OLIVER LIFE SCIENCES SEARCH
Novo Nordisk and OpenAI: An Enterprise-Wide AI Bet
What an AI-Native Pharma Operating Model Means for Life Sciences Talent
THE DEAL THAT MAKES IT REAL
On April 14, 2026, Novo Nordisk announced a sweeping enterprise partnership with OpenAI, becoming the latest Big Pharma to formally commit to an AI-native operating model. The agreement is notable less for any single application than for its scope: Novo will deploy OpenAI’s models across research and development, manufacturing, supply chain, and commercial operations, with pilots launching now and full integration targeted by the end of 2026. The partnership also includes an explicit workforce dimension, with OpenAI assisting Novo in upskilling its global employee base and raising baseline AI literacy across functions.
FROM EXPERIMENT TO ENTERPRISE INFRASTRUCTURE
Novo’s announcement does not exist in isolation. It follows the $1 billion NVIDIA–Eli Lilly co-innovation lab unveiled in January, Lilly’s $2.75 billion research and licensing agreement with Insilico Medicine, and a steady cadence of platform-level AI deals across the sector. Taken together, the past several months mark a clear inflection point: AI in pharma is no longer a discovery experiment confined to a chem-informatics team. It is being repositioned as enterprise infrastructure, with implications for how drugs are designed, how plants are run, how trials are operationalized, and how commercial teams reach prescribers and patients.
ORGANIZING AROUND AI, NOT JUST ADOPTING IT
For executive teams, the strategic question has shifted from whether to adopt AI to how to organize around it. That is a harder problem. It implicates where a Chief AI or Chief Digital Officer reports, who owns model governance and validation, how computational and wet-lab investment trade off inside R&D, and how an organization brings tens of thousands of scientists, operators, and commercial colleagues up the AI literacy curve without losing the domain expertise that makes a pharma company a pharma company. Companies that treat AI as a technology procurement decision will fall behind. Those that treat it as a leadership and operating-model question are the ones positioning to lead.
OUR PERSPECTIVE
We at Oliver Life Sciences Search expect the AI-native pharma model to reshape executive hiring priorities for the remainder of 2026 and well into 2027. We are seeing rising demand for Chief AI and Chief Digital Officers with a credible blend of life sciences domain experience and modern AI fluency — pure-play tech executives rarely translate. We are also seeing more pharma and biotech clients elevate computational biology, computational chemistry, and AI/ML leadership into senior R&D roles. We expect that demand to broaden into adjacent areas where the talent market is thinner — AI governance and model validation, computational manufacturing, and the cross-disciplinary R&D and operations executives who can translate between domain experts and AI teams. Companies that hire only for technical capability will get a tool. Companies that hire for translation and operating-model leadership will get a transformation.
SOURCES
Novo Nordisk and OpenAI, joint press release (April 14, 2026): https://www.globenewswire.com/news-release/2026/04/14/3273010/0/en/novo-nordisk-and-openai-partner-to-transform-how-medicines-are-discovered-and-delivered.html
CNBC coverage: https://www.cnbc.com/2026/04/14/novo-nordisk-openai-ai-drug-discovery-healthcare-nvo.html
NVIDIA–Lilly co-innovation lab (January 12, 2026): https://nvidianews.nvidia.com/news/nvidia-and-lilly-announce-co-innovation-lab-to-reinvent-drug-discovery-in-the-age-of-ai
Lilly–Insilico Medicine R&D collaboration (March 29, 2026): https://insilico.com/news/uiy12zcjg1-insilico-medicine-announces-global-rampd
ABOUT OLIVER LIFE SCIENCES SEARCH
Oliver Life Sciences Search is a boutique executive search practice focused exclusively on the healthcare and life sciences sector. We partner with biotech and pharmaceutical companies on C-suite and senior leadership placements — Director through C-level — where the stakes are high and the right hire is genuinely consequential.
The leaders who will define AI-driven drug development — the translational scientists, the AI-embedded operators, the executives who have actually built what Lilly and Insilico are now commercializing together — are the talent our practice is built to find. We know this pool not because we search a database, but because we have spent years building relationships within it. If your next critical hire sits at the intersection of AI and drug development, we would welcome the conversation.
