OpenAI researcher Miles Wang in talks to launch AI drug discovery startup valued at $2B 38%

By Marina Temkin59%

7/14/2026, 5:27:04 PM

BS Summary: This article contains 20 faulty reasoning types, including Appeal to Authority, Anecdotal, and Biased Writer Voice, with Unattributed Quote as the most egregious example at 34.1% saturation with 114 hits. Analysis detected 712 faulty-reasoning hits from 334 analyzed words, generating a BS Score of 44.2% and a BS Rank of 38% (13,576 of 21,887 articles). This article is better (less manipulative) than 62.00% of the article peer group.

Miles Wang, an OpenAI researcher whose work includes using AI to accelerate scientific and biological discovery, is leaving the ChatGPT maker to launch a new startup focused on developing AI models for drug discovery, according to four people with knowledge of his plans. 
Several other OpenAI researchers are expected to join the new company. 
Wang is in talks to raise about $200 million at a $2 billion valuation, two of the people said. 
Lightspeed is in discussions to lead the funding round, according to sources. 
Talks are ongoing, the deal may not be final and details could change. 
Wang disputed the story’s funding figures and description of the company but did not specify the correct numbers or details. 
Lightspeed didn’t respond to a request for comment. 
The funding discussions point to investor interest in applying AI to make breakthroughs in life sciences. 
Chai Discovery, a two-year-old startup developing AI models that can predict molecular interactions to identify new drugs, announced on Tuesday that it raised $400 million at a $3.8 billion valuation. 
(Co-founder Josh Meier also passed through OpenAI as a researcher.) 
Meanwhile, Google DeepMind spinout Isomorphic Labs, which also develops AI models for drug discovery, raised a $2.1 billion Series B in May. 
Wang’s new startup may be working on AI models that will help find new uses for existing drugs and possibly those that previously failed in trials, a couple of sources told TechCrunch. 
Finding new uses for FDA-approved drugs can result in significantly faster time to revenue than developing new drugs from scratch, as these medicines have already been tested for safety. 
Wang joined OpenAI in 2024 after dropping out from Harvard, where he was working on a bachelor’s degree in computer science. 
(In recent years, investors are once again comfortable betting on young founders who haven’t completed college.) 
At OpenAI, he co-authored research papers, including evaluating how AI models can automate and accelerate scientific discovery. 
Article reasoning-pattern comparisonThis article: 9.6%Marina Temkin: 4.0%TechCrunch: 3.0%Confirmation Bias9.6%This article: 0.0%Marina Temkin: 5.2%TechCrunch: 1.4%Anchoring Bias0.0%This article: 14.4%Marina Temkin: 5.3%TechCrunch: 3.5%Availability Heuristic14.4%This article: 4.8%Marina Temkin: 1.9%TechCrunch: 1.1%Representativeness Heuristic4.8%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.6%Hindsight Bias0.0%This article: 0.0%Marina Temkin: 2.9%TechCrunch: 2.5%Overconfidence Bias0.0%This article: 0.0%Marina Temkin: 3.3%TechCrunch: 4.8%Framing Effect0.0%This article: 0.0%Marina Temkin: 0.4%TechCrunch: 0.6%Loss Aversion0.0%This article: 0.0%Marina Temkin: 0.5%TechCrunch: 0.6%Status Quo Bias0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.2%Sunk Cost Effect0.0%This article: 9.6%Marina Temkin: 8.8%TechCrunch: 4.9%Optimism Bias9.6%This article: 0.0%Marina Temkin: 0.3%TechCrunch: 1.3%Pessimism Bias0.0%This article: 6.0%Marina Temkin: 0.8%TechCrunch: 5.0%Negativity Bias6.0%This article: 6.0%Marina Temkin: 4.1%TechCrunch: 2.1%Self-Serving Bias6.