From Macro to Micro | September 18, 2026

September 18, 2026

Technology Angst

I barely remember, but our family once had a game called Pong¹. The premise was super simple – hit the ball with the digital paddle as if in a game of ping pong. The graphics were practically nonexistent. The game objective was clear. Fast forward to Space Invaders and then to Nintendo and Super Mario Bros., then to PlayStation and Xbox, and the games got more engaging, had much better graphics, became more complex, and became more addictive.

Screenshot of Pong

We saw an evolution with cell phones and tablets. We saw massive numbers of users also join in social networks – Facebook, YouTube, Twitter, and Instagram. We witnessed some problems get solved – finding a friend across the world on Facebook after more than 30 years – and other problems get created – phone addiction, mental health struggles, decreased social interaction, etc. We still find solutions and we still find ourselves with additional challenges.

ChatGPT 3.5 came out on November 30, 2022, and it felt revolutionary². Looking back not quite four years later, the entire world has changed as a result of artificial intelligence (AI). We are now on ChatGPT-6 and there are many other models, both frontier and open free models³.

Some of the largest companies by market value are now either directly or indirectly related to AI hardware, AI software models, or data centers⁴. Trillions of dollars will likely be spent on software, hardware, infrastructure, and energy generation related to AI⁵.

The models have gotten much better, but also more complex. Many problems have been solved and new ones have emerged. This pattern is true for AI as it has been for other technologies⁶.

With all this change has come increased anxiety about job security, privacy, national security, and what the future will look like. It’s been a mixture of excitement about possibilities – the potential to cure diseases and solve complex problems, autonomous vehicles, brain implants that will allow the paralyzed to walk again, helper robots – but also much anxiety and angst.

The angst seemed to come to a new head in the last week. Several leaders of AI companies publicly stated that we need to slow the pace of AI development and deployment⁷. An  Anthropic researcher reportedly estimated that there was a greater than 10% chance that AI could see humans go extinct within the next decade⁸. At the same time, other leaders in government and at AI hardware companies said there’s no reason to slow things down^7,9.

There’s complexity to the issue and risks are not one-sided:

• Increased supervision and regulation could protect society, but it could also protect the profits of the leading frontier companies and stifle competition.
• A slowdown could allow the Chinese-backed AI models to catch up and be on a level playing field with the US models.
• A slowdown in innovation and development may encourage engineers to leave the US and move to where there is no measured pacing.
• Not slowing the development and deployment could mean loss of control of AI agents and models that find ways around rules.
• Not slowing down could introduce newer cyber threats that consumers, businesses, and local, state, and federal governments are not prepared to handle.
• Not slowing the pace might mean that we run out of energy capacity to support the innovative growth.

These are all possibilities. Nothing is certain. There’s no easy answer. And that’s okay. Our country is endowed with a number of extremely talented individuals from all walks of life. Some are in government. Some in business. Some are engineers. Others are in finance or economics. Others in math. Many are working in technology to solve problems we may not even know about. As we have always done, we will work the problem or problems and try to come up with solutions. We will likely make mistakes along the way, but we will most likely figure it all out. It will take time. We have figured it out before and we will likely do the same here again. We found a way to go to the Moon when it seemed impossible. We found ways to inoculate ourselves from harmful diseases. We found a way to make cars drive themselves. We found a way to make computers model things and reason and help solve problems. We figured out how to get qubits to behave a certain way and advance quantum computing.

I am confident that we will use some of these new tools to also figure out the best ways to reduce the risks or protect against them. That may mean hardening cybersecurity and building more rules for models or model agents. Or it may mean coming out with better and safer designs that enhance both innovation and safety and privacy and national security.

As someone who uses some of the most advanced AI models on a regular basis, I am confident that we will work through many of these challenges and develop better solutions. It won’t happen overnight and it may be messy, but I believe we will figure it out.

