BS Summary: This article contains 16 faulty reasoning types, including Attempt to Sell a Product or Service, Appeal to Authority, and Self-Serving Bias, with Optimism Bias as the most egregious example at 26.9% saturation with 188 hits. Analysis detected 887 faulty-reasoning hits from 700 analyzed words, generating a BS Score of 42.3% and a BS Rank of 47% (14,500 of 26,881 articles). This article is better (less manipulative) than 53.90% of the article peer group.

Tucked away in the East Bay foothills, a sleek white room houses the appliances of the future  an induction stove, a battery-powered HVAC system, a fridge with a thin black box resting on top. 
Though these devices may seem average, behind the scenes, the machines are sharing more than wires  including information about how much energy they’re using, whether or not the refrigerator door is open and increasingly, how to help balance the power grid. 
Utilities and technology companies say this kind of “smart home” could make the transition to an all-electric future cheaper, more reliable and more resilient during power outages. 
As part of the state’s ambitious climate agenda, California wants millions of residents to electrify their homes and shift away from gas-powered vehicles. 
But every new electric appliance increases demand on a grid already under pressure from increased use, expanding data centers and more frequent extreme weather. 
Building enough infrastructure to meet that demand is expensive, with costs often falling on consumers. 
Instead of making costly grid upgrades, utilities are increasingly searching for ways to use the existing grid more efficiently. 
At the PG&E Applied Technology Services Center, an all-electric home in San Ramon, the utility works with private companies to show how advanced energy management technologies can work together to make electrification easier and more affordable. 
“PG&E doesn’t make money off of energy; they make money off of stuff,” said Robert Stafford, a researcher at the World Resources Institute working on transportation electrification and grid resiliency. 
“Once all of those chargers and transformers and wires are out in the world, it is in everyone’s best interest to use them as efficiently as possible.” 
Because the grid is built to meet the highest periods of electricity demand, most of the time, we actually only use 40% or 50% of its capacity, said Paul Doherty, a PG&E spokesperson. 
The utility already offers “time-of-use” plans to encourage customers to run appliances overnight or during off-peak hours. 
Smart devices would automate, or run this process quietly in the background, according to Doherty. 
Rather than physically turning off lights or turning down thermostats, customers would be able to set their energy preferences and the system would take care of the rest. 
“If they’re running their air conditioning and their dishwasher, lights are on, TV’s on, dishwasher, the laundry is running  that EV charger may say, ‘Hey, there’s a lot going on within the home, we got to keep that down within capacity,” Doherty said. 
Another example: the thin black box on top of the fridge in PG&E’s smart home. 
The device is a Pila battery, which can power most household appliances. 
Pila CEO Cole Ashman describes the tool as a way for both renters and homeowners to access “seamless” backup power during outages and to shift their power consumption to cheaper times of day. 
“All of these batteries kind of have a turbocharged brain inside of them,” Ashman said. 
“It’s going to work for you in the background, store energy when it’s cheap, charge up at night, and then, when power prices get expensive in the late afternoon, it seamlessly switches to start to run your refrigerator, your window AC, whatever is connected.” 
Some customers may be put off, however, or concerned by the potential privacy or cybersecurity risks posed by these technologies. 
International researchers, in collaboration with NYU, found that potential threats include the exposure of unique device names and even household geolocation data, all of which can be harvested by external companies without user awareness. 
With his company’s own technology, Ashman emphasized that consumers own all their own data and that the batteries are “local first,” which means they can opt in to wifi connection or cloud data sharing. 
“Our phones are getting smarter, our computers are getting smarter, and the appliances in our home are also getting smarter,” Ashman said. 
“While that might sound scary to some folks  that can be really exciting if done well,” Ashman continued. 
