S.F. high-tech police surveillance center gets $3M gift, partly for ‘counter drone’ tech 65%

By Brandon Pho43%

7/21/2026, 11:30:00 AM

BS Summary: This article contains 28 faulty reasoning types, including Appeal to Authority, Post Hoc (False Cause), and Self-Serving Bias, with Ambiguity (Equivocation) as the most egregious example at 20% saturation with 156 hits. Analysis detected 1,708 faulty-reasoning hits from 781 analyzed words, generating a BS Score of 59.6% and a BS Rank of 65% (7,415 of 21,171 articles). This article is worse (more manipulative) than 65.00% of the article peer group.

San Francisco’s high-tech surveillance center  propped up with funding from Silicon Valley  just got another $3 million boost from Salesforce, one of its many private benefactors. 
Close to $1 million will go toward two new drone-docking stations and the development of “counter-drone” technology for the Real Time Investigations Center. 
The center helps the police department find suspects on the streets and aids arrests using drones and Flock Safety automatic license plate reading cameras. 
The department did not say what the “counter-drone technology” would look like, and a spokesperson for the foundation could not offer details. 
The technology is generally understood to be tactics that detect, track, and neutralize other drones, presumably those that are hostile or unauthorized and disrupt police activity. 
The remaining $2 million will go toward “additional technology, software, infrastructure, and operational needs in future years,” according to a July 16 memo. 
“We are proud to work with our cross-sector partners in the local business community to support public safety improvements in San Francisco, giving SFPD effective and proven technology tools that allow our city to remain a national leader in the field, while responsibly balancing safety with privacy,” said Ixchel Acosta, CEO of the San Francisco Police Community Foundation, in a statement. 
Salesforce sent the funds through the San Francisco Police Community Foundation, a law enforcement fundraising nonprofit whose top donors are tech companies and billionaires. 
A spokesperson for Salesforce declined to comment. 
Other donors include Ripple co-founder Chris Larsen, Reddit CEO Steve Huffman, former YCombinator partner Michael Seibel, Michael Moritz' family foundation Crankstart, and J Capital principals Ted and Kathleen Janus, the latter of whom is a former senior advisor to Gov. 
Gavin Newsom. 
Salesforce announced its donation to the police foundation in May. 
Larsen famously became a key sponsor of the surveillance unit with an initial $9 million donation in 2025, and in that same year moved the center’s headquarters from the aging Hall of Justice to a former Ripple Labs office in the Financial District. 
That was the year after voters approved Proposition E, a ballot measure championed by then-Mayor London Breed, which expanded police authority to use drones and facial recognition cameras without approval from the police commission or Board of Supervisors. 
The police department is also expanding its “Drone as First Responder” program  where trained officers at the surveillance center deploy drones from various takeoff sites around the city  to better support the Mission Bay police station serving SoMa, according to the July 16 memo. 
SFPD flight logs show drone activity from 2024 to 2026 has concentrated heaviest in three neighborhoods: SoMa at 1,056 flights, the Mission at 942, and the Tenderloin at 698 as of May 31. 
Abdul Alomar counts himself lucky to have never experienced a break-in and hasn’t seen any police drones, but they’ve flown more than 40 times this year and last over the Tenderloin block where he runs Ember Grill, a halal burger joint on Larkin Street. 
Recorded reasons for the drone flybys included: reports of gunshots, a purse-snatching, a carjacking, robberies, and vague entries simply stating: “criminal investigation.” 
He has mixed feelings about the drones. 
“It’s nice to hear that something is being done. 
Crime is always in the back of our minds. 
But we’ll have to see what the results are over the next few years,” Alomari said. 
“It definitely raises privacy concerns. 
I’m really big on privacy, and in this day and age you can argue there’s no more privacy. 
We have Flock cameras everywhere. 
We have drones everywhere.” 
Police have told other news outlets the drones aren’t used to find crime but help with active criminal investigations. 
They’ve touted myriad drone-related arrests between May and June, including four enforcement operations on the 700-block of Mission Street where drone operators worked with retail stores to arrest 29 suspects. 
That included theft suspects (otherwise known as “boosters”) and middlemen (“fencers”) who allegedly purchased stolen merchandise to resell. 
Police drones have flown through the Japantown neighborhood at least 23 times between this and last year, according to the flight logs. 
Recorded reasons for the flybys mostly pertain to unspecified criminal investigations. 
Brandon Quan, deputy director of the neighborhood’s community benefit district, said he’s seen a substantial decline in property crime in Japantown since the pandemic  when vehicle break-ins were one of the most persistent issues. 
But he said he isn’t sure how much the city’s surveillance unit has helped that trend. 
