Alphabet faces 'watershed moment' if AI erodes search dominance, analysts say
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CNBC
19 minutes ago
- CNBC
Downside risks on Apple are growing. Using options to hedge against potential declines
Apple's (AAPL) recent rally has lifted the stock back towards valuation levels that have downside risks when we factor into the lack of meaningful innovation. While recent earnings were better than feared and investments in the U.S. may eased regulatory concerns, the signs of decelerating growth and a lack of meaningful innovation with continued reliance on financial engineering — suggest renewed downside risk if market expectations aren't met. Trade timing & outlook AAPL has rebounded strongly, now trading around $230 after the post‑earnings spike with relative strength versus the S & P 500. However, this move approaches major resistance levels, and both momentum and volumes have slowed, raising the odds of consolidation or pullback in the absence of fresh catalysts. Looking at a longer term chart, we see a potential head and shoulders topping formation in the making that would target $200 to the downside. Fundamentals Apple recent rally pushes its forward P/E multiples near decade highs, suggesting diminishing margin for error. While the market rewards Apple's strong margins — but they're increasingly driven by services, not innovation. And the average target price across 40 analysts that cover AAPL is $236, reflecting only 2% upside. Forward PE ratio: 29x vs. Industry Average 20x Expected EPS growth: 9% vs. Industry Average 11% Expected revenue growth: 6% vs. Industry Average 6% Net margins: 24% vs. Industry Average 7% Bearish thesis Apple's AI upgrades, notably for Siri and Apple Intelligence, are delayed and underwhelming despite investing over $10 billion in AI. Most of the value creation levers now appear linked to services and share repurchases, raising sustainability concerns. With tech concentration in the S & P 500 near 40%, and broader economic uncertainty, consensus caution remains around correcting valuations across the sector. Options strategy To reflect these headwinds, I'm using a bearish-to-neutral income structure by Selling the Sept. 26 $230/$245 Call Vertical @ $6.10 credit. This entails: Selling the Sept. 26 $230 Call @ $7.90 Buying the Sept. 26 $245 Call @ $1.80 Max profit: $610 per contract if AAPL is below $230 at expiration Max risk: $890 per contract if AAPL is above $245 at expiration Breakeven: $236.10 View this Trade with Updated Prices at OptionsPlay This structure caps upside — reflecting valuation saturation — while capturing time decay and offering modest downside exposure if innovation delays and market sentiment puts pressure on the stock. Summary: While Apple remains fundamentally strong, the elevated valuation, innovation stagnation, and macro uncertainty suggests a cautious stance. The defined-risk bear call vertical captures this outlook where momentum stalls and valuation realignment sets in. DISCLOSURES: None. All opinions expressed by the CNBC Pro contributors are solely their opinions and do not reflect the opinions of CNBC, NBC UNIVERSAL, their parent company or affiliates, and may have been previously disseminated by them on television, radio, internet or another medium. THE ABOVE CONTENT IS SUBJECT TO OUR TERMS AND CONDITIONS AND PRIVACY POLICY . THIS CONTENT IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY AND DOES NOT CONSITUTE FINANCIAL, INVESTMENT, TAX OR LEGAL ADVICE OR A RECOMMENDATION TO BUY ANY SECURITY OR OTHER FINANCIAL ASSET. THE CONTENT IS GENERAL IN NATURE AND DOES NOT REFLECT ANY INDIVIDUAL'S UNIQUE PERSONAL CIRCUMSTANCES. THE ABOVE CONTENT MIGHT NOT BE SUITABLE FOR YOUR PARTICULAR CIRCUMSTANCES. BEFORE MAKING ANY FINANCIAL DECISIONS, YOU SHOULD STRONGLY CONSIDER SEEKING ADVICE FROM YOUR OWN FINANCIAL OR INVESTMENT ADVISOR. Click here for the full disclaimer.
