Archive for the ‘Artificial Intelligence – Breakthroughs in Theories and Technologies’ Category

DeepSeek-V3 and Reasoning Model R1: Four Views (a) Explanations (b) The Chinese Perspective (c) DeepSeek Impact on Demand for Inference Chips & Training Chips, and (d) LPBI Group: Expert Content for ML Models in Healthcare, Pharmaceutical, Medical and Life Sciences

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Deep Learning, Machine Learning, Natural Language Processing (NLP) on January 28, 2025| Leave a Comment »

DeepSeek-V3 and Reasoning Model R1: Four Views (a) Explanations (b) The Chinese Perspective (c) DeepSeek Impact on Demand for Inference Chips & Training Chips, and (d) LPBI Group: Expert Content for ML Models in Healthcare, Pharmaceutical, Medical and Life Sciences

Curator: Aviva Lev-Ari, PhD, RN

With the announcement of DeepSeek on January 27, 2025, it became compelling to cover several aspects of this hot Artificial Intelligence Technology.

This curation has four Parts: 

Part A: Explanations 

Part B: The Chinese Perspective

Part C: DeepSeek potential Impact on Demand for Inference Chips & Training Chips, and

Part D: LPBI Group: Expert Content for ML Models in Healthcare, Pharmaceutical, Medical and Life Sciences

 

Part A: Explanations by Morgan Brown

@morganb

Jan 27   Read on X

🧵 Finally had a chance to dig into DeepSeek’s r1…

Let me break down why DeepSeek’s  AI innovations are blowing people’s minds (and possibly threatening Nvidia’s $2T market cap) in simple terms… 

0/ first off, shout out to @doodlestein who wrote the must-read on this here:

The Short Case for Nvidia StockAll the reasons why Nvidia will have a very hard time living up to the currently lofty expectations of the market.https://youtubetranscriptoptimizer.com/blog/05_the_short_case_for_nvda

1/ First, some context: Right now, training top AI models is INSANELY expensive. OpenAI, Anthropic, etc. spend $100M+ just on compute. They need massive data centers with thousands of $40K GPUs. It’s like needing a whole power plant to run a factory. 

2/ DeepSeek just showed up and said “LOL what if we did this for $5M instead?” And they didn’t just talk – they actually DID it. Their models match or beat GPT-4 and Claude on many tasks. The AI world is (as my teenagers say) shook. 

3/ How? They rethought everything from the ground up. Traditional AI is like writing every number with 32 decimal places. DeepSeek was like “what if we just used 8? It’s still accurate enough!” Boom – 75% less memory needed. 

4/ Then there’s their “multi-token” system. Normal AI reads like a first-grader: “The… cat… sat…” DeepSeek reads in whole phrases at once. 2x faster, 90% as accurate. When you’re processing billions of words, this MATTERS. 

5/ But here’s the really clever bit: They built an “expert system.” Instead of one massive AI trying to know everything (like having one person be a doctor, lawyer, AND engineer), they have specialized experts that only wake up when needed.

[color added by curator, See Part D, below]

6/ Traditional models? All 1.8 trillion parameters active ALL THE TIME. DeepSeek? 671B total but only 37B active at once. It’s like having a huge team but only calling in the experts you actually need for each task. 

7/ The results are mind-blowing:
– Training cost: $100M → $5M
– GPUs needed: 100,000 → 2,000
– API costs: 95% cheaper
– Can run on gaming GPUs instead of data center hardware 

8/ “But wait,” you might say, “there must be a catch!” That’s the wild part – it’s all open source. Anyone can check their work. The code is public. The technical papers explain everything. It’s not magic, just incredibly clever engineering. 

9/ Why does this matter? Because it breaks the model of “only huge tech companies can play in AI.” You don’t need a billion-dollar data center anymore. A few good GPUs might do it. 

10/ For Nvidia, this is scary. Their entire business model is built on selling super expensive GPUs with 90% margins. If everyone can suddenly do AI with regular gaming GPUs… well, you see the problem. 

11/ And here’s the kicker: DeepSeek did this with a team of <200 people. Meanwhile, Meta has teams where the compensation alone exceeds DeepSeek’s entire training budget… and their models aren’t as good. 

12/ This is a classic disruption story: Incumbents optimize existing processes, while disruptors rethink the fundamental approach. DeepSeek asked “what if we just did this smarter instead of throwing more hardware at it?” 

13/ The implications are huge:
– AI development becomes more accessible
– Competition increases dramatically
– The “moats” of big tech companies look more like puddles
– Hardware requirements (and costs) plummet 

14/ Of course, giants like OpenAI and Anthropic won’t stand still. They’re probably already implementing these innovations. But the efficiency genie is out of the bottle – there’s no going back to the “just throw more GPUs at it” approach. 

15/ Final thought: This feels like one of those moments we’ll look back on as an inflection point. Like when PCs made mainframes less relevant, or when cloud computing changed everything.

 AI is about to become a lot more accessible, and a lot less expensive. The question isn’t if this will disrupt the current players, but how fast.

/end 

P.S. And yes, all this is available open source. You can literally try their models right now. We’re living in wild times! 🚀 

Momma, I’m going viral! No substack or gofundme to share but a few things to add/clarify:

1/ The DeepSeek app is not the same thing as the model. Apps are owned and operated by a Chinese corporation, the model itself is open source.

2/ Jevon’s paradox is the counter argument. Thanks papa @satyanadella. Could be a mix shift in chip type, compute type, etc. but we’re constrained by power and compute right now, not demand constrained.

3/ The techniques used are not ground breaking. It’s the combination of them w/the relative model performance that is so exciting. These are common eng techniques that combined really fly in the face of more compute is the only answer for model performance. Compute is no longer a moat.