Susan Oliver
Founder, Oliver Life Sciences Search
oliversearch.com
OLIVER LIFE SCIENCES SEARCH
The Deal the Algorithm Didn’t Close
What the Lilly–Insilico Medicine Partnership Tells Us About AI, Drug Development, and the Leaders Who Will Define Both
THE VALIDATION THAT CHANGES THE CONVERSATION
On March 29, 2026, Eli Lilly announced a collaboration with Insilico Medicine worth up to $2.75 billion — $115 million upfront and the remainder tied to regulatory and commercial milestones across a portfolio of AI-discovered drug candidates spanning multiple therapeutic areas. The deal builds on a software licensing relationship the two companies established in 2023 and extends it into full R&D collaboration, with Lilly’s clinical development engine and disease-area expertise combining with Insilico’s Pharma.AI platform to move candidates from preclinical discovery toward the clinic.
The announcement was notable not just for its size but for what it said. Insilico’s CEO, Alex Zhavoronkov, offered a line that deserves careful attention from anyone following AI in drug development: he said that Lilly is “better in AI than Insilico,” and that no other company is better in AI than Insilico — except for Lilly. Coming from the founder of one of the most prominent AI-native drug discovery companies in the world, that is not false modesty. It is a precise observation about where competitive advantage now lives.
Andrew Adams, Lilly’s Group Vice President of Molecule Discovery, called Insilico’s AI-enabled discovery "a powerful complement" to Lilly’s development capabilities and deep disease-area expertise. That phrase — powerful complement — is worth holding onto. It captures the actual state of the field more accurately than either the optimists or the skeptics usually manage.
“Lilly is better in AI than Insilico, and no other company is better in AI than us … except for these guys.”— Alex Zhavoronkov, CEO, Insilico Medicine
THIS IS THE MOMENT WE HAVE BEEN TRACKING
In our white paper Big Data & AI in Drug Development: The Promise Is Becoming Reality — But the Leadership Gap Is Now Critical, published earlier this year, we documented the acceleration of AI in drug discovery with some precision. AlphaFold 3 had expanded from protein structure prediction to modeling interactions between proteins, DNA, RNA, and the small molecules that form the basis of most drugs. Insilico’s own rentosertib — a drug designed entirely by AI for idiopathic pulmonary fibrosis — had moved from target identification to Phase 2a clinical results in under 18 months, at roughly one-tenth the traditional cost. The global AI in drug discovery market, valued at $3.6 billion in 2024, was projected to reach $49.5 billion by 2034.
We made the case then that the technology had arrived. The Lilly–Insilico deal is a $2.75 billion confirmation. An industry that once debated whether AI could find a viable drug candidate is now writing nine-figure checks for portfolios of them.
But the deal also illuminates something that our earlier analysis anticipated: the point at which the algorithm stops.
The technology has arrived. The question now is which organizations have the leadership to deploy it.
THE DEAL THE ALGORITHM DIDN’T CLOSE
Insilico’s Pharma.AI platform is genuinely comprehensive. It covers target identification, molecular generation, property prediction, and candidate optimization — the full early discovery stack. By any reasonable measure, it represents the state of the art in AI-enabled drug discovery.
And yet: Insilico needed Lilly.
Not because the algorithm failed. The algorithm succeeded. It found candidates that a $2.75 billion enterprise believed were worth commercializing. The reason Insilico needed Lilly is that the algorithm, however sophisticated, cannot do what comes next. It cannot design and execute a Phase 1 trial. It cannot navigate the FDA’s evolving framework for AI-supported regulatory submissions. It cannot build the clinical operations infrastructure that a late-stage program requires. It cannot make the judgment calls that arise when early efficacy signals are ambiguous, when a safety finding needs interpretation, or when a portfolio prioritization decision has to weigh scientific potential against commercial reality.
Those things require people. Specifically, they require a particular kind of person — one who understands the algorithm well enough to trust it, and understands the biology well enough to know when not to.
The algorithm can design the molecule. The deal that brings it to patients still requires human judgment at every stage that follows.
THE COMPLEMENT LILLY IS PAYING $2.75 BILLION FOR
Adams’s phrase — “a powerful complement” — points to the organizational logic of the deal. Insilico brings the discovery engine. Lilly brings the development infrastructure, the regulatory expertise, the clinical operations capability, and the disease-area depth that transforms a candidate into a medicine.
