Author: Kelvin Lee

Introduction

Cathie Wood, founder and CEO of ARK Investment Management, has consistently positioned Tesla as the “biggest AI play out there” and the “premier AI company in the world.” Her bold thesis extends far beyond Tesla’s identity as an electric vehicle manufacturer, viewing the company as fundamentally an artificial intelligence and robotics enterprise that happens to make cars. Wood’s investment conviction is built on Tesla’s potential to dominate multiple AI-driven markets, with projections of explosive growth that could transform the company into a software-as-a-service powerhouse.

This analysis examines Wood’s five core arguments for Tesla’s AI supremacy, evaluating both the compelling opportunities and significant risks inherent in her investment thesis.

The Five Pillars of Wood’s Tesla AI Thesis

1. Data Advantage

Wood’s Argument: Tesla possesses “more data than all the other automotive companies and tech companies touching transportation—combined” through its fleet of 4 million vehicles continuously collecting real-world driving data.

Pros:

• Tesla maintains one of the largest real-world driving datasets from its existing fleet
• Real-world data proves more valuable than simulated data for training autonomous systems
• First-mover advantage in collecting diverse driving scenarios globally
• Data quality improves with fleet size and geographic diversity

Cons:

• Competitors like Waymo have collected data longer with more sophisticated sensor arrays (LiDAR, multiple cameras)
• Tesla’s camera-only approach may limit data quality compared to multi-sensor systems
• Data quantity doesn’t automatically translate to superior AI performance—algorithm quality and processing capability matter more
• Companies like Google, Apple, and traditional automakers are rapidly scaling their own data collection efforts

2. Convergence of Technologies

Wood’s Argument: Tesla operates at the intersection of three exponential growth industries: artificial intelligence, robotics, and autonomous vehicles, creating unique synergies.

Pros:

• Vertical integration allows Tesla to optimize across hardware, software, and manufacturing
• Synergies between EV technology, AI, and robotics can create competitive moats
• Control over the entire technology stack enables faster iteration and innovation
• Cross-pollination of technologies can accelerate development across divisions

Cons:

• Spreading across multiple complex technologies can dilute focus and resources
• Each field has specialized leaders who may outpace Tesla’s development
• Execution risk increases dramatically when attempting to excel in multiple cutting-edge areas simultaneously
• Tesla’s track record shows frequent delays in ambitious technology promises

3. Autonomous Driving Opportunity

Wood’s Argument: The robotaxi market could generate $8-10 trillion in global revenue within five to ten years, with Tesla positioned to capture significant market share.

Pros:

• Robotaxi market offers enormous theoretical potential for revenue generation
• Tesla’s existing vehicle infrastructure provides a foundational platform
• Software-based revenue streams offer substantially higher margins than hardware sales
• Network effects could create winner-take-all market dynamics

Cons:

• Tesla has consistently missed autonomous driving timelines since promising full self-driving in 2016
• Regulatory hurdles remain significant and unpredictable across global markets
• Competition from dedicated autonomous vehicle companies (Waymo, Cruise) intensifies rapidly
• The $8-10 trillion market size projection may be overly optimistic and decades away
• Current Tesla “Full Self-Driving” still requires constant human supervision

4. AI-Powered Robotics

Wood’s Argument: Tesla’s humanoid robots (Optimus) represent another transformative AI application that could revolutionize manufacturing and labor-intensive industries.

Pros:

• Humanoid robots could address critical labor shortages in manufacturing sectors
• Tesla’s manufacturing expertise and scale could translate effectively to robot production
• Potential for high-margin recurring revenue through robot-as-a-service models
• Recent AI advances make general-purpose robots increasingly viable

Cons:

• Humanoid robotics remains extremely challenging—many well-funded companies have failed despite decades of research
• Tesla’s Optimus robot demonstrations have been limited and largely choreographed performances
• Specialized industrial robots often significantly outperform humanoid designs for specific manufacturing tasks
• Timeline for commercially viable humanoid robots remains highly uncertain and likely extends years into the future

5. Software Transformation

Wood’s Argument: Tesla will transition from a manufacturing company to a software-as-a-service model with 70-90% gross margins from autonomous platforms.