4/ Thanks to all for pointing out my NVIDIA market cap numbers miss and other nuances – will do better next time, coach. 🫡 

SOURCE

https://threadreaderapp.com/thread/1883686162709295541.html#google_vignette

 

Part B: The Chinese Perspective

© 2025 Jordan Schneider

DeepSeek: The View from China

China’s takes are better than yours

JORDAN SCHNEIDER, IRENE ZHANG, ANGELA SHEN, AND YIWEN JAN 28

 

SOURCE

From: ChinaTalk <chinatalk@substack.com> on behalf of ChinaTalk <chinatalk@substack.com>
Reply-To: ChinaTalk <reply+2ktto4&8t4ds&&6fa8442469b96573268378f7538ff49c28c45589f5811b2a55b30e89ee8ff94d@mg1.substack.com>
Date: Tuesday, January 28, 2025 at 9:54 AM
To: Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu>
Subject: DeepSeek: The View from China

And

https://www.chinatalk.media/p/deepseek-the-view-from-china

https://open.substack.com/pub/chinatalk/p/deepseek-the-view-from-china?r=8t4ds&utm_campaign=post&utm_medium=email

The Mystical DeepSeek. ‘The most important thing about DeepSeek is pushing intelligence’

1. Founder and CEO Liang Wenfeng is the core person of DeepSeek. He is not the same type of person as Sam Altman. He is very knowledgeable about technology.
2. DeepSeek has a good reputation because it was the first to release the reproducible MoE, o1, etc. It succeeded in acting early, but whether or not it did the absolute best remains to be seen. Moving forward, the biggest challenges are that resources are limited and can only be invested in the most high-potential areas. DeepSeek’s research and culture are still strong, and if given 100,000 or 200,000 chips, they might be able to do better.
3. From its preview to its official release, DeepSeek’s model’s long-context capabilities have improved rapidly. DeepSeek’s long-context 20K can be achieved with very conventional methods.
4. The CEO of Scale.ai said that DeepSeek has 50,000 chips, but that is definitely not reality. According to public information, DeepSeek had 10,000 old A100 chips and possibly 3,000 H800 cards before the ban. DeepSeek pays great attention to compliance and has not purchased any non-compliant GPUs, so it should have few chips. The way the United States uses GPUs is too extravagant.
5. DeepSeek focused all its efforts on a single goal and subsequently gave up many things, such as multimodality. DeepSeek is not just serving people, but seeking intelligence itself, which may have been a key factor in its success.
6. In some ways, quant trading can be said to be the business model of DeepSeek. Huanfang (another quantitative investment company founded by Liang Wenfeng) is the product of the last round of machine learning. DeepSeek’s highest priority is to push intelligence. Money and commercialization are not high priorities. China needs several leading AI labs to explore things that can beat OpenAI. Intelligence takes a long time to develop, and has begun to differentiate again this year, so new innovations are bound to result.
7. From a technical perspective, DeepSeek has been instrumental as a training ground for talent.
8. The business model of AI labs in the United States is not good either. AI does not have a good business model today and will require viable solutions in the future. Liang Wenfeng is ambitious; DeepSeek does not care about the model and is just heading towards AGI.
9. Many of the insights from DeepSeek’s paper involve saving hardware costs. On a couple of big dimensions of scaling, DeepSeek’s techniques are able to reduce costs.
10. In the short-term, everyone will be driven to think about how to make AI more efficient. In the long-run, questions about computing power will remain. Demand for compute remains strong and no company has enough.
11. Discussing DeepSeek’s organization:
    1. When investing, we always choose the most advanced talent. But we see from DeepSeek’s model (the team is mostly smart young people who graduated from domestic universities) that a group that coheres well may also gradually advance their skills together. It has yet to be seen whether poaching one person might break DeepSeek’s advantage, but for now this seems unlikely.
    2. While there’s a lot of money in the market, DeepSeek’s core advantage is its culture. The research culture of DeepSeek and ByteDance are similar, and both are critical for determining the availability of funding and long-term viability. Only with an important business model can there be a sustainable culture. Both DeepSeek and ByteDance have very good business models.
12. Why did DeepSeek catch up so fast?
    1. Reasoning models require high-quality data and training. For LLMs or multimodal AI, it’s difficult to catch up with a closed source model from scratch. The architecture of pure reasoning models hasn’t changed much, so it’s easier to catch up in reasoning.
    2. One reason R1 caught up quickly was that the task was not particularly difficult. Reinforcement learning only made the model choices more accurate. R1 did not break through the efficiency of Consensus 32, spending 32 times the efficiency, which is equivalent to moving from deep processing to parallelization, which is not pushing the boundaries of intelligence, just making it easier.

Pioneers vs. Chasers: ‘AI Progress Resembles a Step Function – Chasers Require 1/10th the Compute’

Points 13 – 17

[Points 18-48 was a long technical discussion we’ve machine-translated below]

Why didn’t the other companies take the DeepSeek approach: ‘Models from the big labs need to maintain a low profile’

Points 49, 50

The Divergence and Bets of 2025 Technology: ‘Can We Find Architectures Beyond Transformer?’

