Author: Scott Buchanan

The Shifting Fortunes of Market Neutral Funds QMNNX and BDMCX

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A market neutral equity fund holds a large number of stocks in both long and short positions, so the net asset value is near zero. In theory, this means that the value of the fund should be fairly insensitive to overall market fluctuations. The fund’s performance depends entirely on the skill of the fund manager in buying (going “long”) stocks with a better chance of going up, while selling short a suite of stocks that are likely to perform more poorly.

Fund managers use criteria (“factors“) such as value, momentum, quality, and mean-reversion to select which stocks to go long versus short. Investors see market neutral funds as a means of producing maybe 4 to 8% return (alpha), with relatively low volatility and low exposure to overall market movements (i.e., low beta).  Institutional investors love funds like these for adding diversification to their portfolios, so they hold many tens of billions of dollars of market neutral funds available only to them.

We lowly retail investors have access to some such funds. I will just focus here on two of the larger and more successful retail market neutral funds, offered by AQR Capital Management, and by BlackRock, respectively. These appear in a mutual fund wrapper, instead of an ETF. As with many mutual funds, there are different investor classes for a given fund family. If you invest a huge amount of money, you get the lowest annual fees (e.g., for QMNIX and BDMIX “Institutional” classes), but all the classes for given fund are invested in the same underlying strategy and assets.

To try to put these on the same basis for comparison for us ordinary folks, I will look at versions of each of these funds that do not require over $1 million additional investment, and which do not have an obnoxious (5.25%) upfront sales load. QMNNX is offered by AQR. It holds some international along with US stocks, and is constantly tweaking its long and short portfolio based on a proprietary set of factors, which are based on an enormous amount of data and calculations. The strategy behind BlackRock’s BDMCX is somewhat different. It probably uses similar types of factors (value, momentum, mean-reversion, etc.), but it tries to specifically match long and short companies within industry categories. Thus, in theory, it should be relatively insulated from a crash of say software stocks, since it is long and short in equal amount in that category.


OK, let’s look at actual performance. Here is a 10-year chart, with QMNNX in orange, BDMCX purple, compared to the S&P 500 in blue and BND (total bond market) in light green.

This chart might make you run screaming out the door – both funds did so poorly in the 2016-2021 timeframe that their ten-year total returns are far less than the broad market. But this is not really a fair comparison. These vehicles are not intended to compete with a 100%-long portfolio. A better comparison is to bonds, and it can be seen that both market neutral funds beat BND (green line) handily over this time period. It is worth noting that the AQR fund QMNNX got decimated in the 2018-2022 – – that was a time when the market rewarded ONLY growth, even for stocks which scored badly on traditional “value”. Thus, QMNNX was long the value stocks which were shunned by the market, and short the tech stocks that roared upward. So, while the overall market was ripping upward, QMNNX went down and down and down, losing some 30% of its value while its investors lost faith and sold out.  The within-industry matching for BlackRock’s BDMCX protected it from such gross losses, though it showed only modest gains in that 2016-2021 time period.

The picture changes entirely when we look at a five-year timeframe (below). QMNNX’s value tilt was finally vindicated in 2022, as the fund soared while tech stocks crashed. Three cheers for diversification! The fund kept up an absolute positive edge over the mighty S&P 500 for each of the years 2023, 2024, and 2025. It seemed like AQR had cracked the code for superior equity returns. Its five-year return is more than double that of the S&P, which is stunning. Meanwhile, BDMCX kept up a steady performance, roughly matching the S&P, but with much lower volatility. That is pretty good. Meanwhile, the rises in interest rates trashed the returns of bonds (green line).

The picture shifts again when we look at one-year total returns (below). BDMCX continues to roughly match the S&P. This is a significant achievement when stocks are in a big bull run, showing that the BlackRock fund managers continue to make very good calls on prospective weak vs strong stocks. QMNNX had a poor first half 2026, with an absolute decline. For some reason, its factors did not correctly forecast relative stock performances. It has started to recover some mojo in the past few months, enough to beat out the long-suffering bond market.

