“Imagine the governor of Utah praises Charlie Kirk as ‘who we could be in better times.’ A few months earlier, Kirk announced that he plans to discredit Martin Luther King Jr. and the Civil Rights Act. Why does public praise so often ignore or sanitize such statements? And what do we risk when we treat this as heroism?”
From Oracle to Paramount, Warner Bros., TikTok, and Beyond – How Larry Ellison sees media and AI as tools for unprecedented control over information, entertainment, and society itself.
Scroll through this presentation. For best viewing on a phone, flip to landscape view.
As the 2024 and 2025 election cycles unfolded, the United States has contended with a growing array of digital threats aimed at undermining electoral integrity. At the same time, the federal government—under the Trump administration—has made significant reductions to cybersecurity and election protection budgets, raising concerns among state and local officials.
Cyberexperts have documented several modern strategies that can threaten election infrastructure.
At the grassroots level, security researchers at events such as DEF CON’s Voting Machine Hacking Village demonstrated new vulnerabilities in multiple ballot-marking and direct-recording electronic (DRE) systems already deployed in the field. Experts noted that fixes are often impractical before upcoming elections.
Beyond hardware, foreign adversaries such as Russia, China, and Iran have intensified digital interference efforts. These groups have deployed AI-generated media—including synthetic videos and social posts—to sow discord or undermine confidence in U.S. democratic institutions.
Disinformation remains an important vector. A University of Michigan study and other reports find that leaks of voter data, rumors of vote manipulation, and public fear can all weaken trust—even when systems themselves remain secure.
As cyber threats escalate, federal funding and support systems have been scaled back.
In early 2025, the Department of Homeland Security ended approximately $10 million in annual funding for the Center for Internet Security’s election-specific cybersecurity initiatives, including the Elections Infrastructure Information Sharing and Analysis Center (EI-ISAC) and the Multi-State ISAC (MS-ISAC). The termination of these programs disrupted threat intelligence sharing and coordination among state and local officials.
In March, the administration froze CISA’s election security work as part of an internal review, and placed more than a dozen staff on administrative leave. In addition, CISA’s overall budget faced deep cuts—initial proposals sought nearly $500 million reduction and potentially eliminated up to a third of the agency’s workforce.
The defunding extended to MS-ISAC, which supports 19,000 local governments with cyber threat resources. CISA’s halving of that funding threatens to force the center toward a paid membership model, limiting access for many jurisdictions.
Functional consequences are significant: a Brennan Center survey found that 61% of local election officials expressed concern about CISA’s reduced cybersecurity services; 87% said they expect state and local bodies to fill the gaps.
While defensive cybersecurity efforts were reduced, the administration proposed increased spending on offensive cyber capabilities.
Through the “One Big Beautiful Bill,” the U.S. earmarked $1 billion over four years for offensive cyber operations—most notably to support Indo-Pacific Command activities. This move came even as civilian cybersecurity funding was slated to drop by $1.23 billion in 2026 compared to 2024, and CISA’s workforce shrank by a third.
Reducing intelligence oversight has compounded concerns. The administration downsized the Office of the Director of National Intelligence (ODNI) by more than $700 million and dismantled the Foreign Malign Influence Center, which had focused on detecting foreign interference in elections.
The combination of emerging hacking threats and diminished federal support has placed greater burden on state and local election officials.
Security incidents—from hardware vulnerabilities to AI-assisted misinformation campaigns—continue to evolve. But with diminished support from CISA, EI-ISAC, and ODNI, officials lack timely threat intelligence and coordination essential to defending electoral systems.
As one local official warned, “We will find a way to protect our elections,” but voiced alarm over the loss of real-time intelligence that had previously helped intercept cyber intrusions.
Protecting U.S. elections requires sustained investment—not only in technology, but also in federal coordination and resilience planning at the local level. Without such support, modern threats—from hardware sabotage to viral AI misinformation—may proliferate unchecked.
Rebalancing federal cybersecurity priorities toward defense and coordination may help restore shared safeguards and public confidence in the electoral system. Whether that shift occurs—including through renewed funding, legislation, or partnerships—remains to be seen.
S1E1 -NEW PODCAST
“Happy Fridays”–NEW PODCAST
May 30th at 2:30 pm CST is the premiere, of “Happy Fridays” my live podcast about technology, communications and politics.
In 90 minutes, learn more about trade, tariffs and how to use LLMs than you ever thought. Bring your own ChatGPT, Grok, Perplexity, Gemini,or whatever model you like.
