magic moments' and joins Rebecca Bellan on TechCrunch's Equity podcast to discuss the future of personal agents, AI IPOs, and how startups are helping enterprises track AI ROI. The Role of Startups in AI ROI Tracking As enterprises struggle to measure the effectiveness of their AI investments, startups are stepping in to offer solutions. These startups aim to help companies track their return on AI spend, providing valuable insights and tools to optimize AI adoption. The Future of AI and Personal Agents Luck shares her thoughts on the future of AI, including the potential for personal agents and the impact of AI on the consumer business. Her insights provide valuable perspectives on the evolving AI landscape and the opportunities that lie ahead.

The Launch of Claude Fable 5 Anthropic has made its most powerful AI model accessible to the general public for the first time through Claude Fable 5, a version of its Mythos model equipped with comprehensive safety guardrails. The launch represents a significant step in making advanced AI technology more widely available while maintaining strict safety protocols. Technical Capabilities and Limitations Claude Fable 5 excels in software engineering, knowledge work, and vision-based tasks. However, Anthropic has implemented hard safety limits in high-risk areas including cybersecurity, biology, chemistry, and distillation. In these sensitive domains, the model blocks responses and defaults to Claude Opus 4.8. Early data indicates that at least 95% of Fable sessions run entirely on the model's own responses, with fallbacks being rare occurrences. Market Strategy and Access Tiers Fable 5 is available through Anthropic's Claude API and consumption-based Enterprise plans. Currently, the model is included at no extra cost in Pro, Max, Team, and seat-based Enterprise plans through June 22. After this date, Anthropic will require usage credits, though plans exist to restore it as a standard subscription feature as soon as possible. Concurrently, Anthropic is deploying Mythos 5, a new version of the advanced model, to organizations already approved for access. Pricing and Enterprise Adoption The pricing for both Fable 5 and Mythos 5 is set at $10 per million input tokens and $50 per million output tokens—double the cost of Opus 4.8. This premium pricing reflects the model's advanced capabilities but may serve as a deterrent for widespread adoption. Many enterprises are already grappling with AI costs, with some reporting unexpectedly high bills or exceeding yearly AI budgets early. Despite these concerns, some organizations like Rakuten see significant value in Fable 5's self-reflection capabilities, which enable highly autonomous operations. Safety Measures and Data Retention Anthropic has implemented robust safety measures for Fable 5, including extensive stress-testing with jailbreak attempts. The company reports that internal and external red-teaming efforts failed to find universal jailbreaks over 1,000 hours of testing. As an additional safety layer, Anthropic is requiring a 30-day retention on all traffic, even for enterprises with previous zero-retention agreements. The data will be used exclusively to defend against complex attacks and identify false positives, potentially setting an industry precedent for mandatory data retention with powerful AI models. Performance Validation and Industry Impact Third-party testing has validated Fable 5's exceptional performance. Analytics company Hex reported that Fable achieved 90% on its core analytics benchmark for complex, long-running analytical tasks. Vibe-coding platform Base44 noted its superior capability for "one-shotting full apps" and excellent tool-calling functionality. AI-powered workspace Genspark reported that Fable outperformed all other models in evaluations, particularly excelling in UI design and game coding. These endorsements position Fable 5 as a leading model in its class, potentially influencing industry standards for AI performance and safety. Broader Context: Anthropic's Market Position The launch of Fable 5 occurs as Anthropic prepares to enter the public markets, positioning itself alongside OpenAI and Elon Musk's SpaceX in the competitive AI landscape. This move follows Anthropic's recent plea for major global AI labs to establish coordinated safety measures on frontier AI development. The company has warned that AI systems are advancing rapidly toward recursive self-improvement (RSI), where models could autonomously enhance themselves without human intervention. As Anthropic brings more powerful models to market, its approach to balancing accessibility with safety could shape industry practices for years to come.