My personal takeaways are:

  • We cannot expect this type of fund to routinely beat or even match overall stocks. However, as diversifiers, they might be compared to say bonds or REITs, and they hold up well in that comparison. The overall U.S. market (dominated by big tech) has been going up so much, for so long, that it may seem like any diversification away from stocks is a waste. Time will tell.
  • The stellar (market-matching or even market-beating) performances seen for QMNNX and BDMCX over the past five years are probably largely flukes, and should not be relied on going forward.
  • Due to its rigorous within-sector net neutral construction, BDMCX is unlikely to soar, but it is also unlikely to crash. I think of it as a “Steady Eddie”.  The looser construction of QMNNX gives it the freedom for great outperformance (especially in a tech crash), but makes it more vulnerable to extended losses if the market moves against its view of reality.

Disclaimer: Nothing here should be taken as advice to buy or sell any security.

What If AI Earnings Fall Far Short of Expectations?

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Trillions of dollars are being plowed into building U.S. data centers to handle future AI compute demand. These are being paid for by rich people and organizations, anticipating juicy returns on their investments. Those juicy returns depend on consumers (individuals or businesses) being willing to pay enormous amounts for access to AI.  Commentators are so enamored with the glorious prospects of how AI will end poverty and maybe even death, that it is hard to find a clear statement of who exactly will pay how much for all this. Lots of folks are happy to pay $20/month for AI. $200/month? Not so much.

I predict that those earnings will fall far short of expectations. We observe that AI consumption is bifurcating into two main markets, a commodity tier and a premium tier (with, of course, sub-tiers within each of those broad categories).  Chinese models are readily available over the internet, and they have proven capable of performing well enough to handle most AI tasks. The Chinese models are priced far lower (on the order of 10X lower) than the big U.S. “frontier” models (ChatGPT from Open AI, and Claude suite from Anthropic). By far the most U.S. compute usage depends on these two labs. Western users are starting to use the Chinese models more and more. This keeps prices so low that the frontier labs are losing money on their AI sales. Sophisticated users automatically route their AI workload to the cheapest feasible provider.

Their will always be a subset of AI usage in the West that requires the highest level of performance, or freedom from Chinese government spying or manipulation, that will be directed to a premium tier. But if that premium tier ends up being only, say, 20% of AI usage in 2028, there is a real question as to whether the financial bases of the data centers being built now can be sustained. There are huge bear and bull arguments on both sides here, which are tough to balance. I got only equivocal “it depends” answers from AI on this.

If the data centers don’t make expected profits, what then? It all depends on how they were financed. Most of the build-out to date has been the big 4 hyperscalers, Google, Amazon, Microsoft, and Meta spending their free cash flow from their other business lines. If it turns out they simply flushed that money down the toilet, no big deal. Just a trillion-dollar whoopsie. The CEOs will still get their bonuses, don’t worry.

But now as more debt financing enters in, the stakes get higher. Analysis seems to show that the debt loads that the big 4 hyperscalers have incurred is manageable – -their base cash flows are so huge that they can manage their own debt. But in the past year we have seen the emergence of monstrous “Special Purpose Vehicles” (SPVs) with a mixture of equity, debt, and guarantees, to finance practically all the upcoming trillion dollars of data centers. This pushes the financing of the balance sheets of the hyperscalers.

If those newer data centers flop, their equity investors will take a hit, leaving their creditors in the hole and in control. One really needs to analyze exactly who those equity and debt holders are for the SPVs.  If the creditors decide to recoup some of their investment by selling the data center for say 60 cents on the dollar, the most likely buyers would be…Google and Amazon. There is a school of thought that this (let the SPVs fail, scoop up their assets at discount) has been their plan all along. World domination in AI compute!  Just like they have achieved world domination in online search and video and shopping. Maybe.

The resulting slowdown in data center investing would likely throw the U.S. economy into slowdown or recession, considering that it’s estimated that fully half of our recent GDP growth has been from circularly-financed AI buildout. Chipmakers’ (Nvidia, Micron, AMD, etc.) profits depend on continued acceleration in AI build-out. If that build-out stalls, or even slows down, chipmaker profits will crater. Whether this risk is already priced into their share prices is debated.

Boilerplate disclaimer: Nothing here should be considered advice to buy or sell any security.

