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    How NADAC, WAC, and ASP Shape Drug Costs

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    How NADAC, WAC, and ASP Shape Drug Costs

    How NADAC, WAC, and ASP Shape Drug Costs

    April 20, 2026
    The Hidden Costs Employers Don’t See in Traditional Health Plans

    The Hidden Costs Employers Don’t See in Traditional Health Plans

    March 22, 2026
    The Impact of COVID-19 on Patient Trust

    The Impact of COVID-19 on Patient Trust

    March 3, 2026
    Debunking Myths About GLP-1 Medications

    Debunking Myths About GLP-1 Medications

    February 16, 2026
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    The Future of LLMs in Healthcare

    January 26, 2026
    The Future of Healthcare Consumerism

    The Future of Healthcare Consumerism

    January 22, 2026
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    Public Perception of Peptide Regulation and Compounding Practices

    Public Perception of Peptide Regulation and Compounding Practices

    April 19, 2026
    Understanding of Clinical Evidence in Peptide and Hormone Use

    Understanding of Clinical Evidence in Peptide and Hormone Use

    March 30, 2026

    Survey Results

    Can you tell when your provider does not trust you?

    Can you tell when your provider does not trust you?

    January 18, 2026
    Do you believe national polls on health issues are accurate

    National health polls: trust in healthcare system accuracy?

    May 8, 2024
    Which health policy issues matter the most to Republican voters in the primaries?

    Which health policy issues matter the most to Republican voters in the primaries?

    May 14, 2024
    How strongly do you believe that you can tell when your provider does not trust you?

    How strongly do you believe that you can tell when your provider does not trust you?

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Home Innovations & Investing

We Need Open Artificial Intelligence

Machine learning models are not software.

Heather Meeker by Heather Meeker
January 10, 2024
in Innovations & Investing
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We Need Open Artificial Intelligence

Possessed Photography

Machine learning has been around for a long time. But in late 2022, recent advancements in deep learning and large language models started to change the game and come into the public eye. And people started thinking, “We love Open Source software, so, let’s have Open Source AI, too.”

But what is Open Source AI? And the answer is: we don’t know yet.

Machine learning models are not software. Software is written by humans, like me. Machine learning models are trained; they learn on their own automatically, based on the input data provided by humans. When programmers want to fix a computer program, they know what they need: the source code. But if you want to fix a model, you need a lot more: software to train it, data to train it, a plan for training it, and so forth. It is much more complex. And reproducing it exactly ranges from difficult to nearly impossible.

The Open Source Definition, which was made for software, is now in its third decade, and has been a stunning success. There are standard Open Source licenses that everyone uses. Access to source code is a living, working concept that people use every day. But when we try to apply Open Source concepts to AI, we need to first go back to principles.

For something to be “Open Source” it needs to have one overarching quality:  transparency. What if an AI is screening you for a job, or for a medical treatment, or deciding a prison sentence? You want to know how it works. But deep learning models right now are a black box. If you look at the output of a model, it’s impossible to tell how or why the model came up with that output. All you can do is look at the inputs to see if its training was correct. And that’s not nearly as straightforward as looking at source code.

AI has the potential to greatly benefit our world. Now is the first time in history we’ve had the information and technology to tackle our biggest problems, like climate change, poverty and war. Some people are saying AI will destroy the world, but I think it contributes to the hope of saving the world.

But first, we need to trust it. And to trust it, it needs to be open and transparent.

As a consumer, you should demand that the AI you use is open. As a developer, you should know what rights you have to study and improve AI. As a voter, you should have the right to demand that AI used by the government is open and transparent.

Without transparency, AI is doomed. AI is potentially so powerful and capable that people are already frightened of it. Without transparency, AI risks going the way of crypto–a technology with great potential that gets shut down by distrust. I hope that we will figure out how to guarantee transparency before that happens, because the problems AI can help us solve are urgent, and I believe we can solve them if we work together.

Source: Voices of Open Source
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Heather Meeker

Heather Meeker

Heather Meeker is also a Founding Portfolio Partner at OSS Capital, an early-stage venture capital fund specializing in commercial open source development. She was formerly a partner in O’Melveny & Myers’ Silicon Valley office in the Mergers and Acquisitions practice group.

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Videos

summary

An in-depth exploration of drug pricing, including key databases like NADAC, WAC, and ASP, and how they influence the pharmaceutical supply chain, policy, and patient advocacy. The episode also introduces MedPricer's innovative pricing intelligence platform, offering valuable insights for healthcare professionals, policymakers, and patients.

Chapters

00:00 Understanding Drug Pricing Dynamics
03:52 Exploring the Drug Pricing Database
10:07 Patient Advocacy and Drug Pricing
13:56 Market Intelligence in Drug Pricing
How NADAC, WAC, and ASP Shape Drug CostsDaily Remedy
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Policy Shift in Peptide Regulation

Clinical Reads

FDA Evaluation of Certain Bulk Drug Substances in Compounding: Clinical Interpretation

FDA Evaluation of Certain Bulk Drug Substances in Compounding: Clinical Interpretation

by Daily Remedy
April 19, 2026
0

Clinicians increasingly encounter patients using or requesting peptide-based therapies sourced through compounding pharmacies. The U.S. Food and Drug Administration has identified a subset of bulk drug substances, including certain peptides, that may present significant safety risks when used in compounded formulations. The clinical question is whether these regulatory signals reflect meaningful patient-level risk and how they should influence prescribing behavior. This matters because compounded peptides often sit outside traditional approval pathways, creating uncertainty around quality, dosing consistency, and safety. Understanding...

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