Points 51 – 56

Have developers moved from closed-source models to DeepSeek? ‘Not yet’

Points 57 – 62

OpenAI Stargate’s $500B Narrative and Changes in Computing Power Demand

63. The emergence of DeepSeek has led people to question the latest $500B narrative from Nvidia and OpenAI. There’s no verdict yet on compute — and OpenAI’s $500B narrative is their attempt to throw themselves a lifeline.
64. Regarding the doubts about OpenAI’s $500B infrastructure investment: because OpenAI is a commercial company, it could be risky if debt is involved.
65. $500B is an extreme number — likely to be executed over 4 or 5 years. SoftBank and OpenAI are the leading players (the former providing capital, the latter technology) — but SoftBank’s current funds can’t support $500B; rather SoftBank is using its assets as collateral. OpenAI, meanwhile, isn’t very cash-rich either, and other AI companies are more technical participants than they are funding providers. So it will be a struggle to fully realize the $500B vision.
66. OpenAI’s $500B computing power makes sense: during the exploration phase, the cost of trial and error is high, with both human and investment costs being substantial. But although the path isn’t clear and getting from o1 to R1 won’t be easy, at least we can see what the finish line looks like: we can track the intermediate markers, and from day one, aim for others’ proven end states; this gives us a better bearing on our progress. Being at the frontier exploring the next generation is most resource-intensive. The followers don’t bear exploration costs — they’re always just following. If Google/Anthropic succeed in their exploration areas, they might become the frontier company.
67. In the future, Anthropic might replace all their inference with TPU or AWS chips.
68. Domestic Chinese companies were previously constrained by computing power, but now it’s proven that the potential technical space is vast. For more efficient models, we might not need especially large cards — we can provide relatively customized chips that can be adapted for compatibility with AMD and ASIC. From an investment perspective, Nvidia’s moat is very high, but ASIC will have yet greater opportunities.
69. The DeepSeek situation isn’t really about compute — it’s about America realizing China’s capabilities and efficiency.DeepSeek isn’t Nvidia’s vulnerability; Nvidia will grow as long as AI grows. Nvidia’s strength is its ecosystem, which has been built up over a long time. Indeed, when technology develops rapidly, the ecosystem is crucial. The real crisis comes, though, when technology matures like electricity: it becomes commoditized; then, everyone will focus on products, and many ASIC chips will emerge for specific scenario optimization.

 

Impact on the Secondary Market: ‘Short-term sentiment is under pressure, but the long-term narrative continues’

Points 70 – 74

Open-Source vs Closed Source: ‘If capabilities are similar, closed source will struggle.’

Points 75 – 78

The Impact of DeepSeek’s Breakthrough: ‘Vision Trumps Technology’

79. DeepSeek’s breakthrough made the outside world realize China’s AI strength. Previously, outsiders thought China’s AI progress lagged America by two years, but DeepSeek shows the gap is actually 3 to 9 months, and in some areas, even shorter.
80. When it comes to technologies and sectors that America has historically blocked China from accessing, if China can break through nonetheless, those sectors ultimately become highly competitive. AI might follow this pattern — and DeepSeek’s success may well prove this.
81. DeepSeek didn’t suddenly explode. R1’s impressive results reverberated throughout America’s entire AI establishment.
82. DeepSeek stands on the shoulders of giants — but exploring the frontier still requires much more time and human capital cost. R1 doesn’t mean that future training costs will decrease.
83. AI explorers definitely need more computing power; China, as a follower, can leverage its engineering advantages. How Chinese large-model teams use less computing power to produce results, thereby having some definite resilience — or even doing better — might end up being how the US-China AI landscape plays out in the future.
84. China is still replicating technical solutions; reasoning was proposed by OpenAI in o1, so the next gap between various AI labs will be about who can propose the next reasoning. Infinite-length reasoning might be one vision.
85. The core difference between different AI labs’ models lies not in technology, but in what each lab’s next vision is.
86. After all, vision matters more than technology.

Technical Discussion

There was a deep technical discussion in the article that we’ve machine-translated below.

Technical Detail 1: Supervised Fine-Tuning (SFT). ‘No need for SFT on the reasoning level’

Points 18 – 27

Technical Detail 2: Data. ‘DeepSeek values data annotation’

Points 28 – 30

Technical Detail 3: Distillation. ‘The limit of distillation is that model diversity drops

Points 31 – 43

Technical Detail 4: Process Reward. ‘The upper limit of process reward is human, but the upper limit of outcome supervision is the model itself.’

Points 44 – 48

 

SOURCE of the Chinese Perspective

https://www.chinatalk.media/p/deepseek-the-view-from-china?utm_source=substack&publication_id=4220&post_id=155916148&utm_medium=email&utm_content=share&utm_campaign=email-share&triggerShare=true&isFreemail=true&r=8t4ds&triedRedirect=true

 

Part C: DeepSeek Impact on Demand for “Inference Chips” and “Training Chips”

 

Watch Full Interviews with Ark’s Cathie Wood

 

• Ark’s Wood on DeepSeek, AI, Crypto, Trump | Cathie Wood Full Interview

https://youtu.be/EKELCEW8lNo?si=Zri9QqcMHsESgO8N

 

• Cathie Wood Talks DeepSeek Lessons, Musk, Driverless Cars & UK

https://youtu.be/aThejSuMX-I?si=e9uM7TpoQ1Neb-cT

 

“Inference Chips” and “Training Chips”: Technology explained

 

AI Chips Explained: Training vs. Inference Processors Unveiled

https://www.friendsofthemetaverse.com/blog/ai-chips-explained-training-vs-inference-processors-unveiled

 

Inference chips and training chips are both types of AI chips that serve different purposes. Training chips are used to develop AI models, while inference chips are used to deploy those models in real-world applications. 

An “inference chip” is designed to efficiently execute a trained AI model on new data to make predictions in real-time, prioritizing low latency and power consumption, while a “training chip” is optimized for the computationally intensive process of initially training a machine learning model, requiring high processing power and memory bandwidth, often at the cost of power efficiency; essentially, inference chips are for “applying” the learned model, while training chips are for “learning” the model itself. 

Key Differences: 

• Focus:

Inference chips prioritize speed and energy efficiency to run predictions on new data in real-time, whereas training chips focus on maximum computational power to process large datasets during model development. 

• Application:

Inference chips are typically used in edge devices like smartphones and IoT gadgets where power consumption is critical, while training chips are usually deployed in data centers for large-scale model training. 