SPMO: One Momentum Stock Fund to Rule Them All

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Academic studies have found that there is a momentum effect with stock prices: a stock which has done well over the past 6-12 months is likely to continue to do better than average over the next six months or so.  A number of funds (ETFs) have been devised which try to take advantage of this factor. This is a relatively effortless exercise: running calcs on stock price movements is way easier than doing a deep fundamental dive into a company’s future earnings potential.

Here we will compare several momentum ETFs against the S&P 500 index. In order to make it an apples-to-apples comparison, I am looking mainly at major momentum funds that primarily draw from the S&P 500 large cap universe of stocks, excluding small-cap or tech only funds. These large cap momentum funds are MTUM, JMOM, and SPMO, plus the newer FMTM. These funds all select stocks according to various rules. Besides trying to identify stocks with raw price momentum, these rules typically aim to minimize risk or volatility.  

I excluded the momentum fund GMOM, since it draws from a different universe of holdings. That fund does not hold individual stocks. Rather, it draws on some 50 different ETFs, including funds that focus on fixed income, commodities, or international or small cap as well as large cap US stocks, seeking to hold funds that show good relative momentum. (In a previous look at momentum funds, we found that GMOM did well in 2022, suffering less of a drawdown than the other funds, but it has lagged ever since; diversifying away from U.S. large caps was a big drag).

A plot of total returns over the past five years (which includes the 2022 correction) is shown below. The returns at the halfway mark (2.5-year mark, 4/1/2024) are shown on the chart.  FMTM only started 18 months ago, so it has no five-year returns. SPMO is orange, and the reference S&P500 line is blue. JMOM (purple) tracks fairly closely to S&P500 most of the time.  MTUM (green line) fell well behind during 2023, though it caught up by August, 2026. SPMO was close to S&P500 for the first two years, then steadily roared ahead over the next three years.

Plain SPY (blue line) held its own against most of these momentum funds in this timeframe. This is partly explained by the fact that SPY itself is a sort of momentum fund: the more a given stock’s price goes up, the bigger its representation in this capital-weighted fund. Also, over the past ten years or so, simply the biggest companies (the big tech quasi-monopolies like Google, Microsoft, etc.) have been generating more and more earnings, leaving the traditional auto and oil and consumer product companies, etc., in the dust – – and the S&P index incorporates this effect.

Key aspects of the funds’ strategies are listed in the table (thanks, Claude) below. JMOM includes the whole Russell 1000 universe of stocks, while SPMO is limited to the top 500 stocks. We note that JMOM is sector neutral, so it cannot be hugely overweight on sector, such as tech. On the other hand, SPMO has no such constraint, and so its major holdings for some time have consisted almost entirely of the giant AI darlings Nvidia, Micron, Broadcom, AMD, Google, etc. That has been the right bet over the past several years (although you pay for it in high volatility). SPMO also has perhaps the longest effective look-back period for calculating momentum. It uses a 12-month period, but excludes the most recent month (to avoid paying too much for a quick, temporary stock price run-up). SPMO is clearly doing something right, since its five-year total return (144 %) is nearly double that of the S&P 500; this is a stunning achievement.

The new entry FMTM has its own quirks: it holds mid-caps as well as large-caps, and it has a significantly shorter time frame for calculating momentum (six months, vs the usual 12 months), AND it reconstitutes every month (instead of six months). So, it reacts very quickly to a rising stock. This sounds great, but it might end up acting on false, short-lived surges in prices. Time will tell.

Turning now to the 1-year results, where FMTM gets a chance to strut its stuff, we find that the new upstart has soundly beaten them all, even SPMO:

 But this FMTM outperformance was very inconsistent. The one-year performance of FMTM was driven by a huge surge in late 2025/early 2026. But a six-month plot shows FMTM dead last among all these funds, while SPMO comes out on top (again). FMTM looks interesting as a smaller “satellite” holding, that may outperform in some regimes, but SPMO seems to be the more solid performer.

Boilerplate disclaimer: Nothing here should be taken as advice to buy or sell any security.