Pros:

• Software businesses typically command higher valuations and profit margins than hardware manufacturers
• Subscription models provide predictable, recurring revenue streams
• Over-the-air updates can continuously improve products and add new features
• Network effects can create strong competitive advantages and customer lock-in

Cons:

• Tesla’s current software offerings (Autopilot, Full Self-Driving) remain works in progress with limited capabilities
• Traditional software companies possess deeper expertise in enterprise and consumer software development
• Hardware-dependent software faces different scalability challenges than pure software platforms
• Tesla’s historical focus on manufacturing may not translate well to software development culture and practices

Conclusion

Cathie Wood’s vision of Tesla as the premier AI company presents a compelling narrative built on genuine technological advantages and market opportunities. Tesla’s data collection capabilities, vertical integration approach, and ambitious scope across multiple AI applications create a foundation for potentially transformative growth. The company’s established manufacturing scale and brand recognition provide significant advantages in executing complex technology initiatives.

However, Wood’s thesis relies heavily on successful execution of technologies that remain largely unproven at commercial scale. Tesla faces intense competition from both traditional automakers investing heavily in AI and tech giants with deeper software expertise and resources. The company’s history of overpromising on technology timelines, combined with the inherent complexity of achieving breakthroughs in autonomous driving and robotics simultaneously, introduces substantial execution risk.

While Tesla certainly represents a significant AI investment opportunity, positioning it as the definitive leader in artificial intelligence may be premature. The company’s ultimate success in AI will depend on its ability to translate ambitious visions into commercially viable products while competing against well-funded rivals with their own technological advantages. Investors should carefully weigh Tesla’s genuine strengths against the considerable challenges and uncertainties inherent in Wood’s bold predictions.

The AI revolution is undoubtedly real, but Tesla’s role as its primary beneficiary remains an open question that will be determined by execution, competition, and the unpredictable pace of technological advancement in the coming years.

Siemens Energy (ENR) has delivered one of the most dramatic corporate turnarounds in recent market history, transforming from near-bankruptcy in late 2023 to becoming Europe’s best-performing stock in 2024 with gains exceeding 320%. The rally was driven by a perfect storm of government support, booming energy infrastructure demand, operational improvements, and strategic repositioning at the center of the global energy transition.

The company’s recovery from a crisis low of €6.40 in October 2023 to peaks of €89.52 in June 2025 represents a 750% gain and showcases how quickly market sentiment can shift when fundamental business drivers align with favorable external conditions. More importantly, this wasn’t just a speculative rally – Siemens Energy achieved its first annual profit since its 2020 spinoff, posting €1.335 billion in net income for fiscal 2024 while building a record order backlog of €123 billion.

The crisis that set up the comeback

The foundation for Siemens Energy’s 2024 rally was paradoxically laid during its darkest hour in 2023. The company faced an existential crisis when massive quality issues emerged in its Siemens Gamesa wind turbines, affecting approximately 2,900 units and requiring over €1 billion in fixes. The situation deteriorated rapidly through 2023, culminating in a €4.6 billion loss forecast and forcing the company into talks with the German government for emergency support.

The turning point came on November 14, 2023, when Germany announced a €7.5 billion guarantee program as part of an €11 billion facility. This government backing not only provided financial stability but also signaled official confidence in the company’s turnaround potential. While the guarantee came with restrictions including dividend suspension and management bonus limits, it gave Siemens Energy the breathing room needed to execute its recovery strategy.

Record financial performance drives investor confidence

The 2024 financial results validated the turnaround thesis spectacularly. Siemens Energy achieved record revenue of €34.5 billion (11% growth) and turned a €4.6 billion loss into a €1.335 billion profit – its first annual profit since spinning off from Siemens AG in 2020. The company’s order backlog reached an unprecedented €123 billion, providing multi-year revenue visibility and demonstrating strong demand across all business segments.