• Architecture Design:

Inference chips often have optimized architectures for specific operations like matrix multiplication, while training chips may have more flexible architectures to handle diverse training tasks. 

Example: 

• Inference Chip:

A dedicated AI chip in a smartphone that quickly identifies objects in a camera image. 

• Training Chip:

A high-performance GPU in a cloud computing platform used to train a complex language model on massive amounts of text data. 

 

SOURCE

https://www.google.com/search?q=training+chips+vs+inference+chips&oq=Training+Chips+vs+Inference+chips&gs_lcrp=EgZjaHJvbWUqBggAEEUYOzIGCAAQRRg7Mg0IARAAGIYDGIAEGIoFMg0IAhAAGIYDGIAEGIoFMg0IAxAAGIYDGIAEGIoFMgoIBBAAGIAEGKIEMgoIBRAAGIAEGKIEMgoIBhAAGIAEGKIEMgoIBxAAGIAEGKIE0gEKMTc2OTVqMGoxNagCCLACAQ&sourceid=chrome&ie=UTF-8

 

Training vs. Inference (But, Really: Training Then Inference)

To recap: the AI training stage is when you feed data into your learning algorithm to produce a model, and the AI inference stage is when your  algorithm uses that training to make inferences from data. Here’s a chart for quick reference: 

Table	Inference
Feed training data into a learning algorithm	Apply the model to the inference data
Produces a model comprising code and data	Produces output data
One time-ish (Requirement to retain training data in case of re-training.)	Often continuous

Inference
Apply the model to the inference data
Produces output data
Often continuous

The difference may seem inconsequential at first glance, but defining these two stages helps to show implications for AI adoption particularly with businesses. That is, given that it’s much less resource intensive (and therefore, less expensive), it’s likely to be much easier for businesses to integrate already-trained AI algorithms with their existing systems. 

And, as always, we’re big believers in demystifying terminology for discussion purposes. Let us know what you think in the comments, and feel free to let us know what you’re interested in learning about next.

SOURCE

AI 101: Training vs. Inference

November 9, 2023 by Stephanie Doyle

https://www.backblaze.com/blog/ai-101-training-vs-inference/

 

r/AMD_Stock

CryptOHFrank

“AI is really two markets, training and inference. Inference is going to be 100 times bigger than training. Nvidia is really good at training but very miscast at inference.” – Chamath Palihapitiya

Let’s discuss.

Below I layout AMD investor relevant time stamps:

7:35 – Meta AI business strategy

10:00 – Open source impact on LLM marketplace

12:10 – Telecom analogy (capex discussion)

16:35 – Closed source model economic viability

19:50 – Meta overspend on training (Nvidia)

SOURCE

https://www.reddit.com/r/AMD_Stock/comments/1cf765y/ai_is_really_two_markets_training_and_inference/

 

Part D: LPBI Group: Expert Content for ML Models in Healthcare, Pharmaceutical, Medical and Life Sciences

 

LPBI Group’s Journal http://pharmaceuticalintelligence.com had a fully developed ontology for the Healthcare, Pharmaceutical, Medical and Life Sciences domains of knowledge.

The ontology comprises of +750 categories of research. Each category consists of multiple scientific articles that were curated by domain knowledge experts in the fields of Healthcare, Pharmaceutical, Medical and Life Sciences.

• Each article is a token, a Non Fungible Token (NFT) = a mutually exclusive scientifically written piece that makes a Prior Art artifact from the intellectual property law perspective and copyright law.
• Each category of research is “An expert system knowledge base”
• Examples: The last column in this table represents the number of articles in this category of research
• Each curation is written by an expert in this domain, and
• Each one of the 469 articles in Example #1, in this category of research had been assigned THIS category by an EXPERT in this domain. 
• The universe of 469 articles represents an “Expert System Knowledge Base” in the domain of biological networks, gene regulation and evolution
• Example #1 comprises of 469 NFTs
• Example #2 comprises of 1,022 NFTs
• Example #3 comprises of 681 NFTs
• An ML model can be trained on the content of a Master file that included the content of all the 469 article files mentioned in Example #1 – that process is performed on Training Chips
• The outcomes of the model involve the phase of Inference. That process is performed on Inference Chips.

 

Example #1: 469 articles in Biological Networks, Gene Regulation and Evolution

Expert, Author, Writer (EAW): Dr. Larry Bernstein
Degree: BS, MS, MD
Specialty: Clinical Pathology
e-Mail: larry.bernstein@gmail

N = 469

Biological Networks, Gene Regulation and Evolution

 

Points (a) to (f) are applicable as well to Example #2, and #3, below. Or for any other category of research from the universe of +750 categories that consists of +50 articles

 

Example #2: 1,022 articles in CANCER BIOLOGY & Innovations in Cancer Therapy

Contributor EAW: Prabodh kumar Kandala, PhD Specialty: Preclinical Oncology, Prabodh.kandala@gmail.com

Contributor EAW: Ritu Saxena, PhD
ritu.uab@gmail.com

Contributor EAW: Dr. Larry Bernstein
Degree: BS, MS, MD
Specialty: Clinical Pathology
e-Mail: larry.bernstein@gmail.com

Contributor EAW: Stephen J. Williams
Degree: Ph.D. Pharmacology
Specialty: cancer pharmacology, ovarian specialty
e-Mail: sjwilliamspa@comcast.net
Phone: 215-487-0259

Contributor EAW: Tilda Barliya
Degree: PhD
Specialty: Cancer biology, cell biology, nanotechnology and drug delivery
e-Mail: tildabarliya@gmail.com
Phone: +972-50-8622289

N = 1,022

CANCER BIOLOGY & Innovations in Cancer Therapy

 

