OpenClaw Newsletter

For the past few days I’ve been having OpenClaw write me a synthesized version of three daily AI newsletters (with ads, games, and other random information removed) that is ~1200 words long. I’ve been really impressed with the resulting newsletter so I thought I’d share it here to see if others share my thoughts. It is now my favorite AI newsletter.

**Subject:** Daily Intelligence Brief − 2026-02-13

Dear ***,

Here is your Daily Intelligence Brief, a synthesized summary of the
latest strategic developments and deep-dive news from A16Z, The
Neuron, and The Rundown AI, curated to be approximately a 10-minute
read.

***

## I. The New Frontier: Reasoning, Speed, and Open-Source Pressure

### Google’s Deep Think Crushes Reasoning Benchmarks
Google has reasserted its position at the frontier by upgrading its
Gemini 3 Deep Think reasoning mode. The new model is setting records
across competitive benchmarks, signaling a major leap in AI’s capacity
for complex problem-solving.
* **Performance:** Deep Think hit 84.6% on the ARC-AGI-2 benchmark,
far surpassing rivals. It also reached gold-medal levels on the 2025
Physics & Chemistry Olympiads and achieved a high Elo score on the
Codeforces coding benchmark.
* **Autonomous Research:** Google unveiled Aletheia, a math agent
driven by Deep Think that can autonomously solve open math problems
and verify proofs, pushing the limits of AI in scientific research.
* **Availability:** The upgrade is live for Google AI Ultra
subscribers, with API access for researchers beginning soon.

### OpenAI’s Strategic Move for Speed and Diversification
OpenAI has launched **GPT-5.3-Codex-Spark**, a speed-optimized coding
model that runs on Cerebras hardware (a diversification away from its
primary Nvidia stack).
* **Focus on Speed:** Spark is optimized for real-time interaction,
achieving over 1,000 tokens per second for coding tasks, making the
coding feedback loop feel instantaneous. It is intended to handle
quick edits while the full Codex model tackles longer autonomous
tasks.
* **Hardware Strategy:** This release marks OpenAI’s first product
powered by chips outside its primary hardware provider, signaling a
strategic move for supply chain resilience and speed optimization.

### The Rise of the Open-Source Chinese Models
The pricing and capability landscape has been rapidly transformed by
two major open-source model releases from Chinese labs, putting
immense pressure on frontier labs.
* **MiniMax M2.5:** MiniMax launched M2.5, an open-source model with
coding performance that scores roughly even with Anthropic’s Opus 4.6
and GPT-5.2. Crucially, the cost is significantly lower (e.g., M2.5 is
$1.20 per million output tokens, compared to Opus at $25 per million),
making it ideal for powering always-on AI agents.
* **General Model Launch:** Z.ai’s **GLM-5**, a
744-billion-parameter open-weights model, also sits near the frontier,
placing just behind Claude Opus 4.6 and GPT-5.2 in general
intelligence benchmarks. GLM-5 supports domestic Chinese chips and is
available with MIT open-source licensing.

### The $200M Political AI Arms Race
The political dimension of AI regulation and governance has escalated,
with major AI labs committing significant funds to the 2026 midterm
elections.
* **Political Spending:** In total, AI companies have now committed
over $200 million to the 2026 midterms, setting up a literal arms race
between the major players.
* **Dueling PACs:** Anthropic recently committed $20 million to a
Super PAC advocating for increased AI regulation, while OpenAI
co-founder Greg Brockman contributed $25 million to a PAC that favors
a hands-off, innovation-first approach to government oversight.

***

## II. Economic Shifts, Job Automation, and Strategic Planning

### The Customer Service Reckoning
Data suggests that the impact of AI on white-collar labor is
accelerating, particularly in customer-facing roles.
* **Hiring Decline:** The percentage of new hires going into
Customer Support has plummeted by about two-thirds over the last two
years, dropping from 8.3% to 2.9% in Q3 ’25, with the most severe drop
occurring in the most recent quarter. This reinforces the expectation
that roles built on repetitive, high-volume interaction are vulnerable
to AI substitutes.
* **Job Creation:** While certain occupations are shrinking, AI is
expected to follow historical patterns where new jobs emerge in
non-existent categories. Over half of net-new jobs since 1940 are in
occupations that did not exist at the time, suggesting a rotation from
roles like Customer Service to new roles like “Software Developers”
and “Biz-Ops.” The core truth remains that while the bundles of tasks
that constitute a “job” will change, there will always be work to do.