What makes this complement powerful is not that these capabilities coexist. It is that they are integrated at the leadership level. The executives managing Lilly’s end of this collaboration are not choosing between AI fluency and scientific depth. They are required to have both.
In our earlier analysis, we identified the leadership profiles that pharma organizations most urgently need as AI becomes central to drug development:
• AI/ML Strategy Leaders who can evaluate and prioritize AI investments, build internal capability, and distinguish genuine capability from vendor claims — while remaining grounded in the realities of drug development timelines and regulatory requirements.
• Computational Biology and Generative AI Scientists who bridge structural biology, cheminformatics, and deep learning — the profiles that make tools like Pharma.AI operationally useful rather than theoretically impressive.
• Regulatory AI Experts who understand the FDA’s credibility framework for AI in regulatory decision-making and can build discovery pipelines that satisfy that framework without sacrificing scientific rigor.
• Translational Leaders — the rarest and most consequential profile — who can operate at the intersection of AI, biology, statistics, and business. These are the executives who determine which organizations realize AI’s potential and which are left with expensive infrastructure and little to show for it.
The Lilly–Insilico deal is a live demonstration of what happens when these profiles are present on both sides of the table. It is also a signal about what is at stake for organizations where they are not.
THE LEADERSHIP GAP HAS NOT CLOSED
In our earlier white paper, we cited a GlobalData industry survey finding that “lack of specific skills and talents” was the single biggest obstacle to digital transformation in pharma, named by 49% of surveyed professionals. A Deloitte survey found that 83% of pharmaceutical and life sciences companies struggle to find skilled AI talent, and 75% expect the shortage to worsen over the next five years.
The Lilly–Insilico deal does not change those numbers. If anything, it accelerates the urgency behind them. When the largest deals in the sector are being structured around AI capability, the organizations that cannot field leaders who understand that capability will find themselves on the wrong side of an increasingly wide gap.
There is a subtlety here that matters for talent strategy. The leaders pharma needs are not simply AI experts who have learned enough biology to function. They are, as Zhavoronkov’s comment about Lilly suggests, organizations and individuals who have done the harder work: embedding AI into how they actually operate, not just into how they describe themselves.
A Chief Data Officer who can articulate an AI strategy is no longer sufficient. What organizations need — and what the market is beginning to price accordingly — is the executive who has built the infrastructure, run the programs, navigated the regulatory conversations, and has the scar tissue that comes from doing this work in a drug development context. That profile is rare. It is in extraordinary demand. And it is not sitting passively on a job board.
The executives who will determine which organizations lead in AI drug development are already leading somewhere. Finding them requires a different kind of search.
WHAT THIS MEANS FOR YOUR ORGANIZATION
The Lilly–Insilico collaboration offers a practical framework for thinking about AI and talent in life sciences. The question is not whether to invest in AI. That question has been answered, at scale, by the capital markets. The question is whether your organization has the leadership to make that investment generative.
For most biotech and pharma companies, that means being honest about two things. First: the AI tools your competitors are using are increasingly accessible. The differentiation will not come from the platform. It will come from the people who know how to use it in the specific, high-stakes context of drug development. Second: those people are not interchangeable with either traditional drug development executives or with AI leaders from adjacent industries. The combination of domain depth and technical fluency that the Lilly–Insilico deal embodies is what you are actually looking for — and it is a smaller pool than either search alone.
The organizations that close that gap first will be the ones that look back on this period of AI-driven drug discovery and recognize that they were on the right side of it.
ABOUT OLIVER LIFE SCIENCES SEARCH
Oliver Life Sciences Search is a boutique executive search practice focused exclusively on the healthcare and life sciences sector. We partner with biotech and pharmaceutical companies on C-suite and senior leadership placements — Director through C-level — where the stakes are high and the right hire is genuinely consequential.
The leaders who will define AI-driven drug development — the translational scientists, the AI-embedded operators, the executives who have actually built what Lilly and Insilico are now commercializing together — are the talent our practice is built to find. We know this pool not because we search a database, but because we have spent years building relationships within it. If your next critical hire sits at the intersection of AI and drug development, we would welcome the conversation.