Example #3: 681 articles in Frontiers in Cardiology and Cardiovascular Disorders

EAW: Aviva Lev-Ari, PhD, RN

EAW: Justin D. Pearlman
Degree: MD ME PhD MA FACC
Specialty: Internal Medicine, Cardiology, Cardiovascular Radiology, Image Processing, Computer Science, Electronic Records
jdpmdphd@gmail.com
Phone:617-894-6888

N = 681

Frontiers in Cardiology and Cardiovascular Disorders

 

Respectively, the categories of research are

• “Expert systems domain knowledge bases”
• They are ready for ML model development in each of the domains that a category comprises more than 50 articles.
• Total number of categories of research in the Journal’s Ontology N = 757 on 1/28/2025

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Live Notes from JP Morgan Healthcare Conference Virtual Endpoints Preview: January 8-9 2024

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, Big Data & Analytics, BioTechnology - Venture Creation, Cancer and Current Therapeutics, Conference Coverage with Social Media, Data Science, Drug Development Process, Drug Discovery Chemistry, Institutional Capital Raised by Female Founders, Longevity, Machine Learning, New Drug Approval, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, RNA Biology, Cancer and Therapeutics, Technology Transfer: Biotech and Pharmaceutical, Translational Science, US and Global Economy: Trends and Findings, Venture Capital, tagged #JPM24, antibody-drug conjugate, antibody-drug conjugates, antiobesity, bioinvestment, Biotech Investment and Venture Growth: The Franchising of Intellectual Property as a Business Model, Cancer immunotherapy, click chemistry, GLP-1, JPM, nobel leureates on January 16, 2024| Leave a Comment »

Live Notes from JP Morgan Healthcare Conference Virtual Endpoints Preview: January 8-9 2024

Reporter: Stephen J. Williams, Ph.D.

Endpoints at #JPM24 | Primed to unlock biopharma’s next dealmaking wave

Endpoints at JP Morgan Healthcare Conference

January 8-9 | San Francisco, CA80 Mission St, San Francisco, CA

An oasis has emerged in the biopharma money desert as backers look to replenish capital — still, uncertainty remains on whether it’s a mirage or the much needed dealmaking bump the industry needs. Yet spirits run high as JPM24 marks the triumphant return of inking strategic alliances and peering into the industry crystal ball — while keeping an eye out for some major M&A.

We’re back live from San Francisco for JPM Monday and Tuesday — our calendar of can’t-miss panels and fireside chats will feature prominent biopharma leaders to watch. The Endpoints Hub provides the ultimate coworking space with everything you need — 1:1 and group meeting spots plus guest pass capabilities and more. Join us in-person at the Endpoints Hub or watch online to stay plugged into all the action.

8 JAN

Welcome remarks

8:05 AM – 8:25 AM PST

Pfizer vet Mikael Dolsten has some thoughts on Big Pharma R&D

Endpoints News founding editor John Carroll will sit down with longtime Pfizer CSO Mikael Dolsten to talk about Pfizer’s pipeline, what he’s learned on the job about preclinical research and development and what’s ahead for the pharma giant in drug development and deals.

Mikael Dolsten

Chief Scientific Officer, President, Pfizer Research & Development

Pfizer

Pfizer Mikael Dolsten: Pfizer produced a series of AI generated molecules with new properties. Sees rapid adoption of AI in the area of drug discovery and molecular design.

 

 

8:25 AM – 9:05 AM PST

What pharma wants: The industry’s dealmakers look ahead at 2024

The drug industry’s appetite for new assets hasn’t slowed down. Top business development execs will give their outlook on the year, what they’re looking for and how they see the market.

Glenn Hunzinger

Pharmaceutical & Life Sciences Consulting Solutions Leader

PwC US

Rachna Khosla

SVP, Head of Business Development

Amgen

James Sabry

Global Head of Pharma Partnering

Roche

Devang Bhuva

SVP, Corporate Development

Gilead Sciences, Inc.

Endpoints News

Dealmaking panel

Glenn Hunzinger: if you do not have a GLP1 will have a tough time getting a good market price for your company; capital markets are not where they want to be; sees a tough deal making climate like last year.  The problem with many biotech companies are they are coming earlier to the venture capital because of greater funding needs and so it is imperative that they articulate the potential of their company in scientific detail

Rachna Khosla:  Make sure your investors are not just CAPITAL PARTNERS but use their expertise and involve them in development issues you may have, especially ones that a young firm will face.  The problem is most investments assume what the future looks like (for example how antibody drug conjugates, once a field left for dead, has been rejuvenated because of advances in chemistry). 

James Sabry: noted that cardiac and metabolic drugs are now at the focus of many investors, especially with the new anti-obesity drugs on market

Devang Bhuva: Most deals we see start as collaborations or partnerships.  You want to involve an alliance management team early in the deal making process.  This process could take years.

 

9:05 AM – 9:20 AM PST

The IPO: How Apogee Therapeutics went public in the most challenging market in years

Not many biotechs went public in 2023. And of those that did, not many have had a great time of it. Apogee is the exception and our panel will offer a behind-the-scenes look at their decision to enter the market and what life is like as a young public company.

Michael Henderson

CEO

Apogee Therapeutics

Kyle LaHucik

MODERATOR

Senior Reporter

Endpoints News

Michael Henderson:  Not many biotech IPOs deals happened in 2023.  Michael feels it is because too many biotechs focused on building platforms, which was a hard sell in 2023.  He felt not many biotechs had clear milestones and investors wanted a clear primary validated target.  He said many biotech startups are in a funding crunch and most need at least $440M on their balance sheet to get to 2026.