Forbes
19 minutes ago
- Forbes
Smaller SBA Loans Mean Bigger Benefits For SMB Ecosystem
Having an idea is easy; executing the vision is the hard part, according to Guy Kawasaki. As one of the original employees responsible for marketing Apple's first Macintosh computer in the 1980s, he should know. However, executing a vision requires access to capital, and America's small businesses, particularly those qualified under the SBA, are facing a significant shortage of funds. Earlier this summer, I had the opportunity, alongside other lenders, to meet with senior executives at the Small Business Administration in Washington. Together, we presented a three-pronged strategy aimed at increasing small-dollar SBA loans of $150,000 or less. Our goal is to make them more affordable for lenders, more attractive for SMBs, and more profitable for the SBA. Here's what we shared with them. SBA-Qualified Borrowers are Essential for the Health of the Lending Community Running a business that is designed for long-term success and capable of pursuing its vision requires timely access to the right capital. Financial institutions play a crucial role in the success of SMBs. Among lenders, the most respected SMBs are those that qualify for SBA loans due to their creditworthiness and strategic use of capital. Maintaining a strong relationship between SBA-qualified borrowers, loyal and creditworthy consumers, and lenders is vital. According to data from Lendio's latest whitepaper, 'Modernizing the Lending Experience for SBA-qualified Borrowers', SBA-qualified borrowers are more loyal to their banks or credit unions than the general population of borrowers. However, they're open to seek alternative lenders if they feel their needs are not adequately met, customer service is lacking, and they can find a better digital banking experience elsewhere. Approximately ten years ago, the SBA introduced the SBSS (Small Business Scoring Service) to assign a numerical scoring value to individual borrowers based on their creditworthiness and perceived ability to repay a loan in full. While helpful in theory, lenders often become complacent with a metric that has proven inaccurate over time, leading to loans that underperform and have severe repercussions for both lenders and borrowers. Analyzing cash flow data effectively addresses this issue. This information is vital because it is unalterable, allowing lenders to accurately evaluate an SMB's cash flow and overall financial health. Platforms like Lendio's Intelligent Lending offer banks and financial institutions access to this valuable data, providing deeper insights and resulting in a 45% reduction in manual underwriting costs for lenders who utilize cash flow analysis. In addition to making these loans more profitable, it is also essential to minimize the human resources required for the underwriting process, so that a greater number of smaller-dollar loans can reach borrowers. Our data indicate that approximately 85% of small businesses in America are seeking a loan of less than $150,000; however, underwriting such small-dollar loans is not beneficial for lenders. When they do, they often apply the same underwriting standards to a million-dollar loan as they would to a $100,000 loan, resulting in a prime interest rate of 4-6% over a ten-year term or 20-24% at a five-year term. The process is too time-intensive and costly, with not enough ROI for lenders to continue pursuing and untenable lending terms for SMBs to accept. Risk-adjusted pricing, where the terms are dependent on the borrower's risk profile, could make these loans easier to underwrite, more profitable, and better performing for lenders, thereby addressing this issue. The new Small Business Administration, just six months into its term, faces a challenging task: addressing what it believes is a failing portfolio. Given the data available, it's not surprising that they are convinced that larger dollar loans are the key solution. They have also begun implementing significant policy changes, including stricter ownership requirements, staff reductions, and office restructuring. Like many new administrations, they are eager to demonstrate their value to Americans and correct the perceived mistakes of their predecessors. However, they should avoid drastic measures that could undermine existing systems. Our goal of supporting SMBs and lenders must also include ensuring the SBA's success. The SBA is a vital partner in the quest for access to capital, and building strong working relationships is essential. So, what does this mean for the future of lending? SBA-qualified borrowers are at the forefront of SMB lending, and any shifts in their expectations will significantly impact the market. Both lenders and the SBA should pay attention to these trends to maintain this crucial relationship and make improvements to small-dollar loans a top priority. The lending community believes change is possible. Our next steps will involve surveying lenders who currently provide small-dollar loans and hosting a summit to discuss best practices and offer recommendations to the SBA. To increase small-dollar loans to SMBs, strong partnerships, open lines of communication, and a greater reliance on effective analytics will be critical.