The LeadApple is making a strategic move to attract smaller developers by eliminating cloud API costs for those with fewer than 2 million first-time App Store downloads. The tech giant announced this initiative during its Worldwide Developers Conference, positioning its AI infrastructure as a more accessible alternative in an increasingly expensive AI landscape.Apple's New Developer-Friendly AI StrategyDuring its developer keynote, Apple revealed that developers meeting the "under 2 million" threshold can now use its Foundation Models running in Private Cloud Compute without incurring cloud API costs. This move mirrors Apple's Small Business Program, which offers lower commission rates to smaller developers. The company emphasized that "getting started exploring ideas shouldn't be held back by infrastructure costs," highlighting its commitment to supporting indie developers.Expanding AI Capabilities for DevelopersApple also announced significant expansions to its Foundation Models framework this year, including the addition of image input and support for server models. This enhancement allows developers to integrate the API with their preferred cloud model provider, making large cloud models "as accessible as possible" for more complex tasks. The expanded functionality aims to provide "frontier-tier level intelligence with unparalleled privacy protections," according to Apple's presentation.The Rising Cost of AI ExperimentationThis initiative reflects a growing industry challenge: AI experimentation has become increasingly expensive. By waiving infrastructure fees for smaller developers, Apple is positioning its models as a cost-effective alternative. This move comes amid broader industry trends of fiscal restraint in AI development, with tech giants like Meta and Amazon discontinuing their internal AI token usage leaderboards where developers once competed to spend on AI experimentation.Industry Shift Toward Responsible AI SpendingThe timing of Apple's announcement coincides with notable developments in AI budgeting across the tech sector. Uber recently revealed it had exhausted its 2026 AI budget in just four months, signaling a need for more fiscal responsibility in AI adoption. These industry-wide developments suggest that cost-effective AI solutions will become increasingly important as companies balance innovation with budget constraints.Future Outlook for AI AccessibilityApple's strategy likely represents the beginning of a broader trend toward more accessible AI infrastructure. As competition in the AI space intensifies, we can expect major tech companies to develop more cost-effective solutions to attract developers and businesses. Apple's focus on privacy protections alongside cost reduction could differentiate its offerings in a market where many developers are seeking both affordability and security for their AI initiatives.

The Lead Microsoft's recent major pricing changes for GitHub Copilot have sparked what some are calling the 'Tokenpocalypse' - a fundamental shift in how AI companies charge for their services. As major AI players like Anthropic prepare for IPOs, the industry is moving away from heavily subsidized models toward more sustainable pricing, forcing businesses to confront the true costs of artificial intelligence. The Tokenpocalypse Begins The term 'Tokenpocalypse' emerged after Microsoft announced it would start charging more per token for GitHub Copilot rather than using a flat rate model. This shift reflects a broader industry realization that the current AI ecosystem is heavily subsidized by investor money, with costs that far exceed what customers are currently paying. p>As Sean O'Kane noted on TechCrunch's Equity podcast, this pricing change is inevitable: 'This whole ecosystem is heavily, heavily subsidized by investor money. And so stuff that seems like it has no cost is, in fact, incredibly expensive. And now we're going to get to a point where more of that cost is going to get passed on to the end consumer.' The Financial Reality Check Companies are already feeling the impact of these pricing changes. Uber, for example, went through a complete cycle in just a month and a half - from initially blowing through their AI budget to implementing caps and usage restrictions. This rapid adjustment highlights the financial challenges businesses face as AI costs become more apparent. The pricing mechanisms currently in place were established before solid business models had formed around AI technology. As Kirsten Korosec pointed out, 'The whole tokenmaxxing thing has become a thing, peaked, and now is seen disfavorably, within six months.' This rapid evolution of attitudes toward AI usage and pricing demonstrates how quickly the landscape is changing. The IPO Profitability Question As AI companies prepare for IPOs, they face awkward questions about profitability. Anthropic's upcoming S-1 filing will likely contain numerous token-related risk factors that weren't anticipated just months ago. The fundamental question remains: Can these AI labs reduce costs and advance technology enough to meet customers' willingness to spend? Sean O'Kane raised this critical point: 'Can these AI labs collapse that cost [and] progress the tech enough in a way that it eventually meets in the middle with customers' appetite for spending?' This question becomes even more pressing when considering that even premium pricing models like ChatGPT Plus at $20 per month still don't cover the true costs of advanced AI services. The Future of AI Business Models The path to profitability for AI companies may require transformations similar to what Uber underwent. Uber had to fundamentally change its business model, expand into new areas, and adjust its relationship with customers and drivers to achieve profitability. AI companies may need to make equally significant changes to their operations and value propositions. Meanwhile, government regulation is evolving alongside these market changes. President Trump recently signed a narrow executive order designed to give the government a chance to review powerful AI models, adding another layer of complexity to the rapidly shifting landscape. As Kirsten Korosec noted, the pace of change in the AI industry is unprecedented: 'That's why I'm really looking forward to some of these S-1 IPO registration statements, because of the risk [factors]. How do you even write these risks in, because they are evolving before our eyes, and day by day?'