9:50 AM – 10:10 AM PST

Top predictions for biotech in 2024

Catalent CEO Alessandro Maselli will be back at the big JPM healthcare confab to talk with Endpoints News founder John Carroll about their top predictions of what’s coming up for the biotech industry in 2024. The stakes couldn’t be higher as the industry grapples with headwinds and new opportunities in a gale of market forces. Two top observers share their thoughts on the year ahead.

Alessandro Maselli

President & CEO

Catalent

10:15 AM – 10:35 AM PST

Innovation at a crossroads: Keys to unlocking the value of science and technology

The industry has long discussed the promise of technology and the acceleration it provides in scientific advancement and across the industry value chain. However, the promise of its impact has yet to fully be realized. This discussion will outline the keys to unleashing this promise and the implications and actions to be taken by the biopharmaceutical companies across the industry.

Ray Pressburger

North America Life Sciences Industry Lead & Global Life Sciences Strategy Lead

Accenture

SPONSORED BY

10:35 AM – 11:05 AM PST

Activism and Investing: In conversation with Elliott Investment Management’s Marc Steinberg

Elliott has been behind many of 2023’s highest-profile healthcare investments, including multiple activist engagements and taking Syneos Health private. What has made large healthcare companies such interesting investment opportunities for firms like Elliott? What’s Elliott’s investing strategy in healthcare? And what should companies expect when an activist calls?

Marc Steinberg

Senior Portfolio Manager

Elliott Investment Management

Andrew Dunn

MODERATOR

Biopharma Correspondent

Endpoints News

11:05 AM – 11:35 AM PST

Creating ROI from AI

AI is predicted to transform the way drugs are made, from discovery to clinical trials to market. But beyond the initial hype and early adoption, where has AI made meaningful contributions to R&D? How does it help drug developers advance science? Endpoints publisher Arsalan Arif is convening a panel of leading experts to discuss the state of AI in the pharmaceutical landscape and the outlook for 2024. How does AI impact the drug pipeline, from the early steps of discovery to reducing trial failure rate?

Thomas Clozel

Co-Founder & CEO

Owkin

Venkat Sethuraman

SVP, Global Biometrics & Data Sciences

Bristol Myers Squibb

Frank O. Nestle

Global Head of Research & Chief Scientific Officer

Sanofi

Matthias Evers

Chief Business Officer

Evotec

Arsalan Arif

MODERATOR

Founder & Publisher

Endpoints News

SPONSORED BY

11:35 AM – 12:00 PM PST

Biopharma’s dealmaker: Behind the scenes with Centerview Partners co-president Eric Tokat

Almost every major biopharma deal in 2023 had Centerview’s name attached to it. And much of the time, Eric Tokat was the banker making those deals happen. Hear his outlook for 2024, how transactions are getting done and what’s placed his firm at the center of so much action.

E. Eric Tokat

Co-President, Investment Banking

Centerview Partners

CenterView Partners Eric Tokat feels dealmaking will improve in 2024, given the recent flurry of dealmaking at end of last year and right before main JPM Healthcare Conference.  He says Centerview wants to help the biotechs they invest in on their strategic path.  This may translate into buyers more actively involved (more than startups want) and buyers now are in the drivers seat as far as the timeline of deals and development.

Is the megamerger dead for this year?  He says it is very hard to see two major mergers happening but there will be many smaller and mid size biotech deals happening, but these deals will be more speculative in nature..  The focus for large pharma is top line growth.  Most of the buyers have an infrastructure and value is more of buying and dropping it in their business so there is now a huge emphasis on due diligence on whether synergies exist or not

 

12:00 PM – 12:30 PM PST

Founder, legend, leader: In conversation with Nobel laureate Carolyn Bertozzi

Carolyn Bertozzi’s discoveries around bioorthogonal chemistry won the Nobel Prize in Chemistry in 2022 and are at the heart of new therapies being tested in patients. Join us as we discuss what inspires her and where she sees the next big advances.

Carolyn Bertozzi

Prof. of Chemistry, Stanford University and Baker Family Director of Sarafan ChEM-H

Stanford University

Nicole DeFeudis

MODERATOR

Editor

Endpoints News

Bioorthogonal chemistry: class of high yielding chemical reactions that proceed rapidly and selectively in biological environments without side reactions toward endogenous functions.  This is also a type of ‘click chemistry’ in biological system where only specifically alter the biomolecule of interest.

Orthogonal: two chemicals not interacting with each other

Dr. Bertozzi noted she has started a new Antibody-Drug-Conjugate (ADC) company which involves designing with biorthogonal chemistry to make new functional molecules with varying properties

She noted hardly any biologists knew anything about glycobiology when she first started.  However now she feels pharma and academia are working very well with each other

Bioorthogonal and Click Chemistry
Curated by Prof. Carolyn R. Bertozzi, 2022 winner of the Nobel Prize in Chemistry

Source: https://pubs.acs.org/page/vi/bioorthogonal-click-chemistry

The 2022 Nobel Prize in Chemistry has been awarded jointly to ACS Central Science Editor-in-Chief, Carolyn R. Bertozzi of Stanford University, Morten Meldal of the University of Copenhagen, and K. Barry Sharpless of Scripps Research, for the development of click chemistry and bioorthogonal chemistry.

To celebrate this remarkable achievement, 2022 Nobel Prize winner Professor Carolyn R. Bertozzi has curated this Bioorthogonal and Click Chemistry Virtual Issue, highlighting papers published across ACS journals that have built upon the foundational work in this exciting area of chemistry.

