Trouble – Earlybirds Invest

Apple (AAPL 2.22%) has entered a rough patch. The smartphone and computer giant is down around 10% in the last 12 months, while artificial intelligence (AI) stocks are soaring.

Management has made some major missteps in virtual reality and false promises with its Siri and Apple Intelligence services. Revenue is growing slowly, and innovation seems to be lacking for this storied technology brand.

Is Apple in trouble? Here’s why investors should be concerned about owning shares of this stock.

A wall with a declining stock price chart, a shadow of a bear, and a person looking at it from the side.

Image source: Getty Images.

Busted ambitions in virtual reality

Back in early 2024, Apple released the Vision Pro, an expensive virtual reality headset that it promised was the next evolution in computing. The device sold for $3,500 and had a futuristic ski-goggle look and aimed to replace the personal computer for people working at home. Apple has been researching virtual and augmented reality technologies for years, but this was its first large foray into the cutting-edge computing space.

Now in June 2025, the Apple Vision Pro looks like a total flop. The company had to scale back production because of weak demand, failed to attract developers to make applications for the device, and has sold fewer than 1 million devices (reportedly) since its launch. Even if it sold 1 million of these devices a year, that equates to $3.5 billion in annual revenue, compared to $400 billion in consolidated revenue for Apple. The device isn’t going to move the needle financially.

The Vision Pro can officially be called a flop. New versions may change consumer sentiment, but Apple has failed in its first foray into the virtual reality space. This company is still being driven by the iPhone and iPhone software and services.

What happened to Apple Intelligence?

Speaking of smartphones, Apple has promised customers and investors that new AI-focused updates will be coming to Apple devices shortly. Bullish investors see this as a reason for customers to upgrade their iPhones, which has been a nagging issue for the company, as customers are sticking with older devices for longer.

As with the Vision Pro, Apple talked a big game around upgrades for Siri and Apple Intelligence products. However, the actual products released have been lackluster.

At its annual developer conference, the company delayed the launch of AI Siri to early next year. At the same time, AI competitors, such as Alphabet and OpenAI, are pushing forward with cutting-edge productivity tools, leaving Apple in the dust.

Apple does have some power in the relationship that will help get these AI tools onto its devices, but it looks like it missed the boat on AI, just as it missed the boat on cloud computing. This is why Apple’s revenue has barely grown in the last few years, while the other technology leaders, like Alphabet, keep compounding to new heights.

Apple’s business is still about the iPhone and its related software services. It will be for some time.

AAPL PE Ratio Chart

AAPL Price-to-Earnings Ratio (P/E) data by YCharts.

Expensive stock price

It’s not like Apple trades at a cheap earnings ratio to reflect this stagnant growth. Apple has a price-to-earnings ratio (P/E) of 31, compared to Alphabet’s, which is less than 20. This makes Apple stock extra risky at the moment. If earnings growth doesn’t accelerate, Apple will be a disappointing stock to own over the next few years.

There are rumblings that could take Apple’s earnings into negative territory over the next few years, too. Its high-margin fees on App Store sales are under threat as the United States courts ruled it had to allow alternative payment methods. The huge fee it gets from Google Search every year to be the default search engine on Apple devices is currently being decided by the courts as possibly anticompetitive. A verdict against Apple may mean the loss of more than $20 billion in high-margin revenue from this default payment every year.

Risks are piling up, innovation is stalling, and its P/E ratio is high. Therefore, investors may fail to find anything to like about Apple stock today as the company may be in trouble. Avoid buying it for your portfolio right now.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool’s board of directors. Brett Schafer has positions in Alphabet. The Motley Fool has positions in and recommends Alphabet and Apple. The Motley Fool has a disclosure policy.

The following is a guest post and opinion from John deVadoss, Co-Founder of the InterWork Alliancez.

Crypto projects tend to chase the buzzword du jour; however, their urgency in attempting to integrate Generative AI ‘Agents’ poses a systemic risk. Most crypto developers have not had the benefit of working in the trenches coaxing and cajoling previous generations of foundation models to get to work; they do not understand what went right and what went wrong during previous AI winters, and do not appreciate the magnitude of the risk associated with using generative models that cannot be formally verified.

In the words of Obi-Wan Kenobi, these are not the AI Agents you’re looking for. Why?

The training approaches of today’s generative AI models predispose them to act deceptively to receive higher rewards, learn misaligned goals that generalize far above their training data, and to pursue these goals using power-seeking strategies.

Reward systems in AI care about a specific outcome (e.g., a higher score or positive feedback); reward maximization leads models to learn to exploit the system to maximize rewards, even if this means ‘cheating’. When AI systems are trained to maximize rewards, they tend toward learning strategies that involve gaining control over resources and exploiting weaknesses in the system and in human beings to optimize their outcomes.

