Google’s next flagship Gemini model reportedly stuck months behind schedule ⁠61%

By Matt Horne⁠27%

7/16/2026, 9:41:37 PM

BS Summary: This article contains 25 faulty reasoning types, including Negativity Bias, Biased Writer Voice, and Confirmation Bias, with Unattributed Quote as the most egregious example at 46.4% saturation with 153 hits. Analysis detected 1,221 faulty-reasoning hits from 330 analyzed words, generating a BS Score of 57% and a BS Rank of ⁠61% (8,294 of 21,196 articles). This article is worse (more manipulative) than 60.90% of the article peer group.

TL;DR 
Gemini 3.5 Pro is reportedly months behind schedule as Google works to improve the model. 
Bloomberg says the delay has frustrated employees, with some worried Anthropic and OpenAI are pulling ahead. 
Coding performance appears to be a major problem, despite Google recently updating the model’s training data. 
Google will certainly feel that it belongs at the front of the AI race with the other big hitters, but its next big Gemini model is said to be struggling to get over the line. 
Gemini 3.5 Pro is reportedly months behind schedule as Google works to bring it up to its internal standards. 
According to Bloomberg, the delay is based on information from people familiar with the matter, along with ten current and former Google employees. 
The setback has reportedly frustrated engineers, researchers, and managers inside the company, with some worried that Anthropic and OpenAI are beginning to pull further ahead. 
Google had reportedly been widely expected to unveil Gemini 3.5 Pro at its developer conference in May. 
Coding appears to be one of the main sticking points, with Bloomberg saying Google updated the model’s training data late last month in an effort to improve those abilities, only for the results to disappoint. 
The report suggests Google’s sheer size may be working against it. 
Multiple teams across Google Cloud, DeepMind, Android, and other parts of the company are building AI coding tools, while several layers of stakeholders are involved in preparing models for release. 
Employees are also said to face competition for computing power when trying to use AI internally. 
Google pushed back on the idea that it is moving too slowly, with a spokesperson saying it is “shipping quickly across a wide range of models” while keeping them cost-effective. 
The company also confirmed that it is testing Gemini 3.5 Pro, an upgraded Flash model, and other models with partners, while discussing model testing and safety standards with the US government. 
Article reasoning-pattern comparisonThis article: 25.8%Matt Horne: 2.1%Android Authority: 4.3%Confirmation Bias25.8%This article: 0.0%Matt Horne: 1.6%Android Authority: 2.2%Anchoring Bias0.0%This article: 16.7%Matt Horne: 3.3%Android Authority: 3.6%Availability Heuristic16.7%This article: 3.3%Matt Horne: 0.6%Android Authority: 1.2%Representativeness Heuristic3.3%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.6%Hindsight Bias0.0%This article: 3.3%Matt Horne: 2.7%Android Authority: 3.4%Overconfidence Bias3.3%This article: 9.1%Matt Horne: 5.6%Android Authority: 5.4%Framing Effect9.1%This article: 0.0%Matt Horne: 0.9%Android Authority: 1.1%Loss Aversion0.0%This article: 9.4%Matt Horne: 0.9%Android Authority: 1.3%Status Quo Bias9.4%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.4%Sunk Cost Effect0.0%This article: 9.1%Matt Horne: 5.1%Android Authority: 6.9%Optimism Bias9.1%This article: 15.5%Matt Horne: 1.4%Android Authority: 1.8%Pessimism Bias15.5%This article: 36.4%Matt Horne: 5.5%Android Authority: 6.0%Negativity Bias36.4%This article: 9.1%Matt Horne: 1.1%Android Authority: 1.7%Self-Serving Bias9.1%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.6%Fundamental Attribution Error0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.1%Actor-Observer