SEPTEMBER 5, 2026

“So what are you doing with your money right now?” is the question every Fool gets at a family dinner, and the one we can never answer in a single line. Here we’ve tried anyway. Each analyst below lays out how they’re investing at this moment, and what got them there, in a few sentences flat.

Some have gotten more defensive. Others are doubling down on what’s been working. Use their answers to reflect on where your money is sitting.

By Tom Gardner
Motley Fool Co-Founder and CEO

Our Hidden Gems market indicators are flashing warnings on valuation and speculation. Therefore, I’m reviewing any investments that combine high growth, high beta, and high valuation with any risk to their moat. I’m willing to take some off the table.

• Current stance: My investing focus is on adding companies classified as Cautious and Moderate in Fooldom.
• Primary focus: Companies with high rates of return on capital, AI expertise and/or insulation from AI threats, and forward-leaning leadership.
• What has changed: AI is a disruptive wrecking ball. With uncertain futures and eroding moats, I’m looking for terra firma.
• Investor takeaway: I believe in owning 50+ stocks. I believe in actively managing a portfolio while, in dollar terms, maintaining an average holding period of 5+ years.

By Jason Hall
Team Rule Breakers

I’m sticking to my plan: Hold roughly 10% in cash and 10% in bonds; the rest in stocks. I won’t be adding new cash until 2027, so smart asset allocation helps me avoid trying to time the market.

• Current stance: A motley mix of everything. Have a plan, but be flexible to the risks and opportunities!
• Primary focus: High-growth equities; dividend/income stocks; cash/short-term yields; value/distressed assets.
• What changed: Financial independence could be less than a decade away with a prudent plan, financial discipline, and a little bit of luck.
• Investor takeaway: You can only make the most out of the best stock picks if you have a plan you can stick to!

By Lou Whiteman
Team Hidden Gems

I’m avoiding most of the AI trade due to valuations but looking for value among well-established financials and industrials, as well as small, speculative start-ups, while also assessing stocks that have run up considerably as potential sale or partial-sale targets.

• Current stance: Opportunistically hunting for new value.
• Primary focus: High-growth equities; dividend/income stocks; cash/short-term yields; value/distressed assets.
• What changed: Regardless of where we are in the cycle, my investing strategy is to seek out areas where market inattention creates value.
• Investor takeaway: The challenge in investing is identifying opportunities created from market inefficiencies, while also finding quality businesses that will be rewarded over the long term as more rational pricing takes hold.

By Yasser El-Shimy
Team Rule Breakers

I am building up my exposure toward physical commodities/precious metals and biopharma, two sectors I believe will be immune to or beneficiaries from the inflationary super cycle we started during COVID, but is accelerating with deficit spending, energy and food shortages, and debased currencies.

• Current stance: Opportunistically hunting for new value
• Primary focus: Energy, precious metals, and biopharma.
• What changed: I expect some sovereign debt crises to come starting with Japan and the UK (two countries that import much of their energy and food needs) and spread to similar economies in Europe, Asia, and even the U.S.
• Investor takeaway: Make sure your portfolio has exposure to sectors that can do well in an adverse macroeconomic/geopolitical situation as the one we are currently undertaking.

By Tim Green
Team Hidden Gems

I’m always looking for misjudged and mispriced opportunities, but I haven’t been finding many lately, so the cash in my portfolio has been rising.

• Current stance: A mix of getting more defensive and hunting for new value
• Primary focus: I’m always looking for stocks with good growth prospects that the market is mispricing.
• What changed: Valuations are high, and where they’re not, AI has introduced a tremendous amount of uncertainty.
• Investor takeaway: We’re in the middle of a technological sea change with AI. Constantly evaluating why you own the stocks you own is more important than ever.

By Toby Bordelon
Team Rule Breakers

I am generally staying the course right now, with high exposure to stocks, but I have recently been trimming a few positions and writing more covered calls to increase my cash balance.

• Current stance: Getting more defensive/raising cash
• Primary focus: High-growth equities; cash/short-term yields.
• What changed: I’ve seen several of my stocks run up in price significantly recently. That’s good news, and I am very much a believer in the Rule Breakers “let your winners run” philosophy. But in some cases, the increases mean that a position is becoming a larger portion of my portfolio than I would like. In those cases, I think some targeted trimming is warranted. Even more so as I’m becoming more wary of overall market optimism.
• Investor takeaway: Hold to your personal portfolio allocation strategy and constraints. Let the market inform your approach, but don’t let it control you.

By Matt Frankel, CFP®
Team Hidden Gems

I’m taking a more cautious approach than usual, focusing on established businesses with stable cash flow.

• Current stance: Getting more defensive/raising cash
• Primary focus: Dividend/income stocks; value/distressed assets.
• What changed: The stock market is on the more expensive end of the spectrum, historically speaking.
• Investor takeaway: There’s no way to accurately time the market, and just because stocks are expensive doesn’t mean they can’t keep rising. But by focusing on excellent businesses at reasonable valuations, you can set yourself up nicely for whatever comes next.

Foolish Final Thoughts

Every answer above is someone putting real money behind a belief they’ve said out loud. That’s harder than it sounds. It means naming what has to go right, and living with what happens if it doesn’t. Do the same with whatever you’re weighing this month. Write down the belief before you buy, then check it in three years.

Today’s Question!

How are you investing, and why?

Debate with friends and family, or become a member to hear what your fellow Fools are saying!

The changes occurred amid an unusual rollout of the blog post. OpenAI originally planned for the post to go live at 2 p.m. ET, but it took almost another two hours before it was widely viewable online.