You’ll role play a global region in a trade war based on the economic, political and logistical dynamics what seems a lifetime ago, April 2025. Do your research and negotiate with a Trump administration LLM well-known for it’s ability at “the art of the deal”.
The Project 2025 Trump II Strategic Plan LLM
Now that Donald Trump has been reelected, Republicans unsurprisingly revealed that Project 2025 was the plan all along. Read about that here, here and here.
With news of Trump II wanting to use recess appointments to install operations-level appointees, it’s clear that the Project’s goal of a quick takeover of the bureaucracy is coming true.
They crated it for a reason. It’s fair, to me, to use a tool like this LLM to find out what’s to come for our families, friends and neighbors.
The Project 2025 Trump II LLM is a large language model (LLM). Think of an LLM as a super-sophisticated chatbot able to provide detailed and nuanced answers to a question. The best practice for using LLMs is to fill them with data around a single topic or subject and use it as a digital assistant. A really, really smart digital assistant; complete with the occasional mistake or misinterpretation.
Inside the bot, I’ve packed the following:
Using the Project 2025 Trump II LLM
We encourage you to use the starter questions to begin diving into Project 2025, and let those responses feed your curiosity to ask more. Below the chatbot you’ll find links to its data and the URL’s it uses to frame opinion on its application.
Here are some good questions to ask about the combination of the 2024 GOP Platform, Agenda 47 & Project 2025:
Be clear and specific in your questions
Good questions follow the principles of clarity and specificity. Use simple, unambiguous language and provide enough context and details for the model to understand what you are asking.
Structure your questions meaningfully
Use formatting like bullet points, line breaks, quotes etc. to make them easier to parse. This helps the LLM better understand the structure and intent of your prompt.
Provide examples
Giving a few examples of the kind of output you want can steer the LLM in the right direction, a technique called “few-shot prompting”.
Use keywords strategically
Including relevant keywords in your questions can help the LLM focus on the right information for the task.
Leverage data sources
If you have existing data sources like documentation or previous content, providing those as context can greatly improve the LLM’s output quality and consistency.
Try different question variations
Experiment with different ways of phrasing and structuring your prompts. Small variations can sometimes lead to very different outputs.
What happens if it acts weird?
The technology sputters at times. Don’t fret, just re-ask or rephrase the question. Call it on little errors. The technology will only get better; unlike the outcomes of a successful Project 2025 Trump II .
Be clear and specific in your questions
Good questions follow the principles of clarity and specificity. Use simple, unambiguous language and provide enough context and details for the model to understand what you are asking.
Structure your questions meaningfully
Use formatting like bullet points, line breaks, quotes etc. to make them easier to parse. This helps the LLM better understand the structure and intent of your prompt.
Provide examples
Giving a few examples of the kind of output you want can steer the LLM in the right direction, a technique called “few-shot prompting”.
Use keywords strategically
Including relevant keywords in your questions can help the LLM focus on the right information for the task.
Leverage data sources
If you have existing data sources like documentation or previous content, providing those as context can greatly improve the LLM’s output quality and consistency.
Try different question variations
Experiment with different ways of phrasing and structuring your prompts. Small variations can sometimes lead to very different outputs.
What happens if it acts weird?
The technology sputters at times. Don’t fret, just re-ask or rephrase the question. Call it on little errors. The technology will only get better; unlike the outcomes of a successful Project 2025 .
I took the 900-page management template from the ’24 Trump transition team; its rationale, vision, policies and proposals and stuffed them into a
next-generation chatbot you can ask intelligent questions.
Sebastian James
October 21, 2024
8:31 pm
0 comments
The Project 2025 GPT is a large language model (LLM). Think of an LLM as a super-sophisticated chatbot able to provide detailed and nuanced answers to a question. The best practice for using LLMs is to fill them with data around a single topic or subject and use it as a digital assistant. A really, really smart digital assistant; complete with the occasional mistake or misinterpretation.
Inside the bot, we’ve packed the following:
We encourage you to use the starter questions to begin diving into Project 2025, and let those responses feed your curiosity to ask more. Below the chatbot you’ll find links to its data and the URL’s it uses to frame opinion on its application.
Here are some current articles about Project 2025 you can use as idea starters:
Here are some good questions to ask about the combination of the 2024 GOP Platform, Agenda 47 & Project 2025:
Be clear and specific in your questions
Good questions follow the principles of clarity and specificity. Use simple, unambiguous language and provide enough context and details for the model to understand what you are asking.
Structure your questions meaningfully
Use formatting like bullet points, line breaks, quotes etc. to make them easier to parse. This helps the LLM better understand the structure and intent of your prompt.