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.5%Fundamental Attribution Error0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.1%Actor-Observer Bias0.0%This article: 0.0%Marina Temkin: 0.5%TechCrunch: 0.6%In-Group Bias0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.3%Out-Group Homogeneity Bias0.0%This article: 14.1%Marina Temkin: 14.4%TechCrunch: 3.5%Halo Effect14.1%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.1%Horn Effect0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.0%Dunning-Kruger Effect0.0%This article: 6.6%Marina Temkin: 4.9%TechCrunch: 2.3%Recency Bias6.6%This article: 3.0%Marina Temkin: 0.4%TechCrunch: 0.3%Primacy Effect3.0%This article: 0.0%Marina Temkin: 0.4%TechCrunch: 0.1%Blind-Spot Bias0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.3%Ad Hominem0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.6%Straw Man0.0%This article: 21.0%Marina Temkin: 8.6%TechCrunch: 4.4%Appeal to Authority21.0%This article: 0.0%Marina Temkin: 1.3%TechCrunch: 1.7%False Dilemma0.0%This article: 0.0%Marina Temkin: 0.8%TechCrunch: 0.7%Slippery Slope0.0%This article: 0.0%Marina Temkin: 0.4%TechCrunch: 0.2%Circular Reasoning0.0%This article: 8.1%Marina Temkin: 7.0%TechCrunch: 6.0%Hasty Generalization8.1%This article: 3.6%Marina Temkin: 0.1%TechCrunch: 0.2%Red Herring3.6%This article: 6.6%Marina Temkin: 3.2%TechCrunch: 1.1%Bandwagon6.6%This article: 0.0%Marina Temkin: 1.1%TechCrunch: 2.2%Appeal to Emotion0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.6%Begging the Question0.0%This article: 4.8%Marina Temkin: 2.3%TechCrunch: 2.9%Post Hoc (False Cause)4.8%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.1%Tu Quoque0.0%This article: 6.0%Marina Temkin: 1.3%TechCrunch: 0.5%Burden of Proof6.0%This article: 6.3%Marina Temkin: 0.5%TechCrunch: 0.2%Appeal to Nature6.3%This article: 0.0%Marina Temkin: 1.0%TechCrunch: 0.3%Composition/Division0.0%This article: 18.6%Marina Temkin: 1.5%TechCrunch: 2.4%Anecdotal18.6%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.1%No True Scotsman0.0%This article: 13.8%Marina Temkin: 3.5%TechCrunch: 2.0%Ambiguity (Equivocation)13.8%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Marina Temkin: 0.3%TechCrunch: 0.2%Middle Ground0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.0%Personal Incredulity0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.1%Special Pleading0.0%This article: 0.0%Marina Temkin: 0.2%TechCrunch: 0.1%Genetic Fallacy0.0%This article: 34.1%Marina Temkin: 3.7%TechCrunch: 2.0%Unattributed Quote34.1%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.7%Quote-first Misdirection0.0%This article: 16.5%Marina Temkin: 4.6%TechCrunch: 4.6%Biased Writer Voice16.5%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.8%Indoctrination0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%Marina Temkin: 0.0%TechCrunch: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Marina Temkin: 7.3%TechCrunch: 4.9%Attempt to Sell a Product or S…0.0%

334 words analyzed.

Speakers

3speakers16%attributed speech279writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 15 words • 0.0% coverageWriter's voice • 43 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 19 words • 100.0% coverageWriter's voice • 12 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageMiles Wang • 20 words • 100.0% coverageLightspeed • 8 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageJosh Meier • 10 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 32 words • 100.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageMiles Wang • 17 words • 0.0% coverage
Selected voice

Miles Wang

100%flagged-word coverage
37 attributed words67% of attributed speech90% writer coverage
0%27.5%55.0%Unattributed Quote+20.4 ptsWriter: 33.7%Miles Wang: 54.1%54.1%Biased Writer Voice-19.7 ptsWriter: 19.7%Miles Wang: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.