From Mechanism to Mouse: A Tale of Two Bioorthogonal Reactions

Ellen M. Sletten and Carolyn R. Bertozzi* Acc. Chem. Res. 2011, 44, 9, 666-676 August 15, 2011

Abstract

Bioorthogonal reactions are chemical reactions that neither interact with nor interfere with a biological system. The participating functional groups must be inert to biological moieties, must selectively reactive with each other under biocompatible conditions, and, for in vivo applications, must be nontoxic to cells and organisms. Additionally, it is helpful if one reactive group is small and therefore minimally perturbing of a biomolecule into which it has been introduced either chemically or biosynthetically. Examples from the past decade suggest that a promising strategy for bioorthogonal reaction development begins with an analysis of functional group and reactivity space outside those defined by nature. Issues such as stability of reactants and products (particularly in water), kinetics, and unwanted side reactivity with biofunctionalities must be addressed, ideally guided by detailed mechanistic studies. Finally, the reaction must be tested in a variety of environments, escalating from aqueous media to biomolecule solutions to cultured cells and, for the most optimized transformations, to live organisms.

9 JAN

9:40 AM – 10:10 AM PST

Biotech downturn survival school

Our panelists have seen the worst, and made it through to the other side. Join us for downturn survival school as our panelists talk about what sets apart the ones who make it through tough times.

These panalists think it will be specialist capital year to shine while the general capital is still sitting on the sidelines

JJ Kang

CEO

Appia Bio

“2023 was a tough year while 2020 was a boon year to start a company.  We will continue to see these cycles; many of these new CEOs have never seen a biotech downturn yet and may not know how to preserve capital for the downturn”.

“Doing a partnership with Kite Pharmaceuticals early in our startp allowed us to get work done without risking a lot of capital, even if it means equity and asset dilution.  That makes sense. However even if you are small insist on being an equal partner.”

“There are many investors we talk to who do not want to invest in cell therapy.  Too risky now”

Carl Gordon

Managing Partner

OrbiMed Advisors

There are many macroeconomic factors affecting investment and capital today which will carry on through 2024.   Not raising money when you do not need money is a bad philosophy.  Always bbe raising captial.  This is especially true when you have to rely on hedge funds.  Parnerships howeve are sometimes the only way for small biotechs to leverage their strengths.

Joshua Boger

Executive Chair

Alkeus Pharmaceuticals, Inc.

Boger: Expect volatility for 2024.  This environment feels very different than past downturns.

Even in downturns there is still lots of capital; remember access to human capital is better in a downturn and is easier to access;  however it has become harder to get drug approvals

The panelists agree that access to capital and funding will be as tricky in 2024 than 2023.  They did

suggest that a new funding avenue, private credit, may be a source of capital.  This is discussed below:

When thinking about a private alternative investment asset class, the first thing that springs to mind is private equity. But there’s one more asset class with the word private in its name that has recently gained much attention. We’re talking about private credit. 

Indeed, this once little-known investment strategy is now growing rapidly in popularity, offering private investors worldwide an exciting opportunity to diversify their portfolio with, in theory, less risky investments that yield significant returns. 

• Private credit investments refer to investors lending money to companies who then repay the loan at a given interest rate within the predetermined period.
• The private credit market has grown significantly over the past years, rising from $875 million in 2020 to $1.4 trillion at the beginning of 2023. 

Please WATCH VIDEO BY GOLDMAN SACHS ON PRIVATE CREDIT

https://www.gsam.com/content/dam/gsam/video/common/en/2023/TheRoadAheadForPrivateCredit.mp4

 

 

 

 

10:50 AM – 11:20 AM PST

The New Molecule: How breakthrough technologies are actually changing pharma R&D

Join us for a look at how AI, machine learning and generative technologies are actually being applied inside drugmakers’ labs. We’ll explore how new technologies are being used, their implications, how they intersect with regulatory and IP issues and how this fast-changing field is likely to evolve.

Kailash Swarna

Managing Director & Global Life Sciences Clinical Development Lead

Accenture

Artificial Intelligence is making impact in a grand way on biology in three aspects:

1. Speeding up target validation: now we can get through 300 molecules a day
2. Predicition like AlphaFold is doing; molecular simulations
3. Document submission especially with regulatory and IND submissions

Pamela Carroll

COO

Isomorphic Labs formerly of AlphaFold

We were first with Novartis at last year JPM and was one year old but parnering with them in that initial year was very important for sealing the deal.

They are looking now at neurologic diseases like ALS.  She wondered whether ALS is actually multiple diseases and we need to stratify patients like we do in oncology trials.  Their main competion is the whole tech world like Amazon, Google and other Machine Learning companies so being a tech player in the biotech world means you are not just competing with other biotechs but large tech companies as well.

Jorge Conde

General Partner

Andreessen Horowitz

Need is still great for drug discovery; early adopters show AI tools can be used in big pharma. There are lots of applications of AI in managing care; a lot of back office applications including patient triaging.  He does not see big AI mergers with pharma companies –  this will be mainly partnerships not M&A deals

Alicyn Campbell

Chief Scientific Officer

Evinova, a Healthtech Subsidiary of the AstraZeneca Group

There is a need to turn AI for real world example.  For example AI tools were used in clinical trials to determine patient cohorts with pneumonitis.  At Evinova they are determining how AI can hel[p show clinical benefit with respect to efficacy and safety

Joshua Boger at #JPM24 (Brian Benton Photography)

  January 12, 2024 09:06 AM ESTUpdated 10:00 AM PeopleStartups

Vertex founder Joshua Boger on surviving downturns, ‘painful’ partnerships, and the importance of culture: #JPM24

Andrew Dunn

Biopharma Correspondent

Source: https://endpts.com/jpm24-vertex-founder-joshua-boger-on-surviving-downturns-painful-partnerships-and-the-importance-of-culture/

While the JP Morgan Healthcare Conference was full of voices of measured optimism, rooting for the market to bounce back in 2024, one longtime biotech leader warned against setting any firm expectations.