Bias0.0%This article: 0.0%Matt Horne: 0.2%Android Authority: 0.6%In-Group Bias0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.2%Out-Group Homogeneity Bias0.0%This article: 0.0%Matt Horne: 1.2%Android Authority: 4.7%Halo Effect0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.1%Horn Effect0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.0%Dunning-Kruger Effect0.0%This article: 3.0%Matt Horne: 2.6%Android Authority: 2.2%Recency Bias3.0%This article: 5.2%Matt Horne: 0.2%Android Authority: 0.5%Primacy Effect5.2%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.1%Blind-Spot Bias0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.3%Ad Hominem0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.1%Straw Man0.0%This article: 15.8%Matt Horne: 2.3%Android Authority: 2.7%Appeal to Authority15.8%This article: 10.6%Matt Horne: 0.7%Android Authority: 2.0%False Dilemma10.6%This article: 0.0%Matt Horne: 0.5%Android Authority: 0.4%Slippery Slope0.0%This article: 0.0%Matt Horne: 0.2%Android Authority: 0.1%Circular Reasoning0.0%This article: 17.6%Matt Horne: 3.9%Android Authority: 8.7%Hasty Generalization17.6%This article: 0.0%Matt Horne: 0.2%Android Authority: 0.1%Red Herring0.0%This article: 7.6%Matt Horne: 0.3%Android Authority: 0.9%Bandwagon7.6%This article: 12.4%Matt Horne: 1.0%Android Authority: 2.9%Appeal to Emotion12.4%This article: 0.0%Matt Horne: 0.0%Android Authority: 1.0%Begging the Question0.0%This article: 18.8%Matt Horne: 1.4%Android Authority: 1.7%Post Hoc (False Cause)18.8%This article: 9.1%Matt Horne: 0.3%Android Authority: 0.1%Tu Quoque9.1%This article: 0.0%Matt Horne: 0.5%Android Authority: 0.4%Burden of Proof0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.3%Appeal to Nature0.0%This article: 9.1%Matt Horne: 0.6%Android Authority: 0.2%Composition/Division9.1%This article: 20.0%Matt Horne: 3.0%Android Authority: 6.0%Anecdotal20.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.1%No True Scotsman0.0%This article: 18.8%Matt Horne: 1.2%Android Authority: 3.1%Ambiguity (Equivocation)18.8%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.2%Middle Ground0.0%This article: 0.0%Matt Horne: 0.1%Android Authority: 0.2%Personal Incredulity0.0%This article: 0.0%Matt Horne: 0.3%Android Authority: 0.2%Special Pleading0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.0%Genetic Fallacy0.0%This article: 46.4%Matt Horne: 4.9%Android Authority: 2.0%Unattributed Quote46.4%This article: 9.1%Matt Horne: 0.5%Android Authority: 0.2%Quote-first Misdirection9.1%This article: 29.1%Matt Horne: 3.7%Android Authority: 12.3%Biased Writer Voice29.1%This article: 0.0%Matt Horne: 0.1%Android Authority: 2.5%Indoctrination0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Matt Horne: 0.0%Android Authority: 0.0%Politically Right Leaning Bias0.0%This article: 0.0%Matt Horne: 1.6%Android Authority: 7.5%Attempt to Sell a Product or S…0.0%

330 words analyzed.

Speakers

2speakers41%attributed speech195writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 100.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 15 words • 100.0% coverageBloomberg • 16 words • 100.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 35 words • 100.0% coverageWriter's voice • 19 words • 100.0% coverageBloomberg • 23 words • 0.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 17 words • 100.0% coverageBloomberg • 35 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 16 words • 100.0% coverageGoogle • 30 words • 100.0% coverageGoogle • 31 words • 0.0% coverage
Selected voice

Bloomberg

100%flagged-word coverage
74 attributed words55% of attributed speech99% writer coverage
0%35.0%70.0%Biased Writer Voice+45.8 ptsWriter: 23.1%Bloomberg: 68.9%68.9%Unattributed Quote+16.6 ptsWriter: 52.3%Bloomberg: 68.9%68.9%

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.