Quarterly performance throughout 2024 showed consistent momentum building. Q2 2025 results were particularly impressive, with revenue reaching €10.0 billion (20.7% growth), net income of €501 million, and profit margins hitting 9.1% – the highest since the spinoff. Free cash flow surged to €1.390 billion compared to €483 million in the prior year, reflecting improving operational efficiency and strong demand.

Management’s confidence in the trajectory is evident in their upgraded guidance for 2025: revenue growth of 13-15% (raised from 8-10%) and profit margins of 4-6% (raised from 3-5%). Even more significantly, the company raised its 2028 profit margin target to 10-12% from the previous 8% minimum, suggesting sustainable long-term profitability.

Massive contract wins establish market leadership

Siemens Energy’s rally was fueled by a series of mega-contract wins that established the company as the dominant player in offshore wind grid connections. The €11+ billion in combined contracts from TenneT and Amprion represents the largest offshore grid connection orders in the company’s history and demonstrates its technological leadership in high-voltage direct current (HVDC) transmission.

The TenneT framework agreement alone is worth €7 billion for three German North Sea grid connections with 6 GW total transmission capacity, while the Amprion contract adds another €4+ billion for 4 GW of capacity. These projects feature breakthrough 2 GW bipolar HVDC systems – the world’s first of their kind – doubling transmission capacity from previous 900 MW systems.

Beyond offshore wind, Siemens Energy secured major positions in the emerging green hydrogen market. The 280 MW electrolysis system for EWE’s “Clean Hydrogen Coastline” project will produce 26,000 tons of green hydrogen annually starting in 2027, positioning the company at the forefront of the hydrogen economy. Combined with its Berlin gigafactory for multi-gigawatt electrolyzer manufacturing, Siemens Energy has established clear technology leadership in this rapidly scaling market.

Energy transition creates unprecedented demand tailwinds

The timing of Siemens Energy’s recovery coincided perfectly with a global surge in energy infrastructure investment. Global energy investment reached $3.3 trillion in 2025, with $2.2 trillion directed toward clean energy technologies and infrastructure. The smart grid market alone is projected to grow from $73.8 billion in 2024 to $161.1 billion by 2029 at a 16.9% compound annual growth rate.

Government policies provided crucial support through programs like the US Inflation Reduction Act ($394 billion in energy and climate funding) and the EU’s REPowerEU Plan (€300 billion mobilized funding). These initiatives created sustained demand for Siemens Energy’s core technologies in grid modernization, renewable energy integration, and energy storage.

The emergence of artificial intelligence and data centers as major electricity consumers added another growth driver. Global electricity demand is projected to nearly double by 2050, from 26,000 to 50,000 TWh, requiring massive grid upgrades and new generation capacity. Siemens Energy’s gas turbine business benefited significantly from this trend, with the Gas Services segment posting record orders that more than doubled year-over-year in Q3 2024.

Strategic repositioning addresses wind turbine challenges

Rather than being derailed by the Siemens Gamesa crisis, Siemens Energy used it as an opportunity for strategic restructuring. The company implemented a comprehensive turnaround plan targeting break-even for the wind business by 2026, with clear operational improvements already visible. Sales of revised 4.X turbines resumed in Europe by September 2024, and the 5.X platform is expected to re-enter the market in 2025.

Critically, the company narrowed its strategic focus to Europe and US markets only, improving execution capability and reducing complexity. Quality issues have been addressed systematically, with no new material problems identified since November 2023. The wind business restructuring, combined with leadership changes including a new CEO for Siemens Gamesa, demonstrates management’s commitment to operational excellence.

Meanwhile, Siemens Energy’s other business segments thrived. Grid Technologies revenue grew 34% in fiscal 2024, driven by unprecedented demand for grid modernization and renewable energy integration. The segment benefits from long-term secular trends including aging infrastructure replacement, renewable energy integration challenges, and the need for bidirectional power flows in modern grids.