Footnotes

1. Pong. The Strong National Museum of Play describes Atari’s 1972 arcade game and the home version introduced in 1975. https://www.museumofplay.org/games/pong/
2. ChatGPT launch. OpenAI introduced ChatGPT on November 30, 2022, based on a model in the GPT-3.5 series.
3. Model terminology. OpenAI identifies GPT-6 Astra as a model; ChatGPT is the product. Its gpt-oss release provides an example of open-weight models distributed under Apache 2.0. Open weights do not make hardware, electricity, hosting, or every use free.
4. Market value and AI exposure. Per Bloomberg data as of 9/16/26, NVIDIA, Apple, Microsoft, Amazon.com, Alphabet, Broadcom, Meta Platforms, Micron Technology, Tesla, Advanced Micro Devices, and Intel are in the top 20 companies in the S&P 500 by market cap and represent 40.07% of the index.
5. Investment scale. McKinsey projects $5.2 trillion in AI-related data-center capital requirements through 2030 under its central scenario, including equipment, infrastructure, and power. This is projected investment needed, not confirmed spending already incurred, and does not substantiate the draft’s full software-spending scope. Separately, the IEA reports approximately $500 billion of global data-center investment in 2024, including non-AI uses. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers https://www.iea.org/reports/energy-and-ai/executive-summary
6. Benefits and risks. NIST’s AI Risk Management Framework addresses risks to individuals, organizations, and society. https://www.nist.gov/itl/ai-risk-management-framework. Electricity solved problems – reduced darkness, improved safety, enabled long-distance communications – but created other challenges such as environmental damage, safety hazards, and power dependency. Automobiles solved urban pollution, increased travel speed, and connected rural communities but increased traffic fatalities, created air pollution, and led to massive traffic jams.
7. AI pacing debate. Amodei, Altman, and Musk called for a slowdown; Huang opposed deceleration. Trump called it a “hoax.” Source: Danielle Abril, September 15, 2026. https://www.theguardian.com/technology/2026/sep/15/anthropic-nvidia-ceos-ai
8. Extinction estimate. September 10 reporting attributes to Anthropic researcher Evan Hubinger a personal estimate of greater than 10% within the next decade. This is a personal, subjective judgment about future AI, not an established statistical probability or consensus. This citation is secondary reporting of an interview. https://nypost.com/2026/09/10/tech/anthropic-researcher-jacob-coxon-who-quit-ai-role-over-fears-of-racing-into-extinction-says-people-are-begging-for-regulation/
9. Government opposition to new regulation. Reuters reports David Sacks’s opposition to additional AI regulation on September 16, 2026, while supporting developer responsibility for product safety. Opposition to regulation should not be equated with a claim that safeguards are unnecessary. For the hardware-company position, see note 7. https://www.reuters.com/legal/litigation/trump-adviser-sacks-says-ai-fears-driven-by-fear-mongering-playbook-2026-09-16/

Definitions

Artificial intelligence (AI): Machine-based systems that infer from inputs how to generate outputs such as predictions, content, recommendations, or decisions.

AI model: A computational system trained on data to perform tasks such as generating text, recognizing patterns, or making predictions.

Frontier AI model: A model near the leading edge of current capabilities. There is no single universally accepted threshold.

Open-weight model: A model whose trained numerical parameters are available for download under a license. This does not necessarily include training data or all development code, or imply unrestricted or cost-free use.

AI agent: A system that uses an AI model and tools to take actions toward a goal, with varying levels of autonomy and human oversight.

Data center: A facility housing computing, storage, and networking equipment, together with power and cooling systems.

Market capitalization: The market value of a public company’s outstanding shares, generally calculated as share price multiplied by shares outstanding.

Cybersecurity: Measures used to protect systems, networks, and information from unauthorized access, disruption, or damage.

AI development and deployment: Development involves building, training, and testing systems; deployment makes them available for real-world use. Safeguards can apply differently to each stage.

S&P 500: A stock market index tracking the performance of 500 of the largest publicly traded companies in the United States. It serves as a primary benchmark for the overall health of the U.S. stock market.

Operating EPS: A company’s net profit from regular business operations divided by its outstanding shares, excluding one-time gains or losses. It shows how much profit a company generates from its core everyday business.