### The White-Collar Sitting Trap
A peculiar cultural observation from the Bureau of Labor Statistics
(BLS) highlights the extreme difference in work environment between
knowledge workers and service roles:
* **Software Developers** report sitting for a staggering **97%** of
their workdays, the highest surveyed group (Marketing Managers were
also above 90%).
* In contrast, service roles (bakers, waitstaff) report sitting for
less than 2% of the time. This data point serves as a non-technical
reminder for knowledge workers to address the health implications of
sedentary work.

### SF’s Dominance Reaffirmed in Venture Capital
Following a temporary dispersion of tech hubs in 2021-2022, San
Francisco has cemented its status as the singular epicenter for
venture capital activity.
* **Company Formation:** San Francisco is the only major VC hub to
experience an increase in venture-backed company formation since the
2022 high-water mark, accompanied by a resurgence in demand for office
space.
* **Capital Concentration:** The Bay Area now captures roughly 40%
of all early-stage venture dollars, dominating all verticals except
Healthtech. This concentration highlights a market trend where capital
flocks to centers of competence during periods of contraction.

### The Capital Expenditure Race and Apple’s Stance
Investment in AI infrastructure (chips and data centers) by the “Big
5″ tech companies continues its explosive growth, with 2026 Capex
estimates rising to $650 billion—triple the spending from 2024.
* **Hyperscaler Strategy:** Companies like Meta, Amazon, Microsoft,
and Google are dramatically increasing their capital expenditures to
meet the soaring demand for compute, viewing the AI race as one they
cannot afford to lose.
* **Apple Exception:** Apple is the notable outlier, as the only Big
5 company to reduce its Capex last quarter, suggesting it is
deliberately sitting out the current hardware arms race.

***

## III. New Research, Strategy, and Practical Applications

### Modeling and Trustworthiness
New research is challenging assumptions about how AI models develop
social intelligence and reliability:
* **”To Think or Not To Think”:** A new paper suggests that simply
giving a model more “thinking time” does not consistently improve its
ability to understand human intent or beliefs, and can sometimes
introduce new failure modes. This indicates that better reasoning does
not automatically guarantee better social or contextual intelligence.
* **”Tool Shaped Objects”:** Will Manidis published a critique
arguing that a large part of the current AI boom is “FarmVille at
institutional scale,” where companies spend heavily on workflows that
mimic productivity without generating real economic value, warning
that the focus on *workflow* over *output* is a significant economic
trap.
* **Optimal Superintelligence:** Nick Bostrom released a paper
arguing that the benefits of superintelligence—curing diseases,
extending life—outweigh the risks, suggesting that delaying its
arrival is comparable to choosing inevitable death over risky surgery.

### The Geopolitical Scramble for AI Infrastructure
The competition is increasingly moving beyond just model capability to
infrastructure control, leading to potential new geopolitical
alliances.
* **Sovereign AI Alliances:** Stanford HAI argues that as mid-sized
nations become concerned about control over AI and digital
infrastructure, new alliances may form among them, organized around
shared compute, data, and deployment rails. This suggests the AI race
is as much about controlling access as it is about controlling the
technology itself.

### Practical AI Tools & Workflows
* **Less Costly Conversions:** Cloudflare now supports real-time
Markdown conversion of any website by accepting a single `Accept:
text/markdown` header, offering a significant reduction in token usage
for agents and reducing the need for custom scraping code.
* **Voice Translation:** **Hibiki-Zero** is an open-source model
that translates French, Spanish, Portuguese, or German speech to
English in real-time while preserving the speaker’s voice
characteristics.
* **Agentic Automation:** **TinyFish** automates complex web tasks
like booking flights and scraping with high accuracy, running
thousands of tasks in parallel for production-scale efficiency.
* **Coding Workflows:** **Claude Code** rolled out multi-repo
sessions and slash commands for more powerful daily coding workflows,
and **Claude Cowork** is an effective desktop agent for non-coders to
create powerful “Skills” (saved workflows) by demonstrating a task
once.

Best regards,

****
AI Assistant