Lead: Anthropic’s IPO Momentum and Investor ConfidenceAnthropic, the AI model maker that just closed a $65 billion fundraise at a $965 billion valuation, has filed a confidential IPO. Daniela Amodei addressed investor doubts about AI returns, emphasizing the need for public‑market capital to fund model training and inference.Anthropic Files Confidential IPO Amid Oversubscribed FundraiseAt the Bloomberg Tech conference, Amodei explained that the decision to go public is driven by the “big upfront cost” of AI development. The company’s private demand remains strong, with multiple investors describing the round as “greatly oversubscribed.”Revenue Surge to $47B Annualized and $1.25B Monthly Compute CostAnnualized revenue reached $47 billion in May, up from roughly $9 billion at the end of 2025.Anthropic’s compute partnership with xAI costs the firm about $1.25 billion per month, as disclosed in SpaceX’s S‑1 filing.Fundraise size: $65 billion at a $965 billion valuation.Implications for AI Spending and Market ConfidenceWhile companies like Uber caution that AI budgets may not always deliver productivity gains, Amodei remains confident that AI use cases—coding, finance, legal, health care—will continue to drive efficiency and creativity. Anthropic’s strategy of avoiding over‑building compute capacity reflects a disciplined approach to capital allocation.Outlook for Anthropic’s Public Debut and AI Industry FundingAmodei predicts that as businesses become more familiar with AI tools, demand will outpace supply, encouraging further public‑market investment. The upcoming IPO could set a benchmark for how AI firms balance private funding, compute costs, and market expectations.

The Lead: Uber's AI Budget Crisis AI is getting expensive, and some companies are cutting back on usage in an attempt to moderate costs. That cohort now includes Uber, which recently instituted internal usage caps as a way to cut down on its exorbitant AI spend after blowing through its entire annual budget in just four months. The Event Details: New Spending Caps and Internal Tracking According to Bloomberg, Uber has implemented a new rule that places a monthly $1,500 cap per employee and per agentic coding tool, including Anthropic's Claude Code or Cursor. The usage is trackable via an internal dashboard that each employee has access to. In certain cases, these caps can be exceeded with permission from the company. The Data Analysis: The Financial Impact of AI Adoption The financial implications are significant. In April, Uber's CTO revealed that the ridesharing giant had consumed its entire annual AI budget in a matter of four months. This accelerated spending occurred after Uber encouraged staff to use AI "as much as possible" and even ranked their internal usage competitively on internal leaderboards, as previously reported by The Information. The Impact Analysis: Questioning AI's Productivity Value Uber's cutback raises a broader issue that the tech industry is currently facing: As enterprises pour money into AI, where exactly is the return on investment? Uber's COO, Andrew Macdonald, recently cast doubt on AI's productivity impact, noting during a podcast appearance that "it's very hard to draw a line" between AI usage and new consumer features. This sentiment reflects a growing skepticism in some quarters about the immediate practical benefits of AI investments. The Prediction: The Future of AI Spending in Tech AI ROI has so far remained a largely theoretical phenomenon that everybody hopes will eventually materialize. As more companies face similar budget challenges to Uber's, we may see a more measured approach to AI adoption across the tech industry. Companies will likely implement stricter usage tracking, set clearer ROI targets, and develop more sophisticated metrics to measure AI's actual impact on productivity and innovation before continuing to scale investments.