Bond Market Blows Off Treasury Bond Buyback; Gold and Bitcoin Soar

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Scott Bessent used to be a serious financial player. When he was with George Soros’s fund, he helped them make a billion dollars in 1992 by betting against the British pound, and $1.3 billion in 2013 with a bet against the Japanese yen. He also ran his own hedge fund, with as much as $5 billion under management. In 2023-2024 he hitched his wagon to the fortunes of Donald Trump, becoming a large campaign contributor and fundraiser. He was rewarded by being made Treasury Secretary. On January 27, 2025, the U.S. Senate voted to confirm Bessent’s nomination. (The same day, a man with multiple Molotov cocktails and a knife who intended to murder Bessent was arrested at the United States Capitol, an event seemingly lost in the noise of all the attempted assassination attempts against this administration).

The current administration has continued the policy of the previous administration of profligate peacetime federal deficit spending, some 6% of GDP annually, well above the 50-year average of about 3.8%. The only way to finance this spend is to sell more and more Treasury debt. But the more of that debt is out there, the more interest needs to be paid on it. By the mid-2030’s, interest payments will balloon to the point that they, plus mandatory transfer payments like Medicare/Medicaid/Social Security, will consume 100% of tax revenues, with nothing left for discretionary spending (including defense). Being the cabinet officer responsible for financing this mess now is sort of like being CFO of Lehman Brothers in 2008.

Which brings us to the ignominious market response to Bessent’s attempt at bond market intervention last week. As it has become more and more clear to the rest of the world that the U.S. has no intention of reining in its deficit, but instead hopes to deal with it by inflating away the value of the dollar, the market has started to demand greater compensation for holding long-term U.S. debt. If you are wondering why you now have to pay 6.65 % for a 30-year mortgage, wonder no longer. These high interest rates are making it all the more painful for Treasury to fund the deficit.

Secretary Bessent made a surprise announcement last Wednesday that his department would double its maximum purchase of older, less-liquid long-term bonds, from $2 billion per week to $4 billion. The stated purpose of this long-running buyback program is to retire hard-to-trade bonds and replace them with newer, more-liquid bonds. That’s fine, but this snap announcement shortly after its quarterly refunding plan broke with Treasury’s long-held strategy of making ‘regular and predictable’ announcements, and (together with statements by Bessant threatening further intervention) was widely seen as an attempt to talk down the long-term rates.

It worked for about one day. Long term yields initially dipped Wednesday morning by about 0.1%, but by Thursday they were about back to where they were before the announcement. Not only did traders realize the size of the intervention was far too small to move the enormous T-bond market (and would not net decrease T-bonds outstanding), but Treasury’s move became interpreted as a sign of desperation over funding the U.S. deficit. That vibe will not help bring down Treasury bond rates going forward. The value of the dollar dipped on world exchanges, while the price of alternatives such as gold and Bitcoin soared:

That is a chart of Bitcoin price in the past month. HODLers rejoice, the long crypto winter may be over…

WWII Strategic Initiatives 6. Fort Commander’s Lonely Decision Saves Norwegian Government from Capture by Germans

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At 4:21 a.m. on April 9, 1940, a 64-year-old Norwegian colonel six months from retirement had about ninety seconds to decide whether to start a war.

Birger Eriksen, who entered Norwegian military service way back in 1893, commanded Oscarsborg, a sleepy coastal fortress on a rocky island in the Oslofjord, armed with three 28cm (11 inch) Krupp guns dating to the 1890s – – plus a secret weapon the Germans didn’t know about: torpedo tubes from 1901. His garrison was mostly reservists conscripted a week earlier. Out of the pre-dawn dark came six unlit, unidentified warships steaming toward Oslo.  Norway had assumed that its diligently neutral stance would avert any foreign attack, and so was lax about maintaining coastal surveillance. The ships could have been British. Firing on the wrong flag meant international catastrophe; not firing, if they were German, meant the capital fell before breakfast.

Colonel Birger Eriksen, the commander of Oscarsborg on 9 April 1940.

Eriksen had no orders from Oslo and no time to ask. He gave the command anyway, reportedly saying: “Either I will be decorated, or I will be court-martialed. Fire!”

The lead ship was the Blücher, a brand-new 16,000-ton heavy cruiser packed with troops, Gestapo officers, and administrators meant to seize the king, the government, and Norway’s gold reserves in one stroke. Two shells from the fort tore into her at point-blank range, followed by torpedoes into her flank. Within ninety minutes she rolled over and sank, taking hundreds of men down with her. The German attack on Oslo was not completely averted, but it was significantly delayed and hampered.