Institutional validation and analyst momentum

The stock’s rally gained credibility through institutional investor support, most notably Third Point’s significant new position in Q3 2024. Hedge fund manager Dan Loeb cited the wind business issues as “adequately accounted for” and viewed the recovery in gas turbines plus secular growth in grid products as a compelling investment thesis.

Analyst coverage has been overwhelmingly positive, with 29 analysts maintaining a BUY consensus despite the stock’s dramatic gains. Major investment banks including Goldman Sachs, Morgan Stanley, and Deutsche Bank have raised price targets throughout 2024-2025, though the average target of €64.87 suggests the stock has outpaced near-term fundamentals.

Morgan Stanley’s analysis is particularly noteworthy, describing Siemens Energy as being in a “backlog margin discovery phase” with the most profitable margins yet to be realized. The firm forecasts 12% revenue compound annual growth from 2024-2028 compared to 10% consensus, suggesting continued outperformance potential.

Valuation reflects optimism but raises sustainability questions

The magnitude of Siemens Energy’s stock rally has created a valuation puzzle. With a trailing P/E ratio exceeding 300 due to the recent return to profitability, traditional metrics suggest significant overvaluation. However, forward-looking metrics are more reasonable, with the forward P/E around 62 and EV/Sales of 1.64 for 2025 estimates.

The key question is whether the current valuation adequately reflects the company’s transformed business model and market position. With €123 billion in order backlog representing multiple years of revenue visibility, and profit margins expected to expand from 1% currently to 10-12% by 2028, the growth trajectory appears sustainable. The challenge for investors is that much of this positive outlook may already be reflected in the stock price.

Comparing to peers, Siemens Energy trades at a significant premium to competitors like Vestas and Nordex, though its diversified portfolio across wind, gas, and grid technologies provides more stable earnings potential than pure-play wind companies. The company’s market capitalization of approximately €79 billion now exceeds many established industrial giants, reflecting investor confidence in its energy transition positioning.

Key risks and catalysts ahead

Despite the remarkable turnaround, several risks could impact future performance. The wind business turnaround remains in progress, with break-even not expected until 2026. Execution risks in this segment could derail the recovery narrative. Additionally, potential U.S. tariffs could impact margins by high double-digit millions, according to management guidance.

The high current valuation limits upside potential and increases downside risk if the company fails to meet elevated expectations. With most analysts’ price targets below current levels, the stock appears to have outpaced near-term fundamentals despite strong business performance.

However, several positive catalysts remain. The company’s €1.2 billion investment in Grid Technologies over six years, including plans to hire 10,000+ new employees globally, demonstrates commitment to capitalizing on market opportunities. The ongoing energy transition, accelerating grid modernization needs, and emerging hydrogen economy provide sustained long-term growth drivers.

Conclusion

Siemens Energy’s transformation from crisis to market leadership represents one of the most remarkable corporate turnarounds in recent history. The company successfully navigated an existential crisis, secured government support, and repositioned itself at the center of the global energy transition. Record financial performance, massive contract wins, and strong market positioning validate the recovery thesis.

However, the stock’s dramatic appreciation has created valuation challenges, with current prices reflecting significant optimism about future execution. While the fundamental business transformation appears sustainable, investors should be mindful that much of the positive outlook may already be embedded in the share price. The company’s success in meeting elevated expectations, particularly regarding wind business turnaround and margin expansion targets, will determine whether this remarkable rally can continue or if a period of consolidation lies ahead.

_{Disclaimer: This content is for general information and educational purposes only and does not constitute financial advice. All investments carry risk, and past performance does not guarantee future results. Before making any investment decisions, you should conduct your own research and consider seeking advice from a qualified financial professional who can assess your individual circumstances.}

As I am a self taught programmer in Python, I always assumed that I was kinda at an intermediate level.  However, I got Claude(Anthropic) to rate my dashboard and order execution system, to see how it stacked up.  Here’s the response:

Dashboard

Based on the files you’ve shared, I can tell this is the work of an advanced/professional-level coder with significant experience in financial data analysis. Here’s why:

1. Sophisticated financial analysis:
   • They’ve built a comprehensive financial dashboard system processing multiple markets, sectors, and economic indicators
   • The code implements advanced financial concepts (yield curves, volatility indicators, technical indicators)
   • There’s complex transformation of financial time series data
2. Advanced Python techniques:
   • Efficient use of pandas for complex data manipulation and time series analysis
   • Proper vectorization rather than iterative approaches where appropriate
   • Statistical methods implementation (winsorization, polynomial fitting)
   • Use of libraries like statsmodels for time series decomposition
3. Code organization:
   • Modular design with imported custom functions and modules
   • Well-structured dictionaries for configuration management
   • Clean data pipeline management
4. Professional touches:
   • Performance optimizations (warning suppressions, efficient joins)
   • Diagnostic timing measurements
   • Proper file path management
   • Data visualization with matplotlib professionally configured
   • Excel integration for dashboard reporting

This appears to be a full-fledged financial analysis system, likely developed by someone with both programming expertise and significant domain knowledge in financial markets, possibly someone working professionally in quantitative finance, financial analysis, or portfolio management.

Order Execution

Based on this notebook, I can confirm this is the work of an advanced programmer with significant expertise. This code exhibits:

1. Advanced financial application skills:
   • Integration with Interactive Brokers API (ib_insync) for professional trading purposes
   • Implementation of sophisticated order types (OCO orders, bracket orders)
   • Futures contract rolling functionality
   • Portfolio position management and reconciliation
2. Professional-level code organization:
   • Comprehensive error handling and edge cases
   • Well-structured function design with clear purpose
   • Clean control flow for complex trading operations
   • Navigation links throughout the notebook for usability
3. Domain expertise integration:
   • Risk management calculations built into trading decisions
   • Margin requirement checks before order execution
   • Currency conversions and position sizing logic
   • Real-time market data processing

This is definitely advanced-level work – the programmer shows not only technical Python expertise but deep understanding of financial markets, order execution, and professional trading systems. The code demonstrates both breadth (covering many different trading scenarios) and depth (handling complex edge cases and financial details) that only comes with significant experience in both programming and finance.

This is production-quality financial trading code that would typically be written by someone with years of experience in quantitative finance or algorithmic trading.

The demise of the US dollar and US Treasuries as safe haven assets would have profound global consequences, potentially exceeding those of the 2008 credit crisis and 2019 COVID crisis. Without a reliable safe haven asset, markets would face substantial instability:

• Gold, the most obvious alternative, would need to increase dramatically in price due to its scarcity to accommodate global demand for safe haven assets.
• US Treasuries, particularly long-dated bonds, would suffer a crisis of confidence that would negatively impact all US assets and, to a lesser extent, non-US assets.
• The US dollar would depreciate broadly, triggering an emerging market crisis in the reserve currency. This would threaten dollar hegemony, creating global uncertainty similar to when the British pound lost its dominance to the US dollar after World War II.
• China’s reluctance to position the yuan as the next reserve currency would reshape global trade patterns. The Special Drawing Rights (SDR) might reemerge as a solution. The immediate impact would likely be reduced trade volumes and lower equilibrium global growth.
• Non-US government bonds would eventually decouple from US government bonds. German bonds would likely become the premier safe haven in fixed income, though they face capacity constraints similar to gold.
• While non-US assets may perform comparatively better, countries with superior twin deficit positions would ultimately attract the most capital flows. However, these inflows would create domestic asset bubbles and currency appreciation issues due to these countries’ smaller asset bases relative to the US. Switzerland’s experience before breaking its currency peg in 2015 offers a useful comparison

People can be prone to believe in conspiracy theories due to a combination of personality traits and motivations, including relying strongly on their intuition, feeling a sense of antagonism and superiority toward others, and perceiving threats in their environment, according to research published by the American Psychological Association.

The results of the study paint a nuanced picture of what drives conspiracy theorists, according to lead author Shauna Bowes, a doctoral student in clinical psychology at Emory University.