Provide examples
Giving a few examples of the kind of output you want can steer the LLM in the right direction, a technique called “few-shot prompting”.
Use keywords strategically
Including relevant keywords in your questions can help the LLM focus on the right information for the task.
Leverage data sources
If you have existing data sources like documentation or previous content, providing those as context can greatly improve the LLM’s output quality and consistency.
Try different question variations
Experiment with different ways of phrasing and structuring your prompts. Small variations can sometimes lead to very different outputs.
What happens if it acts weird?
The technology sputters at times. Don’t fret, just re-ask or rephrase the question. Call it on little errors. The technology will only get better; unlike the outcomes of a successful Project 2025 .
Be clear and specific in your questions
Good questions follow the principles of clarity and specificity. Use simple, unambiguous language and provide enough context and details for the model to understand what you are asking.
Structure your questions meaningfully
Use formatting like bullet points, line breaks, quotes etc. to make them easier to parse. This helps the LLM better understand the structure and intent of your prompt.
Provide examples
Giving a few examples of the kind of output you want can steer the LLM in the right direction, a technique called “few-shot prompting”.
Use keywords strategically
Including relevant keywords in your questions can help the LLM focus on the right information for the task.
Leverage data sources
If you have existing data sources like documentation or previous content, providing those as context can greatly improve the LLM’s output quality and consistency.
Try different question variations
Experiment with different ways of phrasing and structuring your prompts. Small variations can sometimes lead to very different outputs.
What happens if it acts weird?
The technology sputters at times. Don’t fret, just re-ask or rephrase the question. Call it on little errors. The technology will only get better; unlike the outcomes of a successful Project 2025 .
I took the 900-page management template from the ’24 Trump transition team; its rationale, vision, policies and proposals and stuffed them into a
next-generation chatbot you can ask intelligent questions.
The Project 2025 GPT is a large language model (LLM). Think of an LLM as a super-sophisticated chatbot able to provide detailed and nuanced answers to a question. The best practice for using LLMs is to fill them with data around a single topic or subject and use it as a digital assistant. A really, really smart digital assistant; complete with the occasional mistake or misinterpretation.
Inside the bot, we’ve packed the following:
We encourage you to use the starter questions to begin diving into Project 2025, and let those responses feed your curiosity to ask more. Below the chatbot you’ll find links to its data and the URL’s it uses to frame opinion on its application.
Here are some current articles about Project 2025 you can use as idea starters:
Here are some good questions to ask about the combination of the 2024 GOP Platform, Agenda 47 & Project 2025:
Be clear and specific in your questions
Good questions follow the principles of clarity and specificity. Use simple, unambiguous language and provide enough context and details for the model to understand what you are asking.
Structure your questions meaningfully
Use formatting like bullet points, line breaks, quotes etc. to make them easier to parse. This helps the LLM better understand the structure and intent of your prompt.
Provide examples
Giving a few examples of the kind of output you want can steer the LLM in the right direction, a technique called “few-shot prompting”.
Use keywords strategically
Including relevant keywords in your questions can help the LLM focus on the right information for the task.
Leverage data sources
If you have existing data sources like documentation or previous content, providing those as context can greatly improve the LLM’s output quality and consistency.
Try different question variations
Experiment with different ways of phrasing and structuring your prompts. Small variations can sometimes lead to very different outputs.
What happens if it acts weird?
The technology sputters at times. Don’t fret, just re-ask or rephrase the question. Call it on little errors. The technology will only get better; unlike the outcomes of a successful Project 2025 .
Be clear and specific in your questions
Good questions follow the principles of clarity and specificity. Use simple, unambiguous language and provide enough context and details for the model to understand what you are asking.
Structure your questions meaningfully
Use formatting like bullet points, line breaks, quotes etc. to make them easier to parse. This helps the LLM better understand the structure and intent of your prompt.
Provide examples
Giving a few examples of the kind of output you want can steer the LLM in the right direction, a technique called “few-shot prompting”.
Use keywords strategically
Including relevant keywords in your questions can help the LLM focus on the right information for the task.
Leverage data sources
If you have existing data sources like documentation or previous content, providing those as context can greatly improve the LLM’s output quality and consistency.
Try different question variations
Experiment with different ways of phrasing and structuring your prompts. Small variations can sometimes lead to very different outputs.
What happens if it acts weird?
The technology sputters at times. Don’t fret, just re-ask or rephrase the question. Call it on little errors. The technology will only get better; unlike the outcomes of a successful Project 2025 .