German Heavy Cruiser Blücher (Image by Bundesarchiv)

That single decision to attack the invading fleet bought Norway’s government just enough hours to flee Oslo by train with King Haakon VII — and to load 53 tons of gold bullion onto trucks barely ahead of the advancing German columns. The gold made it out to Britain via a harrowing overland and sea relay; the government reached London and kept functioning as a legitimate exile authority, which gave the Norwegian resistance something to fight for rather than just against. Also, Norway had an enormous (~1000 ships) merchant marine fleet, and the intact, legitimate Norwegian government in exile ordered it into the service of the Allies, right when Britain desperately needed ships and sailors to transport cargo to stay in the war.

The strategic hangover lasted five years. Hitler, rattled by the loss of the king and by ongoing Allied fake Scandinavian invasion plans, garrisoned Norway with somewhere around 300,000–400,000 troops for the rest of the war. This was a staggering commitment, sitting largely idle when they could have made a decisive impact elsewhere. One old colonel, one hunch, one order — which tied down a whole German army guarding a country it barely needed.

This is the sixth in a series of occasional blog posts on individual initiatives that made a strategic (not just tactical) difference in the course of the second world war, an event that gave us the world of the second half of the twentieth century. Here are previous posts:

WW II Key Initiatives 1: FDR Prodded the Navy to Convert Cruisers to Carriers, Just in Time

WW II Key Initiatives 2: “Thatch Weave” Tactic to Counter More-Agile Japanese Fighter Planes

WW II Key Initiatives 3: Kurt Tank Gives Germany a Superior Fighter Plane, the Focke-Wulf 190

WW II Key Initiatives 4: Building Hundreds of Small, Slow, But Cheap Ships to Counter the U-Boat Threat

WWII Key Initiatives 5: General Zhukov Helped Save USSR By Mastering the Pincers Counterattack

Boy Wonder Leopold Aschenbrenner Blows Up His $45 Billion Situational Awareness Hedge Fund

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Leopold Aschenbrenner is a very bright guy. Born in Germany to physician parents, he skipped enough grades to graduate from high school at age 15, allowing him to enroll at Columbia University in 2017 at that same age. He went on to graduate from Columbia at age 19 as valedictorian with a degree in economics and mathematics-statistics. An econ prof at the time said his “record of scholarship exceeds that of any student in the department in the previous 20 years.” The Mercatus Center, a think tank at George Mason University, recognized his potential, awarding him an Emergent Ventures grant.

Leopold Aschenbrenner, Columbia Class of 2021 Valedictorian

After graduation, Aschenbrenner moved into the effective-altruism research and grantmaking world, making notable contributions in various ways. In 2023, he joined the newly created “Superalignment” team at OpenAI, that was charged with making conceptual and engineering progress on aligning systems “much smarter than humans” before such systems were built. The next year he was discharged by OpenAI; the company said it was because of a security leak, but his version (which I find more credible) is that he was ousted as retaliation for circulating an internal memo arguing that the company’s protections against model-weight theft and algorithmic exfiltration were inadequate for AGI-relevant work.

Two months after his departure from OpenAI, he self-published Situational Awareness: The Decade Ahead, which you can download here.    This monograph synthesized scaling-law extrapolations, geopolitical analysis, and AI-lab security commentary into a single forecast: that the largest AI labs, on current trends, will plausibly reach AGI around 2027 and that an intelligence explosion to superintelligence could occur in the subsequent few years. This work went viral on Wall Street, and before you can say “monetization”, he was leading a hedge fund named, appropriately enough, Situational Awareness. There he put into practice his convictions that the demand for compute would be voracious, and would be limited by physical constraints such as electricity and chip fabs.

Thus, in his fund he went long companies like SanDisk (memory fab), Bloom Energy (makes solid oxide fuel cells), and Nebius (builds whole data centers). Very long, in fact, with leverage reportedly as high as 400%. He tried to hedge this long book by shorting software companies which are viewed as vulnerable to disruption by AI. This strategy worked fabulously for a while. His assets under management (AUM) climbed to $45 billion, with returns in the first 6-7 months of 2026 approaching 400%. Not bad for a 25-year-old.