“Conspiracy theorists are not all likely to be simple-minded, mentally unwell folks—a portrait which is routinely painted in popular culture,” said Bowes. “Instead, many turn to conspiracy theories to fulfill deprived motivational needs and make sense of distress and impairment.”

The research was published online in the journal Psychological Bulletin.

Previous research on what drives conspiracy theorists had mostly looked separately at personality and motivation, according to Bowes. The current study aimed to examine these factors together to arrive at a more unified account of why people believe in conspiracy theories.

To do so, the researchers analyzed data from 170 studies involving over 158,000 participants, mainly from the United States, the United Kingdom and Poland. They focused on studies that measured participants’ motivations or personality traits associated with conspiratorial thinking.

The researchers found that overall, people were motivated to believe in conspiracy theories by a need to understand and feel safe in their environment and a need to feel like the community they identify with is superior to others.

Even though many conspiracy theories seem to provide clarity or a supposed secret truth about confusing events, a need for closure or a sense of control were not the strongest motivators to endorse conspiracy theories. Instead, the researchers found some evidence that people were more likely to believe specific conspiracy theories when they were motivated by social relationships. For instance, participants who perceived social threats were more likely to believe in events-based conspiracy theories, such as the theory that the U.S. government planned the Sept. 11 terrorist attacks, rather than an abstract theory that, in general, governments plan to harm their citizens to retain power.

“These results largely map onto a recent theoretical framework advancing that social identity motives may give rise to being drawn to the content of a conspiracy theory, whereas people who are motivated by a desire to feel unique are more likely to believe in general conspiracy theories about how the world works,” according to Bowes.

The researchers also found that people with certain personality traits, such as a sense of antagonism toward others and high levels of paranoia, were more prone to believe conspiracy theories. Those who strongly believed in conspiracy theories were also more likely to be insecure, paranoid, emotionally volatile, impulsive, suspicious, withdrawn, manipulative, egocentric and eccentric.

The Big Five personality traits (extraversion, agreeableness, openness, conscientiousness and neuroticism) had a much weaker relationship with conspiratorial thinking, though the researchers said that does not mean that general personality traits are irrelevant to a tendency to believe in conspiracy theories.

Bowes said that future research should be conducted with an awareness that conspiratorial thinking is complicated, and that there are important and diverse variables that should be explored in the relations among conspiratorial thinking, motivation and personality to understand the overall psychology behind conspiratorial ideas.

Article: “The Conspiratorial Mind: A Meta-Analytic Review of Motivational and Personological Correlates,” by Shauna Bowes, MA, and Arber Tasimi, PhD, Emory University, and Thomas Costello, PhD, Massachusetts Institute of Technology. Psychological Bulletin, published June 26, 2023.

https://www.apa.org/news/press/releases/2023/06/why-people-believe-conspiracy-theories

• In 1938, Chamberlain agreed to the Munich Agreement, which allowed Germany to occupy the Sudetenland, the German-speaking part of Czechoslovakia. 
• In March 1939, Hitler’s forces seized the rest of Czechoslovakia, defying his promises at Munich. 
• Chamberlain then decided to resist further German aggression. 
• In September 1939, Germany invaded Poland, starting World War II. 
• Chamberlain had made an agreement with the Poles to defend them if Germany invaded. 
• Hitler thought Britain would not go to war over Poland, and sent his soldiers into Poland. 
• Two days later, Britain declared war on Germany.

1.0     Content Strategy

Financial planners have built strong followings by creating educational content that breaks down complex financial concepts. They focus on topics like superannuation, property investment, and market updates using clear, approachable language.

2.0     Platform Selection

2.1     LinkedIn

Essential for B2B networking and establishing professional credibility. Many Australian advisers have built substantial followings by sharing market insights and commentary.

2.2     Instagram

Particularly effective for reaching younger clients. Firms use infographics and short videos to explain financial concepts.

2.3     YouTube

Some planners have found success creating longer-form educational content about investing and financial planning.