“We believe that progress reflects a combination of factors, including our close partnership with Northern Station and the work being done by the District Attorney’s Office,” said Quan. 
Article reasoning-pattern comparisonThis article: 5.5%Brandon Pho: 4.2%Mission Local: 2.7%Confirmation Bias5.5%This article: 5.9%Brandon Pho: 2.8%Mission Local: 0.9%Anchoring Bias5.9%This article: 11.5%Brandon Pho: 3.0%Mission Local: 2.8%Availability Heuristic11.5%This article: 3.1%Brandon Pho: 1.6%Mission Local: 0.9%Representativeness Heuristic3.1%This article: 4.9%Brandon Pho: 0.8%Mission Local: 0.4%Hindsight Bias4.9%This article: 0.0%Brandon Pho: 1.3%Mission Local: 1.0%Overconfidence Bias0.0%This article: 13.6%Brandon Pho: 8.1%Mission Local: 4.8%Framing Effect13.6%This article: 0.0%Brandon Pho: 0.3%Mission Local: 0.4%Loss Aversion0.0%This article: 1.7%Brandon Pho: 0.8%Mission Local: 0.7%Status Quo Bias1.7%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.2%Sunk Cost Effect0.0%This article: 1.2%Brandon Pho: 0.8%Mission Local: 1.9%Optimism Bias1.2%This article: 0.6%Brandon Pho: 2.2%Mission Local: 1.3%Pessimism Bias0.6%This article: 10.0%Brandon Pho: 7.3%Mission Local: 6.1%Negativity Bias10.0%This article: 17.0%Brandon Pho: 1.8%Mission Local: 1.2%Self-Serving Bias17.0%This article: 2.0%Brandon Pho: 2.8%Mission Local: 1.0%Fundamental Attribution Error2.0%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.2%Actor-Observer Bias0.0%This article: 0.0%Brandon Pho: 1.5%Mission Local: 1.2%In-Group Bias0.0%This article: 0.0%Brandon Pho: 0.5%Mission Local: 0.7%Out-Group Homogeneity Bias0.0%This article: 14.5%Brandon Pho: 2.8%Mission Local: 1.2%Halo Effect14.5%This article: 0.0%Brandon Pho: 0.5%Mission Local: 0.1%Horn Effect0.0%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.0%Dunning-Kruger Effect0.0%This article: 9.3%Brandon Pho: 1.8%Mission Local: 1.0%Recency Bias9.3%This article: 5.1%Brandon Pho: 1.1%Mission Local: 0.4%Primacy Effect5.1%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.0%Blind-Spot Bias0.0%This article: 0.0%Brandon Pho: 2.2%Mission Local: 3.1%Ad Hominem0.0%This article: 0.0%Brandon Pho: 0.1%Mission Local: 0.8%Straw Man0.0%This article: 19.7%Brandon Pho: 4.1%Mission Local: 2.8%Appeal to Authority19.7%This article: 2.4%Brandon Pho: 1.5%Mission Local: 1.7%False Dilemma2.4%This article: 0.0%Brandon Pho: 1.9%Mission Local: 0.8%Slippery Slope0.0%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.1%Circular Reasoning0.0%This article: 12.0%Brandon Pho: 6.4%Mission Local: 4.8%Hasty Generalization12.0%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.3%Red Herring0.0%This article: 0.0%Brandon Pho: 0.8%Mission Local: 0.9%Bandwagon0.0%This article: 4.1%Brandon Pho: 3.1%Mission Local: 3.8%Appeal to Emotion4.1%This article: 4.0%Brandon Pho: 1.6%Mission Local: 0.7%Begging the Question4.0%This article: 17.3%Brandon Pho: 3.9%Mission Local: 2.1%Post Hoc (False Cause)17.3%This article: 0.0%Brandon Pho: 0.4%Mission Local: 0.3%Tu Quoque0.0%This article: 2.0%Brandon Pho: 0.5%Mission Local: 1.2%Burden of Proof2.0%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.3%Appeal to Nature0.0%This article: 3.6%Brandon Pho: 0.9%Mission Local: 0.5%Composition/Division3.6%This article: 11.0%Brandon Pho: 2.5%Mission Local: 2.8%Anecdotal11.0%This article: 0.0%Brandon Pho: 0.5%Mission Local: 0.2%No True Scotsman0.0%This article: 20.0%Brandon Pho: 2.7%Mission Local: 1.3%Ambiguity (Equivocation)20.0%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.1%Middle Ground0.0%This article: 0.0%Brandon Pho: 0.1%Mission Local: 0.1%Personal Incredulity0.0%This article: 0.0%Brandon Pho: 0.0%Mission Local: 0.1%Special Pleading0.0%This article: 3.1%Brandon Pho: 1.0%Mission Local: 0.3%Genetic Fallacy3.1%This article: 2.8%Brandon Pho: 1.6%Mission Local: 1.0%Unattributed Quote2.8%This article: 0.0%Brandon Pho: 1.5%Mission Local: 1.0%Quote-first Misdirection0.0%This article: 10.8%Brandon Pho: 6.3%Mission Local: 2.4%Biased Writer Voice10.8%This article: 0.0%Brandon Pho: 0.1%Mission Local: 1.0%Indoctrination0.0%This article: 0.0%Brandon Pho: 0.5%Mission Local: 0.5%Politically Left Leaning Bias0.0%This article: 0.0%Brandon Pho: 0.7%Mission Local: 0.3%Politically Right Leaning Bias0.0%This article: 0.0%Brandon Pho: 0.4%Mission Local: 0.5%Attempt to Sell a Product or S…0.0%

781 words analyzed.

Speakers

3speakers20%attributed speech625writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 100.0% coverageWriter's voice • 28 words • 100.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageIxchel Acosta • 61 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 40 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 43 words • 100.0% coverageWriter's voice • 38 words • 0.0% coverageWriter's voice • 46 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 44 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageAlomar • 16 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageBrandon Quan • 35 words • 0.0% coverageBrandon Quan • 16 words • 0.0% coverageBrandon Quan • 28 words • 0.0% coverage
Selected voice

Ixchel Acosta

100%flagged-word coverage
61 attributed words39% of attributed speech97% writer coverage
0%7.5%15.0%Biased Writer Voice-13.4 ptsWriter: 13.4%Ixchel Acosta: 0.0%0.0%Unattributed Quote-3.5 ptsWriter: 3.5%Ixchel Acosta: 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.