Susan Oliver
Founder, Oliver Life Sciences Search
oliversearch.com
SOURCES
Lilly–Insilico deal announcement (Bloomberg, March 29, 2026): https://www.bloomberg.com/news/articles/2026-03-29/lilly-insilico-ink-deal-on-ai-drugs-worth-up-to-2-75-billion
Lilly–Insilico deal details (BioPharma Dive): https://www.biopharmadive.com/news/eli-lilly-insilico-ai-medicine-drug-discovery/816088/
Insilico Medicine R&D collaboration announcement: https://insilico.com/news/uiy12zcjg1-insilico-medicine-announces-global-rampd
Jayatunga et al. (2024). AI-discovered drugs in clinical trials. Drug Discovery Today. https://doi.org/10.1016/j.drudis.2024.104009
GlobalData (2024). Digital Transformation in Pharma: Industry Challenges Survey. https://intuitionlabs.ai/articles/pharma-ai-skills-gap
McKinsey Global Institute (2024). Generative AI in the Pharmaceutical Industry. https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality
Insilico Medicine Phase IIa rentosertib results (Nature Medicine, 2024): https://insilico.com/news/tnrecuxsc1-insilico-announces-nature-medicine-publi
FDA Draft Guidance: AI in Regulatory Decision-Making (January 2025): https://www.federalregister.gov/documents/2025/01/07/2024-31542
BIG DATA & AI IN DRUG DEVELOPMENT
BIG DATA & AI IN DRUG DEVELOPMENT
The Promise Is Becoming Reality — But the Leadership Gap Is Now Critical
Oliver Life Sciences Search | 2026
Five years ago, we called out the unfulfilled promise of big data in drug discovery. IBM Watson’s $62 million misadventure in oncology had become the cautionary tale of the decade — a reminder that transformative technology without the right organizational infrastructure and leadership produces expensive paperweights. The question then was whether pharma could ever bridge the gap between algorithmic potential and real-world drug development.
Today, that question has a more concrete answer. An AI-designed drug has completed Phase 2 clinical trials. A Nobel Prize has been awarded for AI protein structure prediction. Venture capital poured $3.3 billion into AI drug discovery companies in a single year. The technology is no longer a promise — it is a business imperative.
And yet, the leadership gap that held the industry back in 2020 has not closed. It has widened. The urgency to act has never been greater.
FROM WATSON TO BREAKTHROUGH: WHAT CHANGED
FROM WATSON TO BREAKTHROUGH: WHAT CHANGED
In 2019, IBM quietly pivoted Watson away from drug discovery and toward clinical trials — a face-saving retreat from a space where it had consistently underdelivered. The core problem, as Atrium Research CEO Michael Elliott noted at the time, was not the technology itself but the quality and accessibility of the data behind it. Pharma data was “trapped in Excel or PowerPoint” and “lacks consistency in formats and quality.” That insight remains relevant today. But the tools surrounding that data have advanced dramatically.
The watershed moment came in 2021, when DeepMind’s AlphaFold 2 solved the 50-year-old protein structure prediction problem. The result: a publicly accessible database of more than 200 million protein structures used by over 3 million researchers in more than 190 countries. In 2024, the Nobel Prize in Chemistry was awarded to DeepMind’s Demis Hassabis and John Jumper for this achievement. AlphaFold 3, released in May 2024, expanded the model to predict interactions between proteins, DNA, RNA, and the small molecules that form the basis of most drugs — fundamentally reshaping how targets are identified and validated. (DeepMind, 2024)
Equally significant is the emergence of generative AI in lead discovery. Insilico Medicine’s rentosertib (ISM001-055) — a drug designed entirely by AI to target idiopathic pulmonary fibrosis — progressed from target identification to Phase 2a clinical trial in under 18 months, compared to a traditional timeline of four or more years, and at roughly one-tenth the typical cost. Phase 2a results, published in Nature Medicine in 2024, showed the candidate was safe, well-tolerated, and demonstrated encouraging early clinical efficacy. (Insilico Medicine, 2024)
"AI molecules have shown Phase I clinical trial success rates of 80–90% — compared to an industry average of roughly 50%.” — Jayatunga et al., Drug Discovery Today, 2024
The broader market reflects this momentum. The global AI in drug discovery market was valued at $3.6 billion in 2024 and is projected to reach $49.5 billion by 2034, growing at a compound annual rate of 30%. McKinsey now estimates that generative AI alone could generate $60 to $110 billion per year in economic value for the pharmaceutical and medical products industries, including $15 to $28 billion in early-stage drug discovery. (McKinsey Global Institute, 2024)
TWO PROBLEMS PERSIST — AND ONE HAS BECOME MORE URGENT
TWO PROBLEMS PERSIST — AND ONE HAS BECOME MORE URGENT
The original article identified two structural problems standing between pharma and the full realization of AI’s potential. Both remain. One has evolved. One has become a crisis.