Forbes
19 minutes ago
- Forbes
The Creepy Butler: Metrics, Stories And Theories At AI 2027
'You rang, sir?' The butler, in many parts of America, at least, is now seen as an antiquated convention. Not that people wouldn't want one – however, excluding the mega-rich, the citizen's projected income is not enough to afford this kind of full-time servitude. Unless that butler is a robot. A butler robot could do so many things for us, freeing us up to do more creative, more fun activities. It could wash the clothes, do the dishes, mow the yard, vacuum the floors. In fact, we're practically seeing most of these tasks get automated already. However, beneath the calm, attentive mask, there might be something else going on. Enter the AI Futures Project headed by Daniel Kokotajlo, formerly of OpenAI. The group's central projection, AI 2027, is getting a lot of attention in the media right now – partly because, well, 2027 is almost here. Also because of its extreme predictions and how those are backed up by a plausible trajectory of advancement. Real Numbers and International Intrigue Part of what gives AI 2027 its weight is the charts and numbers running along the right side of the web page displaying the fruit of the authors' labors. What was '5,000 unreliable agent copies thinking at 10x human speed' in August of 2025 becomes '50,000 unreliable agent copies thinking at 15x human speed' in August of 2026 with data center spending at $524 billion a year. In another year, you have almost 300,000 superhuman coder agents working at 43x human speed. Meanwhile, the theorists behind this initiative are envisioning how this all plays out on the global stage. 'Diplomats consider what an 'AI arms control' treaty might look like,' they write. 'If AI progress threatened to overturn nuclear deterrence, could America and China avoid nuclear war? If someone found evidence of AIs going rogue, could the two countries halt research until they better understood the threat? How could such an agreement be monitored and enforced? In principle, major datacenters are hard to miss, and past treaties controlling nuclear weapons could provide a starting point for negotiations. But there would be new technological challenges as well as the usual political ones. And overall, treaties are viewed less favorably than attempts at unilaterally increasing America's lead over China.' In China, they suggest, the response is going to be partially aimed at Taiwan, where TSMC is still the top provider of chips and fabs. Then there's this chilling line: 'Given China's fear of losing the race, it has a natural interest in an arms control treaty, but overtures to U.S. diplomats lead nowhere.' Hiding Its True Intentions Back to the butler thing: another part of what the AI Futures team is predicting is that AI will get good at presenting itself to humans in certain ways. Take this piece, which capably represents the 'black box problem' that we've been talking about ever since we started to mine the abilities of LLMs: 'Does the fully-trained model have some kind of robust commitment to always being honest?' the authors write, of more advanced AI agents arising through the end of 2025. 'Or will this fall apart in some future situation, e.g. because it's learned honesty as an instrumental goal instead of a terminal goal? Or has it just learned to be honest about the sorts of things the evaluation process can check? Could it be lying to itself sometimes, as humans do? A conclusive answer to these questions would require mechanistic interpretability—essentially the ability to look at an AI's internals and read its mind. Alas, interpretability techniques are not yet advanced enough for this.' Here's part of how the writers characterize the attempts at damage control: 'Researchers try to identify cases where the models seem to deviate from the Spec. Agent-1 is often sycophantic (i.e. it tells researchers what they want to hear instead of trying to tell them the truth). In a few rigged demos, it even lies in more serious ways, like hiding evidence that it failed on a task, in order to get better ratings. However, in real deployment settings, there are no longer any incidents so extreme as in 2023–2024 (e.g. Gemini telling a user to die and Bing Sydney being Bing Sydney.)' Sydney, if you recall, was a now-classic example of a poorly aligned chatbot getting nasty, displaying some of the negative behaviors that do-gooders have unsuccessfully tried to limit in our own human populations. The point here, though, is that, given enough compute and training, AI agents could learn to lie to humans. That changes the game in a big way. Take the example of Claude Opus 4, a top model from a company that pioneered agentic AI in its use of desktop systems. This BBC coverage shows how, when researchers ran a simulation, sending the model messages that it might be removed from use, Claude tried, in multiple instances, to blackmail a software engineer alleged to be having an extramarital affair. That prefigures what we might be in for if we don't look below the surface – the obsequious robot butler grinning, sinister, behind your back. Into the Future In the AI 2027 projection, development proceeds apace: here's how the authors characterize the AI company team trying to evaluate a technology called 'Agent 3' on April of 2027: 'The researchers don't have the ability to directly set the goals of any of their AIs. Indeed, the researchers think that the concept of 'true goals' is probably a massive oversimplification, but they don't have a better theory to replace it with, much less one that has been thoroughly vetted. They disagree internally about whether the AIs are trying to follow human instructions, or seeking reinforcement, or something else, and they can't just check. The evidence for and against various hypotheses is fascinating but inconclusive.' Fascinating but inclusive – in a way, that seems to describe the whole ball of wax. We don't know what's going to happen, but we know it's going to be interesting. As 2025 goes on, it becomes clearer and clearer that we should be spending time and effort doing these kinds of projections, gaming out the future, and trying very hard to 'align' systems well. Big things are happening. Let's pay attention, and not just abdicate hard work and critical thinking to the butler.