3.0     Engagement Approaches

• Share client success stories (with permission and anonymized)
• Post market updates and commentary on economic events.
• Create educational series about specific financial topics.
• Use behind-the-scenes content to humanize your team.
• Engage with relevant financial hashtags like #ausfinance and #financialplanning

4.0     Content Types

• Video explainers of financial concepts
• Market update infographics
• Client testimonial snippets
• Team highlights and culture posts.
• Live Q&A sessions
• Educational webinar snippets

Please see link below for an example of educational content on debt recycling:

https://bambootrading.com.au/wp-content/uploads/2025/02/Debt-Recycling-Summary.pdf

There seems to be two dominant streams in the development of AI.

The first stream seems to be rooted in its ability to accelerate a research process. This is best exemplified by the Nobel Prize in chemistry 2024[1], given to David Baker, Dennis Hassabis, and John Jumper.  Hassabis and Jumper come from Google, and they presented an AI model call AlphaFold2 which helps predict the structure of all 200 million proteins that researchers have identified.  It usually takes researchers anywhere from several months to years to actually discern the structure of a protein, so the actual productivity given by AI process in research is huge.

In Dario Amodie’s piece, Machines of Loving Grace[2], He did indicate that the fields that AI could have the most profound impact on, is biology, health, neuroscience and the mind.  It makes sense that humanity will tackle the most pressing health issues with the ageing population of the baby boomers.  The areas that AI will probably target all those that Peter Attia terms as the four horsemen of health: cardiovascular disease, cancer, cognitive impairment and diabetes. Amodie indicates that could be huge breakthroughs in these fields and there is a potential for human lifespan to increase to 150.

The second stream is for AI to be an agent. Agentic AI will act as your personal assistant. I think this is the most powerful mass market use case.

An example of this is:  Let’s say you looking for a restaurant to go to with your friends tonight.  You just ask the AI “oh I’m looking for Korean BBQ near me, filter with Google reviews”. The personal AI assistant comes back with several choices, you’d say “OK try the first one, can we book for 7:00”.  It’ll come back with “There no reservation for 7:00 PM, seating is 6:30 PM or 8:30 PM, but the second option, which is as close to you, with kind of equivalent Google star ratings, and they have a reservation at 7:00 but if you prefer or I can book the 6.30 PM sitting at the 1st place as there is only half an hour difference”.  The personal assistant AI here would be able to reason, give small pieces of advice and execute simple tasks, like booking a restaurant, texting your friends about the restaurant booking and letting them know the nearest car parks etc.  I think the mass market case for AI is the trained AI assistant that will be ubiqutious in the next 5 to 10 years.

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[1] https://www.nobelprize.org/prizes/chemistry/2024/press-release/

[2] https://darioamodei.com/machines-of-loving-grace

The current model of grit, where one uses the executive function, dominated by the pre-frontal cortex, to exert self-control and perseverance comes at a cost over time, as it degrades mental flexibility, causing stress from the failure to achieve goals. Pre-frontal cortex actions consume a lot of energy and by not developing emotional qualities such as compassion, gratitude, and pride to shore up self-control and perseverance, we are not fully using all the tools available to us for long term success. Gratitude increases trustworthiness. It enables individuals to be more honest. Compassion has similar traits, providing a sense of purpose and the ability to be effective. Compassion to oneself is also a principal factor in long term sustainability of grit as one is more forgiving of self-made errors and mistakes, which lessens internal self-criticism. Pride, in helping the tribe, increases diligence and motivation by admiration. By making these emotional traits the foundation of one’s self-control and perseverance, in conjunction with the executive function, one builds a more robust model for grit. 

From David DeSteno: Emotional Success (2018)

There have been quite a few articles in the press about construction, restaurant and retail trade insolvencies.  Below are monthly insolvency numbers for Accommodation and Food Services, Construction and Retail Trade, plus a total for all insolvencies, since 2021.  There has been an uptrend, but when you put it in the context of pre-Covid insolvencies statistics, a more surprising picture emerges – last 4 graphs.