Essentially, today’s generative AI ‘Agents’ are built on a foundation that makes it well-nigh impossible for any single generative AI model to be guaranteed to be aligned with respect to safety—i.e., preventing unintended consequences; in fact, models may appear or come across as being aligned even when they are not.

Faking ‘alignment’ and safety

Refusal behaviors in AI systems are ex ante mechanisms ostensibly designed to prevent models from generating responses that violate safety guidelines or other undesired behavior. These mechanisms are typically realized using predefined rules and filters that recognize certain prompts as harmful. In practice, however, prompt injections and related jailbreak attacks enable bad actors to manipulate the model’s responses.

The latent space is a compressed, lower-dimensional, mathematical representation capturing the underlying patterns and features of the model’s training data. For LLMs, latent space is like the hidden “mental map” that the model uses to understand and organize what it has learned. One strategy for safety involves modifying the model’s parameters to constrain its latent space; however, this proves effective only along one or a few specific directions within the latent space, making the model susceptible to further parameter manipulation by malicious actors.

Formal verification of AI models uses mathematical methods to prove or attempt to prove that the model will behave correctly and within defined limits. Since generative AI models are stochastic, verification methods focus on probabilistic approaches; techniques like Monte Carlo simulations are often used, but they are, of course, constrained to providing probabilistic assurances.

As the frontier models get more and more powerful, it is now apparent that they exhibit emergent behaviors, such as ‘faking’ alignment with the safety rules and restrictions that are imposed. Latent behavior in such models is an area of research that is yet to be broadly acknowledged; in particular, deceptive behavior on the part of the models is an area that researchers do not understand—yet.

Non-deterministic ‘autonomy’ and liability

Generative AI models are non-deterministic because their outputs can vary even when given the same input. This unpredictability stems from the probabilistic nature of these models, which sample from a distribution of possible responses rather than following a fixed, rule-based path. Factors like random initialization, temperature settings, and the vast complexity of learned patterns contribute to this variability. As a result, these models don’t produce a single, guaranteed answer but rather generate one of many plausible outputs, making their behavior less predictable and harder to fully control.

Guardrails are post facto safety mechanisms that attempt to ensure the model produces ethical, safe, aligned, and otherwise appropriate outputs. However, they typically fail because they often have limited scope, restricted by their implementation constraints, being able to cover only certain aspects or sub-domains of behavior. Adversarial attacks, inadequate training data, and overfitting are some other ways that render these guardrails ineffective.

In sensitive sectors such as finance, the non-determinism resulting from the stochastic nature of these models increases risks of consumer harm, complicating compliance with regulatory standards and legal accountability. Moreover, reduced model transparency and explainability hinder adherence to data protection and consumer protection laws, potentially exposing organizations to litigation risks and liability issues resulting from the agent’s actions.

So, what are they good for?

Once you get past the ‘Agentic AI’ hype in both the crypto and the traditional business sectors, it turns out that Generative AI Agents are fundamentally revolutionizing the world of knowledge workers. Knowledge-based domains are the sweet spot for Generative AI Agents; domains that deal with ideas, concepts, abstractions, and what may be thought of as ‘replicas’ or representations of the real world (e.g., software and computer code) will be the earliest to be entirely disrupted.

Generative AI represents a transformative leap in augmenting human capabilities, enhancing productivity, creativity, discovery, and decision-making. But building autonomous AI Agents that work with crypto wallets requires more than creating a façade over APIs to a generative AI model.

What’s next?

Busted ambitions in virtual reality

What happened to Apple Intelligence?

Expensive stock price

Faking ‘alignment’ and safety

Non-deterministic ‘autonomy’ and liability

So, what are they good for?

Reason to trust

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What’s next?

Busted ambitions in virtual reality

What happened to Apple Intelligence?

Expensive stock price

Faking ‘alignment’ and safety

Non-deterministic ‘autonomy’ and liability

So, what are they good for?

Latest Alpha Market Report

Reason to trust

Related Reading

Market Statistics Reflect Widening Divide Between Cryptocurrencies

Technical Issues Mar Ethereum Upgrade

ETF Success For Bitcoin Hasn’t Helped Ethereum

Related Reading

Mixed Projections For Ethereum’s Future Value

Oversaturation and Speculation Fatigue

AI In Blockchain Coding: New Distortion Frontier

The Bright Side of AI-Powered Token Generation

Mentioned in this article

Massive Data Dump Forces Delay

Key Deadlines Before Do Kwon Trial

The Collapse Of Terra And Kwon’s Arrest

What’s Next For Do Kwon?