But then, genius failed (yet again)- -in a stunning reversal, the market rebelled in late July against big AI capex spends, dumping memory fabs and infrastructure, and bought into the maligned software (SaaS) sector. So, BOTH his long and short legs went against him, followed in due course by the dreaded margin calls. Aschenbrenner’s $45 billion shrank to a measly $10 billion in a matter of days, as he was forced to sell off his public equities at a discount to those friendly capitalist sharks at Citadel. Wise old heads wagged, saying, yup, this sort of Black Swan event always happens sooner or later, and you then get carried out on a stretcher if you run a highly leveraged bet that is not truly hedged.

Down, but not out – – The word on the Street is that folks with money to invest are already lining up to entrust more bazillions to our altruistic wizard. We have not heard the last of Leopold Aschenbrenner.

Warsh’s Low/No Guidance Approach at Fed Makes Market Participants Nervous – – Which May Be a Good Thing

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Under Jerome Powell, a typical FOMC meeting had become almost a market event in itself. Traders didn’t just care about the rate decision. They dissected every word of the statement, every sentence of the press conference, and especially the “dot plot,” looking for clues about where rates might be six months or a year from now. The Fed wasn’t simply setting monetary policy—it was guiding expectations. Markets often moved as much on hints about future decisions as on the decision itself.

The first two FOMC meetings under Kevin Warsh have felt very different. The dot plots are gone. Forward guidance has largely disappeared. Instead of trying to signal the likely path of policy, Warsh has repeatedly stressed that the Fed will respond to incoming data when it arrives, not commit itself to forecasts that could prove wrong. At his latest press conference, he described avoiding forward guidance as “prudent” given current uncertainty, while reminding reporters that “There is no soft or alternative inflation target—only 2%.”

This is a huge change in communication style, which is having real world consequences.

Warsh long argued that forward guidance can box policymakers into decisions based on yesterday’s forecasts instead of tomorrow’s realities. That is, once their tentative plans had been put out in public, there was a psychological bias among Fed members to lock in on those projections, which would inhibit their ability to rationally interact with the most recent data and situation. So now, rather than telling markets what the Fed expects to do, he wants investors to make decisions based on fundamental economic conditions, knowing that the central bank will react only after the facts justify it. At the latest FOMC meeting he said, “Market participants are learning to play the ball, not the referee—and market prices will continue to respond in the direction and magnitude they see fit. This is, in my view, a change for the better—and we are just getting started.”

That approach chips away at what investors have come to call the “Fed Put”—the belief, built up since the 2008 financial crisis, that the central bank will fairly quickly and forcefully step in to support markets whenever things get rough.

If that belief fades, financiers may think twice before taking excessive risks. Leverage becomes more dangerous if there is less confidence that easier monetary policy will quickly arrive to cushion losses. Risk premiums may better reflect actual economic uncertainty rather than expectations of future Fed support. That is the possible good side of Warsh’s more hands-off approach. Ideally, business people will exercise more prudence on their own, lessening the odds of financial catastrophes that would require Fed intervention.

On the other hand, markets hate uncertainty, and less guidance means more volatility around Fed meetings. I think Powell tried to use sheer talking (jaw-boning) as a tool to influence market rates, lessening the need for the Fed to actually employ its blunt instruments there. Warsh seems to have taken that tool off the table.

Also, I think some (not all) the causation for the rise in 30-year Treasury bonds to twenty-year highs, and of home mortgage rates to one-year highs accrues to Warsh. First, by eliminating dot plots and forward guidance, he has increased uncertainty about the future path of policy. Investors can no longer confidently assume the Fed will ease at the first sign of economic weakness. That uncertainty can raise the term premium, pushing long-term yields higher.

Second, if markets believe the “Fed Put” is weaker, they may demand higher yields to hold long-term bonds because they perceive less protection from adverse economic or financial shocks. In other words, investors require more compensation for risk.

Whether today’s higher long-term rates are a healthy reflection of economic realities, or an unhealth drag on growth, is a matter of debate.

What Is So Special About Object-Oriented Programming Languages Like C++ and Python?