“These things are going to be able to take care of you even when the data center goes down, or Google goes down or the power goes out.” 
Article reasoning-pattern comparisonThis article: 4.9%Ella Jackson: 2.7%CalMatters: 1.9%Confirmation Bias4.9%This article: 4.7%Ella Jackson: 1.4%CalMatters: 0.8%Anchoring Bias4.7%This article: 0.0%Ella Jackson: 4.6%CalMatters: 2.9%Availability Heuristic0.0%This article: 0.0%Ella Jackson: 0.5%CalMatters: 0.9%Representativeness Heuristic0.0%This article: 0.0%Ella Jackson: 0.2%CalMatters: 0.5%Hindsight Bias0.0%This article: 0.0%Ella Jackson: 1.0%CalMatters: 1.2%Overconfidence Bias0.0%This article: 2.7%Ella Jackson: 6.8%CalMatters: 6.0%Framing Effect2.7%This article: 0.0%Ella Jackson: 1.1%CalMatters: 1.0%Loss Aversion0.0%This article: 6.0%Ella Jackson: 0.5%CalMatters: 0.7%Status Quo Bias6.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.2%Sunk Cost Effect0.0%This article: 26.9%Ella Jackson: 3.6%CalMatters: 3.4%Optimism Bias26.9%This article: 0.0%Ella Jackson: 2.3%CalMatters: 1.4%Pessimism Bias0.0%This article: 6.3%Ella Jackson: 8.2%CalMatters: 6.2%Negativity Bias6.3%This article: 13.9%Ella Jackson: 2.0%CalMatters: 1.6%Self-Serving Bias13.9%This article: 0.0%Ella Jackson: 1.2%CalMatters: 0.7%Fundamental Attribution Error0.0%This article: 0.0%Ella Jackson: 0.3%CalMatters: 0.2%Actor-Observer Bias0.0%This article: 0.0%Ella Jackson: 1.9%CalMatters: 1.6%In-Group Bias0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.4%Out-Group Homogeneity Bias0.0%This article: 2.1%Ella Jackson: 1.4%CalMatters: 2.5%Halo Effect2.1%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.1%Horn Effect0.0%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.0%Dunning-Kruger Effect0.0%This article: 3.1%Ella Jackson: 0.9%CalMatters: 0.9%Recency Bias3.1%This article: 0.0%Ella Jackson: 0.4%CalMatters: 0.3%Primacy Effect0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.0%Blind-Spot Bias0.0%This article: 0.0%Ella Jackson: 0.4%CalMatters: 0.6%Ad Hominem0.0%This article: 0.0%Ella Jackson: 0.4%CalMatters: 0.2%Straw Man0.0%This article: 14.7%Ella Jackson: 4.9%CalMatters: 3.0%Appeal to Authority14.7%This article: 3.4%Ella Jackson: 1.5%CalMatters: 1.1%False Dilemma3.4%This article: 3.1%Ella Jackson: 1.0%CalMatters: 0.7%Slippery Slope3.1%This article: 0.0%Ella Jackson: 0.2%CalMatters: 0.1%Circular Reasoning0.0%This article: 0.0%Ella Jackson: 3.5%CalMatters: 3.4%Hasty Generalization0.0%This article: 0.0%Ella Jackson: 0.3%CalMatters: 0.2%Red Herring0.0%This article: 3.9%Ella Jackson: 0.9%CalMatters: 0.7%Bandwagon3.9%This article: 0.0%Ella Jackson: 8.2%CalMatters: 5.1%Appeal to Emotion0.0%This article: 0.0%Ella Jackson: 1.2%CalMatters: 0.6%Begging the Question0.0%This article: 0.0%Ella Jackson: 3.5%CalMatters: 1.9%Post Hoc (False Cause)0.0%This article: 0.0%Ella Jackson: 0.2%CalMatters: 0.1%Tu Quoque0.0%This article: 0.0%Ella Jackson: 0.6%CalMatters: 0.3%Burden of Proof0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.2%Appeal to Nature0.0%This article: 0.0%Ella Jackson: 0.3%CalMatters: 0.2%Composition/Division0.0%This article: 6.3%Ella Jackson: 3.4%CalMatters: 3.0%Anecdotal6.3%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.0%No True Scotsman0.0%This article: 0.0%Ella Jackson: 1.1%CalMatters: 1.1%Ambiguity (Equivocation)0.0%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.0%Gambler’s Fallacy0.0%This article: 2.7%Ella Jackson: 0.2%CalMatters: 0.1%Middle Ground2.7%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.1%Personal Incredulity0.0%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.1%Special Pleading0.0%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.1%Genetic Fallacy0.0%This article: 0.0%Ella Jackson: 0.8%CalMatters: 0.8%Unattributed Quote0.0%This article: 0.0%Ella Jackson: 0.8%CalMatters: 0.6%Quote-first Misdirection0.0%This article: 0.0%Ella Jackson: 2.2%CalMatters: 2.9%Biased Writer Voice0.0%This article: 0.0%Ella Jackson: 0.9%CalMatters: 1.8%Indoctrination0.0%This article: 0.0%Ella Jackson: 2.4%CalMatters: 1.0%Politically Left Leaning Bias0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.1%Politically Right Leaning Bias0.0%This article: 22.0%Ella Jackson: 1.3%CalMatters: 1.2%Attempt to Sell a Product or S…22.0%

700 words analyzed.

Speakers

3speakers56%attributed speech311writer words
Selected voice

Cole Ashman

100%flagged-word coverage
195 attributed words50% of attributed speech72% writer coverage
0%40.0%80.0%Attempt to Sell a Product +79.0 ptsWriter: 0.0%Cole Ashman: 79.0%79.0%

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.