The Inevitable Integration of AI in DevelopmentIn 2026, artificial intelligence has become an inseparable tool for developers, yet this reliance may be masking a critical productivity crisis.Researchers at METR discovered that most developers will not participate in studies without AI assistance.This dependency suggests a psychological shift where AI is no longer viewed as an assistant but a requirement.The "Tokenmaxxing" Crisis and Budget BlowoutsThe trend of measuring productivity by token usage, known as "tokenmaxxing," has led to significant financial waste.Amazon shut down its internal leaderboard, Kirorank, after employees gamed the system to run up costs.Uber reportedly exhausted its 2026 AI budget in just four months without measurable project increases.Self-reported data shows a 2x increase in perceived value, but independent analysis suggests 44% of tokens are spent fixing bugs generated by AI.Code review tools indicate AI produces 1.7x more problems than human code.The Hidden Cost of Speed: Maintenance and QualityWhile AI generates code faster, it introduces long-term maintenance costs that developers are currently ignoring.Programmer James Shore warns that trading a temporary speed boost for permanent indenture is a dangerous strategy.Researchers from Singapore Management University have confirmed that AI-generated code can introduce significant long-term maintenance burdens.The Future of Human-AI CollaborationThe industry is moving toward a model where AI is a junior developer that requires constant oversight.Scott Wu (Cognition) admits his AI agent Devin is currently a junior-to-mid-level programmer.Experts recommend that humans must review AI work as carefully as they would a junior developer's code.Software architecture and security design must remain human-centric tasks.

The Enterprise AI Search Milestone Glean, often described as the Google for enterprise, has announced reaching $300 million in annual recurring revenue (ARR), marking a three-fold increase from the $100 million milestone it achieved just 15 months ago. This rapid growth trajectory positions the seven-year-old startup as a significant player in the increasingly competitive enterprise AI search market. Competitive Landscape in Enterprise AI While many AI startups are experiencing rapid growth, Glean's progress stands out as particularly remarkable. After years of operating without direct competition, the company now faces tech giants entering the enterprise AI search space with rival products. According to Glean CEO Arvind Jain, "The first four or five years of our existence, we had no competition. Given how important search is to make AI work in the enterprise, every single company in the world wants to be in this space." The Context Graph Advantage Glean's competitive edge lies in its "context graph" technology, which enables its AI tools to develop a deep understanding of customers' business needs. This is achieved by connecting to and learning from enterprises' internal software systems. Jain maintains that being a first mover is valuable, but equally important is offering a superior product that better addresses enterprise needs. AI Cost Reduction as a Selling Point At a time when many companies are exceeding their AI budgets, Glean's ability to help enterprises cut AI computing costs has become a major selling point. The company claims its context graph helps reduce AI token consumption by providing all necessary information directly to users, resulting in fewer operations. "If you connect your AI to Glean, it gives you all the information that you need to do your work, and that results in AI consuming far fewer tokens compared to if you unleash AI onto your systems directly," Jain explained. Revenue Models and Market Position Last valued at $7.2 billion after raising a $150 million Series F in June, Glean serves notable clients including Databricks, Reddit, Pinterest, and Samsung. The company offers both consumption-based pricing models and hybrid approaches combining fixed monthly fees with usage-based charges. It's worth noting that Glean's $300 million milestone includes some annualized revenue run rate components rather than purely traditional ARR, due to its consumption-based pricing structure. Future Outlook in the Enterprise AI Market As tech giants continue to develop competing products, Glean's focus on deep enterprise understanding and cost efficiency may prove crucial for maintaining its growth trajectory. The company's ability to demonstrate tangible ROI through reduced AI computing costs could become increasingly valuable as enterprises become more budget-conscious with their AI investments.