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- Anthropic said to prep 2026 IPO while pursuing $350B valuation in private round
Anthropic said to prep 2026 IPO while pursuing $350B valuation in private round

Top stories today:
- Anthropic said to prep 2026 IPO while pursuing $350B valuation
- OpenAI said to develop âGarlicâ model to fight Gemini, Claude
- Amazon debuts Trainium3 UltraServer, teases Trainium4
- Kalshi raises $1B at $11B as prediction-market boom continues
- Michael, Susan Dell give $6.25B for 25M kids
- American Bitcoin -50% as Trump-affiliated crypto projects shed $1B
0. Data and calendar

All values as of 6 AM ET / 3 AM PT, other than S&P500 and NASDAQ close (4 PM ET / 1 PM PT).

All times are ET.
Listen to our AI-generated podcast summarizing todayâs newsletter (beware of hallucinations):
1. Anthropic said to prep 2026 IPO while pursuing $350B valuation in private round

Anthropic has reportedly hired Wilson Sonsini to begin IPO preparations, targeting as early as 2026.
On track for ~$10B ARR by end-2025, more than 10Ă its 2024 revenue.
~$26B ARR for 2026 was projected to investors in Aug.
CEO Dario Amodei has held preliminary discussions with major investment banks about the potential listing.
$15B commitment from Microsoft and NVIDIA would be included in a new private round at a $300B-$350B valuation.
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Hereâs whatâs inside:
Where expectations are rising and where teams are falling short
The behaviors reshaping product strategy for 2026
What it takes to build trust, earn loyalty, and lead
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3. OpenAI said to develop âGarlicâ model that beats Gemini 3, Claude Opus 4.5 in internal tests

Representational image generated using ChatGPT.
OpenAI Chief Research Officer Mark Chen reportedly briefed colleagues on the new âGarlicâ model.
Coming as soon as possible, Chen said, potentially as GPT-5.2 or GPT-5.5 early next year.
Garlic is distinct from the âShallotpeatâ model Sam Altman mentioned to staff in Oct.
Incorporated bug fixes discovered during Shallotpeatâs development into its own pretraining process.
Chen said OpenAI can now fit the same level of knowledge into a smaller model, reducing the need for much larger systems.