Instead of predicting when the downturn may end, Vertex Pharmaceuticals founder Joshua Boger said he advises biotech leaders to expect — and plan for — volatility. Speaking Tuesday on an Endpoints News panel alongside OrbiMed’s Carl Gordon and Appia Bio CEO JJ Kang, Boger shared lessons learned on surviving downturns, striking pharma deals, and the importance of keeping a company’s culture based on his two decades of founding and leading Vertex as CEO from 1989 to 2009. The 72-year-old is now serving as executive chairman of Alkeus Pharmaceuticals, a startup developing a rare disease drug.

“I never experienced a straight line up,” Boger said. “Everything had its cycles, and it was how you respond to the cycle, not by predicting when the end is going to be, but just by responding to the present situation.”

At Boger’s first appearance at the JP Morgan conference in 1991, he said the conference’s theme was the end of biotech financing. Just a few months later, Regeneron successfully went public, rapidly changing the outlook for the whole field.

“We had no idea we were ever going to take public money,” he said. “When Regeneron did their IPO, we went, ‘Whoa, there’s something happening here,’ and we pivoted quickly.”

Vertex went public later that year. Throughout his 20-year tenure, Boger said no pharma company ever made an acquisition offer for Vertex, which now commands a market value of $110 billion and recently won the first FDA approval for a CRISPR gene editing therapy.

“We had an uber corporate policy to always make ourselves more expensive than anyone would stomach,” Boger said.

However, Vertex did strike a range of partnerships with Big Pharmas, which Boger described as a painful but necessary part of running a biotech startup.

“It’s impossible for a partnership not to slow you down,” he said. “You can and should try as hard as you can not to do that, but just count on it. They’ll slow you down.”

Boger said startups should insist on being equal partners in pharma deals, at least making sure they have a seat at a partner’s development meetings.

“Realize they’re going to be painful, it’s going to be horrible, and you need to do it,” Boger said.

While Vertex suffered through layoffs, stock price plunges, and trial failures, Boger credited a focus on culture as key to its long-term success.

“It’s the most important ingredient for a successful company,” he said. “Technology is acquirable. Culture is not acquirable. There are 10 companies that will fail because of culture for every one that succeeds, and the successful companies in retrospect will almost always have special cultural aspects that kept them through those downtimes.”

JPM24 opens with ADCs the hottest ticket in San Francisco

By Annalee ArmstrongJan 8, 2024 6:30am

Source: https://www.fiercebiotech.com/biotech/jpm24-opens-adcs-hottest-ticket-san-francisco

The overall deal flow in biopharma tapered off in 2023 but the big companies sure know what they want (what they really, really want), according to a new report from J.P. Morgan.

And that’s antibody-drug conjugates, which drove a fourth-quarter spike in licensing deal proceeds and provided a glimmer of hope to an industry battered by outside forces and grim financing prospects.

J.P. Morgan’s annual 2023 Biopharma Licensing and Venture Report arrived on the eve of the firm’s famous conference, which is set to welcome thousands of attendees in San Francisco today—East Coast weather permitting.

2023 was tough, but clinical biotechs still had a lot of opportunities to wheel and deal, according to J.P. Morgan. While licensing deals, venture investments, M&A and IPOs were down overall in the fourth quarter, deal values stayed fairly high thanks to a flurry of late-stage tie ups.

Follow the Fierce team’s coverage of the 2024 J.P. Morgan Healthcare Conference here. 

Biopharma licensing partnerships accounted for $63 billion in total value during the fourth quarter from 108 deals. Just one deal—Merck’s ADC partnership with Daiichi Sankyo—accounted for $22 billion of that. Another huge one was another ADC bet, with Bristol Myers Squibb signing on to work with SystImmune for a total value of $8.4 billion. If you exclude the Merck deal, the total value of these partnerships is still higher than the previous quarter, which ended with $32.1 billion.

The total number of licensing deals compares to 149 in the same quarter a year earlier, 195 for Q4 2021 and 223 for Q4 2022.

As for venture investments, the year closed out with $17 billion total across 250 rounds, thanks to $3.5 billion earned through 79 rounds in the last quarter. Aiolos Bio snagged the title of largest venture round of the quarter with $245 million, which also proved to be the largest series A, too.

There was just one IPO in all of the fourth quarter—Cargo Therapeutics making the plunge for $300 million—and 13 overall for the year. It’s a far cry from the heyday of 2021 and experts are still unsure what 2024 will hold. J.P. Morgan reported $2.5 billion raised from 12 completed biopharma IPOs for the year on Nasdaq and NYSE. Nine out of the 12 companies had clinical programs when they took the leap to the public markets. As of December 13, five of the companies were trading above their IPO price.

As for M&A, December saw a rush of Big Pharmas snapping up companies around Christmas. J.P. Morgan tallied the fourth quarter at $37.6 billion and $128.8 billion across 112 total acquisitions for all of 2023.

AbbVie was the top buyer of the quarter with the two largest acquisitions thanks to the $10 billion outlay for ImmunoGen and $8.7 billion buy of Cerevel Therapeutics.

All of this adds up to 270 total deals in the fourth quarter total, which is lower than the third quarter which exceeded 300.

J.P. Morgan sees some big potential for smaller biopharmas looking for licensing partners, as Big Pharmas have been handing out larger upfront payments for the deals they really want.

Cancer was once again the most in-demand therapeutic areas, reaching a new height of $86.1 billion in 2023. Followed by $21.1 billion for neurological disorders.

For More Articles on Real Time Conference Coverage in this Open Access Scientific Journal see:

Part One: The Process of Real Time Coverage using Social Media

Part Two: List of BioTech Conferences 2013 to Present

https://worldmedicalinnovation.org/

https://pharmaceuticalintelligence.com/2022/05/01/2022-world-medical-innovation-forum-gene-cell-therapy-may-2-4-2022-boston-in-person/

 

https://event.technologyreview.com/emtech-digital-2022/agenda-overview

 

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