Problem One: Data Remains the Foundation — and the Liability
Elliott’s 2019 observation has aged well. Despite years of digital transformation investment, pharma organizations still struggle with fragmented, inconsistent, and siloed data. The problem has become more complex, not less: the modern drug discovery pipeline now requires integrating multi-modal data sets — genomics, proteomics, electronic health records, imaging, and clinical trial results — each collected under different standards, stored in different systems, and owned by different organizational functions.
MIT’s Machine Learning for Pharmaceutical Discovery and Synthesis Consortium (MLPDS), founded in 2018 with partners including Lilly, AstraZeneca, Amgen, and Pfizer, continues to address this divide between academic machine learning research and real-world drug development. But progress requires organizations to make the internal investment to prepare their data for these tools. Federated learning approaches — which allow AI models to be trained across distributed data sets without centralizing sensitive information — are emerging as a practical solution, but they require sophisticated data infrastructure and leadership to implement.
Problem Two: The Leadership Gap Is Now a Business Risk
In 2020, the shortage of AI and analytical leaders in pharma was a competitive disadvantage. In 2026, it is a strategic risk. The technology has arrived. Companies that cannot field the leadership to deploy it will cede ground to those that can.
The numbers are stark. A GlobalData industry report (November 2024) found that “lack of specific skills and talents” was the single biggest obstacle to digital transformation in pharma, cited by 49% of surveyed professionals. A separate Deloitte survey found that 83% of pharmaceutical and life sciences companies have difficulty finding skilled AI talent — and 75% expect the shortage to worsen over the next five years. Demand for AI professionals has driven a 77% year-over-year increase in life sciences job postings. The average time to fill an AI/ML Specialist role in pharma is now four to six months.
The Chief Data Officer role, which was largely new to pharma in 2020, has since proliferated — but companies are now discovering that a CDO alone is insufficient. A new role is emerging: the Chief AI Officer, or CAIO, responsible for AI strategy, governance, and deployment across the enterprise. The competition for these executives is fierce. Pharma is now competing directly with technology, financial services, and consumer companies — industries that have been using AI at scale for a decade. As of 2024, a majority of pharma CDOs and CAIOs still come from outside the life sciences industry, bringing technical depth but often lacking the domain knowledge required to understand drug development’s scientific and regulatory complexity.
"Lack of specific skills and talents is the single biggest obstacle to digital transformation in pharma.” — GlobalData Industry Report, November 2024
THE NEW LEADERSHIP IMPERATIVES
The original article made the case for statisticians as drug development’s unsung leaders — individuals who could interpret safety signals, drive data-driven portfolio decisions, and design studies that minimize bias and false discovery. That case is still valid. But the landscape has expanded considerably.
The leadership needs of a pharmaceutical company deploying AI in 2026 span at least five distinct profiles:
• AI/ML Strategy Leaders: Executives who can evaluate and prioritize AI investments, build internal capability, and distinguish genuine breakthrough from vendor hype. These individuals must be fluent in both the business of drug development and the technical realities of machine learning.
• Data Platform and Infrastructure Leaders: Drug discovery AI is only as good as its data foundation. Companies need senior architects who can design and govern cloud-based data platforms, implement data quality standards across therapeutic areas, and enable the multi-modal data integration that modern AI requires.
• Computational Biology and Generative AI Scientists: The AlphaFold era has created a new category of scientist — one who bridges structural biology, cheminformatics, and deep learning. These individuals are in extraordinary demand and short supply.