When OpenAI’s X account tweeted out the blog post at 3:32 p.m., the link was not loading properly, returning an error message. At 3:50 p.m., OpenAI CEO Sam Altman posted the link, writing, “We hit a little snag getting the blog post deployed, but it is really great.” Multiple commenters were still unable to see it, and were getting the same error, as did Fortune. When we checked back about an hour later, it was visible and loading properly.

It turns out OpenaAI actually published the blog shortly after 2pm but retracted it for reason the company said it could not disclose, but which it said were unrelated to the benchmark performance figures. (OpenAI first told us it was a bug in the content management system, and then an internet outage.) Upon republishing the blog, it had different evaluation metrics that seemed to favor Astra—and some figures have continued to change even since then.

The revelation of the changes comes amid intense competition in the AI industry, as companies release updates to their large language models at a frenetic pace, each seeking to pull ahead of the other. The focus on metrics also highlights the challenges of measuring the performance of large language models using standardized benchmark tests and concerns that the specs are prone to manipulation and gamesmanship.

“We care deeply about getting evaluations right,” an OpenAI spokesperson told Fortune. “Most evaluations have noise within a few percentage points based on the exact checkpoint, scaffold, and evaluation run used in reporting. For our launch blog, we made fixes to ensure the numbers represent our best estimate of available model performance, so that users can make meaningful comparisons.”

Discrepancies between the first and final published blogs—and the numbers are still changing

Among the most notable changes was Astra’s reported hallucination rate. In the first internet archive snapshot of the blog post from 2:23 p.m. ET, it was 4.2%. It remained that number for several more snapshots, the last being a fifth at 3:11 p.m. ET—about 10 minutes before OpenAI tweeted out the final version.

But the hallucination rate, along with four other metrics, changed in the sixth archival snapshot of the page taken at 5:20 p.m.—after everyone could likely finally see the blog. It was halved down to 2% for Astra. The scores for Astra’s predecessor, GPT-5.6 Sol, also went down from 12.2% to 9.4%. OpenAI has continued to change this metric; as of this writing, the hallucination rates are back up to their original 4.2% and 12.2%.

OpenAI also seems to have given GPT-5.6 Sol a big boost on its internal version of the ExploitBench cybersecurity evaluation, going from 5.5% in the first version to 11.5% in the later versions. OpenAI said it is currently investigating reverting that number back to 5.5% because it says the 11.5% result reflects a reasoning level that is not commercially available for Sol.

Astra is especially good at mathematics, OpenAI says, a quality the company highlights in the opening paragraph of the announcement page. While that metric did not change in the snapshots for Astra—it stays at 97.6% for the FrontierMath Tier 4 (v2) eval—OpenAI did briefly alter the scores for GPT-5.6 Sol and Anthropic’s latest model, Fable 5.1.

The result of these changes made Astra briefly appear significantly better at math than those two models. In the first snapshot (2:23 p.m. on Sept. 3), Anthropic’s Fable 5.1 model’s score is 87.8%. By 5:17 p.m., it’s dropped nearly 10 percentage points to 78%. Today, it’s back up to 83%. Similarly, GPT-5.6 Sol’s scores go from 83%, down to 80.5%, and back up to 83% today.

The changes in metrics began even before OpenAI first published its blog at 2 p.m. An embargoed pre-publication draft the company provided to Fortune and other media organizations listed Astra’s score on the ARC-AGI-3 evaluation as 98.6%. It’s now 99.99% in the live blog.

“We always verify evals before publication so adjustments between draft and final version are normal,” a company spokesperson said at the time. OpenAI also noted that the creator of the benchmark, the Arc Prize Foundation, found that Astra performed at 99.9% in its independent assessment, provided the model was given a particularly powerful harness (a set of tools the model can use to complete tasks). It performed at 63%—still significantly better than any other AI model currently in public release—when given the benchmark’s standard harness. OpenAI said “things like harness, reasoning level and other factors inform evals.”

“Benchmaxxing”—or improving accuracy?

Different research teams at OpenAI oversee different metrics, and are responsible for calculating and reporting them to a central team to publish. OpenAI is open about the fact that the numbers are achieved under the best possible conditions and may be slightly different from the models available in the production ChatGPT product that most users can access. “Evaluation scores are the maximum at any effort,” reads a disclaimer on the blog. The company includes further caveats on each metric in footnotes.

Accuracy is elusive, as multiple numbers can be considered accurate based on the conditions in which the tests occurred. But some AI experts wonder if there’s also “benchmaxxing” involved. This is a known practice in the AI industry—not just at OpenAI—to maximizing scores by re-running evaluations with different conditions.

“This can be done in a very tight timeframe, and it’s better for their marketing,” said Anka Reuel and Mike Hardy, researchers at the Stanford Intelligent Systems Laboratory and Stanford Trustworthy AI Lab. They also pointed out that the GPT-6 Astra system card, which should contain more technical information on how the evaluations were performed, does not always properly explain them. For the internal hallucination benchmark, for example, the system card provides “barely any details about the evaluation,” they said. “It doesn’t even include the number of test items.”

This re-running of the numbers could be why Astra’s coding capabilities also got a marginal boost in the later versions of the blog post, up from 57.7% to 57.9%. Though it’s a negligible difference, OpenAI seemed to care enough about it to swap in the new and improved number.

Not all changes OpenAI made portrayed Astra more favorably. For example, two Anthropic model scores improve in the different versions of the healthcare-focused eval HealthBench Professional. Claude Fable 5.1 goes from 56.6% to 58.1%, and Opus 5 goes from 54.5% to 56.4%. The scores for models made by other AI companies are usually taken from published leaderboards and do not involve OpenAI itself running assessments on rivals’ models.