Yeah, so. This BTFO any argument against the infeasibility of planned economies.
tl;dr: Google has created an AI that readily trains specialized AI models that act as data scientists who collect data in an eclectic manner to make predictions for a wide array of real world use cases that are on a large scale, such as predicitng the spread of disease in a country or estimating which regions are at risk of food insecurity. Their AI has produced the best results so far in terms of making reliable predictions ranging from 60-90% depending on the subject. We can only go up from here.
Planning an economy and all social affairs relevant to it is not just theoretically feasible but Google themselves have released an AI that could plausibly do it, even if they haven't explicity stated that.
I know that a lot of you will say that we (socialists) already knew this, but there is something funny about capitalism itself revealing it while also being blind to it. What will their cope be if you pointed this out?
Planetary prediction engine: Automating global models via Earth AIhttps://research.google/blog/planetary-prediction-engine-automating-global-models-via-earth-ai/
<Google's paper introduces the Planetary Prediction Engine (PPE), an autonomous AI system designed to build high-accuracy geospatial predictive models from plain-English requests.
<Traditionally, predicting real-world global events—such as tracking a disease outbreak, forecasting local food shortages, or mapping environmental changes—requires months of tedious work by specialized teams. Data scientists have to manually find datasets, clean satellite imagery, merge census data, eliminate bias, and code customized machine learning models.
<The PPE automates that entire pipeline end-to-end, turning a process that used to take weeks into an automated task that completes in minutes.
<The PPE breaks down complex predictive prompts into three automated stages, orchestrated by Large Language Models (LLMs):
<Intelligent Data Gathering: The user asks a question in natural language (e.g., "Predict regional food insecurity across West Africa over the next 6 months"). The AI parses the request, translates it into geographic and temporal boundaries, and automatically hunts for relevant data across satellite databases (like Google Earth Engine), public statistics (like Data Commons), and live web repositories.
<Data Fusion and Quality Control: The system merges satellite images, demographic data, and climate patterns using specialized foundation models. Critically, it runs automated checks ("Feature Gates") to prevent "data leakage"—ensuring the model doesn't accidentally peek at future or circular data to falsely inflate its accuracy.
<Automated Model Building: The engine tests multiple types of machine learning algorithms, fine-tunes them, guards against overfitting, and outputs a deployable prediction model along with a full explanatory report.
<The primary impact of the Planetary Prediction Engine is democratizing complex geospatial forecasting. You no longer need an elite team of satellite engineers and data scientists to get high-resolution global predictions.
<• Rapid Disaster Response: During sudden crises (flooding, tropical storms, viral outbreaks), humanitarian organizations can instantly generate location-specific threat maps in minutes to route medical supplies or emergency shelter where they are needed most.
<• Public Health Tracking: Public health officials can ask the system to track and forecast conditions like chronic disease prevalence or active virus outbreaks using dynamic local data.
<• Food Security and Agriculture: International relief groups can anticipate crop failures or spot early warnings of famine at a fine-grained, local level rather than relying on outdated, broad regional averages.
<• Policy and Resource Allocation: Governments and NGOs with limited tech budgets can run sophisticated environmental and economic impact analyses directly through simple text queries.