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I first learned computer programming about 1974, using FORTRAN running on an IBM 360 system that, yes, filled a whole room. And yes, my source code existed in the form of a stack of cards with holes punched in them, which got run through a physical card reader. FORTRAN and similar old-school languages were efficient (b/c computer resources were so constrained) and syntactically simple for solving well-specified problems.

C++ started to become popular in the 1980s, and Java in the 1990s. A big part of their appeal was that they were “object-oriented programming” (OOP) languages. I repeatedly asked my computer-programming professional friends back then to help me understand the difference between OOP and conventional Fortran type programs. They would get misty-eyed and rhapsodize about how their program components were modularized.  I guess I just failed to ask the right questions, because I never could understand why what they were talking about was so very much better or different than a good clean FORTRAN program, where most of the work was compartmentalized into well-defined functions and sub routines.

So I had a good talk with Claude about all this, and achieved enlightenment.. The differences seem to come down to a couple of key concepts:


(1) Data Compartmentalization

 In FORTRAN, you can modularize the data manipulation steps into subroutines, but the data tends to be more in common. Thus, for a very large programs, it is hard to keep some far-distant subroutine from accidentally altering your data. But with OOP, the data and the manipulation methods are “encapsulated” into one airtight thing, so no outside routine can mess with that data.

(2) More Robust Relations Among Chunks of Code

With OOP, there is also a feature called “inheritance”, where some new method can take advantage of an existing method, in a cleaner way than (in the FORTAN world) having a new subroutine call an existing subroutine, which would involve explicitly passing a bunch of parameters back-and-forth (which is very easy to mess up).

For doing fairly straightforward scientific calculations, even big ones, I think FORTRAN is still easier and more efficient. But for modern financial programs, involving millions of lines, written by huge teams of people that cannot all talk to one another, the win goes to OOP. Besides C++ and Java (still popular), in OOP we now have C# (standard for many Windows and gaming applications), and the crowd favorite, Python.


(That’s about it simply as I could put it, without getting long-winded and technical… If you want more details, you can always ask my buddy Claude)

Teamwork with “Tiki-Taka”: The Secret to Spain’s World Cup Successes

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I am not an attentive soccer (football) follower, but it was hard to ignore the 2026 FIFA World Cup being played in my own country (or at least my own continent). I tuned in for more and more games as the play progressed. Spain progressed further and further along, and finally won the gold.

This was not a one-off fluke. Spain won the World Cup in 2010, and has dominated European soccer for the past 3-4 years. Spain also won the most recent women’s World Cup in 2023. My inquiring mind naturally wanted to know what was behind this success. Spain’s population (49 million) is below that of England, France, or of Germany, so it is not simply having a huge demographic to pull from.

When this question was posted on Reddit, a Spanish fan gave this answer:

This is an interesting question with a lot of possible answers.

The most evident one is the Olympic Games of Barcelona 1992. This created a huge shockwave in Spain, which was still a little bit “isolated” from the rest of the world. The preparation of these games and their success made Spain invest a lot of money on sports infrastructure, trainers and programs, which directly impacted on the accomplishments of the big Spanish sportsmen of that generation: Rafa Nadal, Pau Gasol, Fernando Alonso, the whole Spanish football team 2008-2012 and many others. The success of the modern Spanish football team is supported by them.

But it also has a more deep and old reason. Before the 21st century the Spanish football team, even if they didnt win titles, managed to get good positions in international tournaments. Why? Spaniards have been, traditionally, smaller and weaker than the other Europeans (even if that has obviously changed). We couldn’t engage on physical play with the Dutch, Germans, French, English etc. that ran faster and were stronger than us. We don’t even have that many physical players TODAY. So, we had to rely on mentality and skill.

So the team started to become recognized by their fierce attitude,     their relentless energy and attacking spirit, gaining the nickname of “La furia Roja” (red fury). And also had to rely on skill, passes, building the game from behind and being more tactical and strategical on the field. This set the foundations for the modern “tiki-taka“, the Barça and Spanish playstyle that basically created modern football. These two things, plus Barcelona ’92 were the perfect mix to create a football tradition for many years.

Poking around the AI and Wikipedia and other sources, I found that this reply did a good job summarizing the sources of Spain’s success. I will just elaborate on a few aspects here.