• Regulatory AI Experts: In January 2025, the FDA published draft guidance introducing a seven-step credibility framework for the use of AI in regulatory decision-making for drug and biological products. By 2024, CDER had already received over 500 submissions incorporating AI components. Companies need leaders who understand this framework and can build AI pipelines that satisfy regulatory requirements without compromising innovation. (FDA, 2025)
• Translational Leaders: Perhaps the most critical — and rarest — profile is the executive who can operate at the intersection of AI and drug development: who understands the biology, the statistics, the technology, and the business. These “bilingual” leaders are the ones who will determine which companies realize AI’s potential and which are left managing expensive infrastructure with nothing to show for it.
OLIVER LIFE SCIENCES SEARCH: BUILT FOR THIS MOMENT
OLIVER LIFE SCIENCES SEARCH: BUILT FOR THIS MOMENT
The promise that big data once dangled in front of the pharmaceutical industry is now being fulfilled — selectively, in organizations that have made the right talent investments. Insilico Medicine succeeded not because it had better technology than its competitors, but because it had the scientific and operational leadership to build and deploy that technology in a drug development context. The same principle that Milind Kamkolkar, then Chief Data Officer at Sanofi, articulated in 2017 holds true today: the data is already there. The algorithms exist. What is still missing, in too many organizations, is the leadership to bring them together.
Oliver Life Sciences Search is purpose-built to close that gap. Our practice focuses exclusively on the life sciences sector, which means we understand not just the technical profiles these roles require, but the scientific context in which they must operate. We know the difference between an AI leader who can build models and one who can drive a drug development program — and we know where to find both.
We bring deep cross-industry reach to match our life sciences expertise. The executives who will lead pharma’s AI transformation are not all sitting in pharma companies today. Many are in technology, financial services, and advanced manufacturing — industries that have been deploying AI at enterprise scale for years. Identifying those leaders, assessing their ability to navigate the specific complexities of drug development, and bringing them into life sciences organizations is one of the most consequential recruiting challenges of this decade.
The question is no longer whether AI will transform drug development. It already is. The question is which organizations will have the leadership in place to be on the right side of that transformation. We look forward to partnering with you to find them.
References
DeepMind. (2024). AlphaFold: Five Years of Impact. Google DeepMind. https://deepmind.google/blog/alphafold-five-years-of-impact/
FDA. (2025, January). Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products; Draft Guidance. Federal Register. https://www.federalregister.gov/documents/2025/01/07/2024-31542
Gibson, E. W. (2017, June 29). Leadership in Statistics: Increasing Our Value and Visibility. doi: https://doi.org/10.1080/00031305.2017.1336484
GlobalData. (2024, November). Digital Transformation in Pharma: Industry Challenges Survey. Cited in IntuitionLabs. https://intuitionlabs.ai/articles/pharma-ai-skills-gap
Grand View Research. (2024). AI in Drug Discovery Market Report, 2033. https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-drug-discovery-market
Insilico Medicine. (2024). Insilico Announces Nature Medicine Publication of Phase IIa Results of Rentosertib. https://insilico.com/news/tnrecuxsc1-insilico-announces-nature-medicine-publi
Kamkolkar, M. (2017, September 22). Chief Data Officer, Sanofi. Digital Transformation in the Pharmaceutical Industry. CXO Talk.
Koperniak, S. (2018, May 17). Applying Machine Learning to Challenges in the Pharmaceutical Industry. MIT News. http://news.mit.edu/2018/applying-machine-learning-to-challenges-in-pharmaceutical-industry-0517
McKinsey Global Institute. (2024). Generative AI in the Pharmaceutical Industry: Moving from Hype to Reality. https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality
Jayatunga, M. K. P., Ayers, M., Bruens, L., Jayanth, D., & Meier, C. (2024). How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons. Drug Discovery Today, 29(6), 104009. https://doi.org/10.1016/j.drudis.2024.104009
Mullin, R. (2019, April 26). IBM Shifts Watson from Drug Discovery to the Clinic. Chemical & Engineering News, 97(17).
Susan Oliver
Partner, Oliver Life Sciences Search
susan.oliver@oliversearch.com
www.oliversearch.com
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