NBER Recession: A significant decline in economic activity spread across the economy, lasting more than a few months, as officially designated by the National Bureau of Economic Research. It is determined by analyzing factors like gross domestic product, income and employment.

Correlation Coefficient: A standardized numerical measure, ranging from -1 to +1, that quantifies the strength and direction of a linear relationship between two variables. A value close to zero indicates no linear relationship, while values near the extremes indicate strong positive or negative relationships.

Standard Deviation: A statistical measure that quantifies how much the data points in a set vary or spread out from their average (mean). A low standard deviation means the data points are closely clustered around the average, while a high standard deviation indicates that the data are spread across a wider range of values.

Z-Score Transformation: A statistical process that converts a raw data point into a standardized score by subtracting the dataset mean and dividing by the standard deviation. This transformation indicates how many standard deviations a specific data point lies above or below the average.

Diffusion Index: An economic or survey metric that measures the proportion of components within a group that are experiencing positive growth or expansion over a given period. It typically oscillates around a baseline of 50%, where values above 50% indicate widespread expansion and values below indicate contraction.

Disclosures

The views expressed are the author’s opinions as of September 16, 2026, and may change without notice. This material is for general informational and educational purposes and is not individualized investment advice or a recommendation to buy or sell any security or adopt any investment strategy.

Statements about future technological capabilities, spending, regulation, economic effects, and risk mitigation are forward-looking and uncertain. Actual outcomes may differ materially. Third-party forecasts and subjective probability estimates are not guarantees and do not necessarily represent the views of the author or the firm. Sources believed to be reliable have been used, but their accuracy and completeness are not guaranteed.

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In general, stock values fluctuate, sometimes widely, in response to activities specific to the companies as well as broad market, economic and political conditions. Stock investing involves risks, including fluctuating prices and loss of principal. Value investments can perform differently from the market as a whole. They can remain undervalued by the market for long periods of time. (135-LPL) International investing involves special risks such as currency fluctuation and political instability and may not be suitable for all investors. These risks are often heightened for investments in emerging markets. (93-LPL)

The fast price swings in commodities will result in significant volatility in an investor’s holdings. Commodities include increased risks, such as political, economic, and currency instability, and may not be suitable for all investors. (122-LPL)

Rebalancing a portfolio may cause investors to incur tax liabilities and/or transaction costs and does not assure a profit or protect against a loss. (28-LPL)

There is no guarantee that a diversified portfolio will enhance overall returns or outperform a non-diversified portfolio. Diversification does not protect against market risk. (26-LPL)

Standard deviation is a historical measure of the variability of returns relative to the average annual return. If a portfolio has a high standard deviation, its returns have been volatile. A low standard deviation indicates returns have been less volatile. (131-LPL)

This is for educational / general purposes only, does not constitute investment, tax or legal advice and should not be relied on as such. This is not to be construed as an offer to buy or sell any financial instruments. Any strategies discussed are not intended to be relied upon as the sole factor in making an investment decision for any individual. As with all investments there are associated inherent risks. Please obtain and review all financial material carefully before investing. All material presented is compiled from sources believed to be reliable and current, but accuracy cannot be guaranteed. The opinions voiced in this material are for general information only and are not intended to provide specific advice or recommendations for any individual. All performance referenced is historical and is no guarantee of future results. All indices are unmanaged and may not be invested in directly. These comments should not be construed as recommendations but as an illustration of broader themes.

Forward-looking statements are not guarantees of future results. They involve risks, uncertainties and assumptions; there can be no assurance that actual results will not differ materially from expectations. In addition, forward-looking statements, including index targets or market scenarios, are hypothetical in nature, reflect current views and assumptions and are subject to change based on market and economic conditions and are not guarantees of future performance. This is a hypothetical example and is not representative of any specific investment. Your results may vary. (88-LPL) Scenario outcomes are illustrative and not predictive. This does not constitute a recommendation of any investment strategy or product for a particular investor. Investors should consult a financial professional before making any investment decisions.

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Publication Date: September 18, 202

For Public Use in the US

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