Apple has been under pressure this year. It's playing catch-up to its fellow tech giants on artificial intelligence, it's seen its stock fall by double digits since the year began, it closed a store in China for the first time ever this week, and looming US tariffs on Beijing threaten its supply chain. On Thursday, the company released its third-quarter earnings of the fiscal year as investors scrutinize how the iPhone maker might turn things around. Despite the gloomy outlook, the company is still worth more than 3tn, and it beat Wall Street's expectations for profit and revenue this quarter. Apple reported a massive 10% year-over-year increase in revenue to 94.04bn, and 1.57 per share in earnings.

Whether it's poaching top talent away from competitors, acquiring AI startups or proclaiming that it will build data centers the size of Manhattan, Meta has been on a spending spree to boost its artificial intelligence capabilities for months now. The massive splurge is paying off, according to Meta's chief executive. In a new memo posted on Wednesday ahead of the company's quarterly earnings report, Mark Zuckerberg, describes his ambitions for developing what he calls "superintelligence". "Over the last few months we have begun to see glimpses of our AI systems improving themselves," Zuckerberg wrote. "The improvement is slow for now, but undeniable. Developing superintelligence is now in sight."

Microsoft, the world's second-most valuable company, is dumping enormous sums of money into its artificial intelligence efforts. At the same time, the company is earning money hand over fist. The enterprise software giant reported fiscal fourth-quarter results that exceeded expectations on Wednesday as the company races to acquire datacenters and talent, which continues to be investigated by investors. The company predicted its capital expenditure for the next fiscal year would top 100bn, a 14% increase from the year prior. It's the fifth quarter in a row that Microsoft has beaten Wall Street's expectations.

Large Language Models (LLMs) are frequently utilized as sources of knowledge for question-answering. While it is known that LLMs may lack access to real-time data or newer data produced after the model's cutoff date, it is less clear how their knowledge spans across historical information. In this study, we assess the breadth of LLMs' knowledge using financial data of U.S. publicly traded companies by evaluating more than 197k questions and comparing model responses to factual data. We further explore the impact of company characteristics, such as size, retail investment, institutional attention, and readability of financial filings, on the accuracy of knowledge represented in LLMs. Our results reveal that LLMs are less informed about past financial performance, but they display a stronger awareness of larger companies and more recent information. Interestingly, at the same time, our analysis also reveals that LLMs are more likely to hallucinate for larger companies, especially for data from more recent years. The code, prompts, and model outputs are available on GitHub.

In Table-to-Text (T2T) generation, existing approaches predominantly focus on providing objective descriptions of tabular data. However, generating text that incorporates subjectivity, where subjectivity refers to interpretations beyond raw numerical data, remains underexplored. To address this, we introduce a novel pipeline that leverages intermediate representations to generate both objective and subjective text from tables. Our three-stage pipeline consists of: 1) extraction of Resource Description Framework (RDF) triples, 2) aggregation of text into coherent narratives, and 3) infusion of subjectivity to enrich the generated text. By incorporating RDFs, our approach enhances factual accuracy while maintaining interpretability. Unlike large language models (LLMs) such as GPT-3.5, Mistral-7B, and Llama-2, our pipeline employs smaller, fine-tuned T5 models while achieving comparable performance to GPT-3.5 and outperforming Mistral-7B and Llama-2 in several metrics. We evaluate our approach through quantitative and qualitative analyses, demonstrating its effectiveness in balancing factual accuracy with subjective interpretation. To the best of our knowledge, this is the first work to propose a structured pipeline for T2T generation that integrates intermediate representations to enhance both factual correctness and subjectivity.

Tesla has reported its biggest decline in quarterly revenue in more than a decade as CEO Elon Musk's political activity weighs on the electric carmaker brand's reputation. Revenue fell to 22.5bn for the April-June quarter from 25.5bn a year earlier, according to its earnings report, which Tesla released after the closing bell on Wall Street. Analysts on average were expecting revenue of 22.74bn, according to data compiled by LSEG. Revenue from car sales declined by 16 percent. Tesla attributed the revenue dip to a decline in vehicle deliveries.

Uber will invest 300m in electric vehicle maker Lucid in a robotaxi deal that aims to start with one major US city late next year. The two companies announced the new partnership on Thursday. Over six years starting in 2026, Uber will acquire and deploy over 20,000 Lucid Gravity SUVs that will be equipped with autonomous vehicle (AV) technology from startup Nuro, the three companies said in a statement. The agreement illustrates the renewed plans and push for financing for self-driving cabs, years after a first wave of autonomous driving investment produced only a limited number of vehicles. Tesla has recently launched a robotaxi trial in Austin, and Alphabet's driverless taxi unit, Waymo, is speeding up its expansion.

Production-grade LLM systems require robust adherence to dozens or even hundreds of instructions simultaneously. However, the instruction-following capabilities of LLMs at high instruction densities have not yet been characterized, as existing benchmarks only evaluate models on tasks with a single or few instructions. We introduce IFScale, a simple benchmark of 500 keyword-inclusion instructions for a business report writing task to measure how instruction-following performance degrades as instruction density increases. We evaluate 20 state-of-the-art models across seven major providers and find that even the best frontier models only achieve 68% accuracy at the max density of 500 instructions. Our analysis reveals model size and reasoning capability to correlate with 3 distinct performance degradation patterns, bias towards earlier instructions, and distinct categories of instruction-following errors. Our insights can help inform design of instruction-dense prompts in real-world applications and highlight important performance-latency tradeoffs. We open-source the benchmark and all results for further analysis at https://distylai.github.io/IFScale.

Recent breakthroughs in artificial intelligence (AI) have triggered surges in market valuations for AI-related companies, often outpacing the realization of underlying capabilities. We examine the anchoring effect of AI capabilities on equity valuations and propose a Capability Realization Rate (CRR) model to quantify the gap between AI potential and realized performance. Using data from the 2023--2025 generative AI boom, we analyze sector-level sensitivity and conduct case studies (OpenAI, Adobe, NVIDIA, Meta, Microsoft, Goldman Sachs) to illustrate patterns of valuation premium and misalignment. Our findings indicate that AI-native firms commanded outsized valuation premiums anchored to future potential, while traditional companies integrating AI experienced re-ratings subject to proof of tangible returns. We argue that CRR can help identify valuation misalignment risk-where market prices diverge from realized AI-driven value. We conclude with policy recommendations to improve transparency, mitigate speculative bubbles, and align AI innovation with sustainable market value.

Tracking the strategic focus of companies through topics in their earnings calls is a key task in financial analysis. However, as industries evolve, traditional topic modeling techniques struggle to dynamically capture emerging topics and their relationships. In this work, we propose an LLM-agent driven approach to discover and retrieve emerging topics from quarterly earnings calls. We propose an LLM-agent to extract topics from documents, structure them into a hierarchical ontology, and establish relationships between new and existing topics through a topic ontology. We demonstrate the use of extracted topics to infer company-level insights and emerging trends over time. We evaluate our approach by measuring ontology coherence, topic evolution accuracy, and its ability to surface emerging financial trends.