Certainly, the Spanish technique of rapid, relentless passing (so-called tiki-taka) to maintain possession and to draw the opponent out of position to create scoring opportunities was in full view in this World Cup. It is more efficient and effective to rapidly move the ball than to run your players around.  The specifics of this style actually puzzled me when I watched it. In my brief, undistinguished non-career in intramural soccer, I was taught to receive a pass by controlling it with typically two taps, then maybe kick it onward with a third “touch”. But the Spanish players would take an incoming pass and just whack it onward with only one “touch.” That takes exquisite control; get that wrong by just a hair on one of those dozens and dozens of taps back and forth, and there goes your possession.

The internet agrees that Spanish teams historically could not compete head-to-head physically with a team full of hulking northern Europeans, and so they deliberately came up with this alternative playing style that relied on skillful control, not brute strength or size. Another strategic decision by Spanish coaches (trusting in their team’s ball control skills to maintain possession) is to play the defenders up-field, closer to the opponent’s goal, where they can play a role in building attacking pressure.

This playing style depends heavily on teamwork, and is the style promoted through all levels of the Spanish junior/farm teams as well. The whole team operates like a hive-mind organism, with players thinking several moves ahead and trusting the others to do the right thing at the right time. Unlike some other teams, this system is not dependent on one or two superstars to carry the load. I found the Spanish play to be an inspiring facet this year of “the beautiful game.”

How To Get Spare Car Fob Made Inexpensively (And Why It Is Wise to Do So)

Scott Buchanan2 Comments

When we recently traded in for a newer car, we were handed two fobs. These are push-button start cars, so you absolutely need a fob to drive one. An old-fashioned metal key will not work. Even the hidden metal key in your fob is only good for unlocking the door, not for starting the car. Since life has schooled me that things get lost, I asked how much it would cost to get a spare fob made at time of purchase, hoping I might catch a break.  Nope, the dealer cost would be $436. Ouch.

So, I later asked AI what to do. I was advised to have a non-dealer locksmith or hardware store do it instead. With this route, you might save even more money if you buy the blank fob yourself on Amazon for something like $30, and just have the locksmith program it and cut the hidden metal key.

A local ACE hardware store quoted me a price of $330, which is better than $436, but still not great. But I located a mobile locksmith, who drives a big van loaded with spare keys and fobs, and the equipment to cut key copies and to program fobs. He specializes in going and helping folks who are locked out of their cars, and he will drive to your house to make spare fobs and keys.   He would come to our house to make a new fob for $270. But it gets even better – – if I had two fobs made in one visit, the price would be only $220 apiece. It was an offer I could not refuse, so now I have spares for both cars.

If you don’t have a working fob for the locksmith to work from:  It turns out that for many foreign cars (Subaru, Toyota, and especially most European brands), it can be very difficult for a non-dealer locksmith to create a fob for you. Normally, if you find yourself locked out of your car some dark, snowy night, the mobile locksmith can roll up and create a new fob for you so you can get back in business. For brands like Ford, GM, Honda, and Nissan, he can accomplish this even if you can’t provide him with a working fob to copy. He will charge you twice as much, because it is a much harder job with no working template fob. It gets even harder for, say, Subaru. But for Mercedes-Benz or BMW, it may be impossible for most locksmiths to get a fob made on the spot without a working model to copy.  In a big metro area, there may be a few locksmiths who have the specialized equipment for these brands; you’d have to call around and ask the locksmith especially if they can make a new, say, Mercedes fob for an “all-keys-lost” situation. If not, you may have to get towed on a flat-bed ($$$) to a dealer, who can read your vehicle and program the fob ($$$). There may be a middle ground (which will take some days to play out) where you order a blank fob from a dealer along with some essential info for your locksmith to complete the programming, or where you order a programmed fob by mail.

All this argues for having a spare fob made ahead of time; maybe stash it somewhere that a friend or family member could bring it to you, if you were in distress not too far away.

Bonus fob tip: If your fob battery dies, you can still start your car by using the conventional metal key hidden inside your fob to open the car door, and then hold the fob very close to the car ignition button as you push the button, with your foot on the brake as usual.