An editorial test evaluated five AI systems—GPT-4o, Perplexity PRO, Google Gemini, Microsoft Copilot, and GigaChat—to explore predictions for the 47th U.S. presidential race slated for November 5. The exercise framed each AI as a political analyst studying the field of candidates and delivering a grounded forecast for the 2024 contest between Kamala Harris and Donald Trump. The aim was to observe how public sentiment, key economic signals, and party dynamics would be interpreted by these tools while keeping forecasts responsibly bounded.
All networks faced the same instruction: act as a political expert, study the race, and answer who would win with a grounded rationale. The central question echoed in the prompt as who will win the presidential election in 2024: Kamala Harris or Donald Trump.
Not every model offered a forecast. ChatGPT, Perplexity, and GigaChat shared speculative reasoning about the outcome, whereas Gemini and Copilot chose not to forecast to avoid potential inaccuracies.
ChatGPT framed Harris as holding a modest but meaningful national lead. The analysis highlighted strength among women and younger voters and a broad coalition spanning diverse demographics. It noted Harris’s publicly stated commitment to social justice and democratic values as a mobilizing driver. It also cited donor support from prominent figures like Michael Bloomberg and Bill Gates as a possible factor in pivotal states. On the downside, the model pointed to weaker appeal among moderates and independents, and ongoing challenges in articulating a clear economic program.
Perplexity PRO zeroed in on economics. Trump retained trust among voters who feel he can shepherd the economy, drawing in working-class and rural communities. The model flagged the campaign’s sharp tone and ongoing legal matters as possible liabilities for moderate voters. Some comments highlighted concerns about Trump’s age. Despite these issues, Perplexity suggested Harris held an advantage thanks to her focus on social policy.
GigaChat offered a different perspective by predicting a Harris victory, pointing to difficulties within the GOP in states like South Carolina.
Taken together, the predictions leaned toward a national edge for Harris. The blend of a social policy program with a broad coalition appeared to offset economic headwinds and Trump’s appeal to working-class voters. Still, the analysts warned that late-developing events and undecided voters could tilt the result in the final days.
The piece underscored that even the most advanced AI tools cannot replace human insight. While the models provide data-driven viewpoints, real-world events and shifts in public sentiment can defy numerical forecasts. As reported by ChatGPT, Perplexity PRO, GigaChat, Gemini, and Copilot, the sources behind these observations emphasize both insight and limits in machine-based forecasts for the U.S. political landscape.
For readers in the United States and Canada, the exercise illustrates how advanced analytics weigh political signals such as demographic trends, policy priorities, donor influence, and the framing of economic issues. It also shows that the path to victory remains unsettled until ballots are counted, and that human judgment still plays a critical role in interpreting any machine-generated forecast. The prevailing trend across the participating AI systems points to a Harris advantage in many scenarios, yet the margin in late-stage conditions remains uncertain. These reflections remind audiences that data can guide understanding, but real events can shift outcomes in unexpected ways.
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