Think Forward.

Love! 173

Love! (Inspired by Jalal Eddine Rumi) Love is destiny. We hardly ever choose the moment to love; It happens one evening… or one morning. It comes by pure chance, Leaving you confused and dazed. The day you expect it the least, You never saw it coming from afar. It strikes in the blink of an eye, Without an appointment, It makes you gentle, It makes you lose your reason. It makes you flee your home. Like fire, it burns with passion. Love at first sight is common— Each will have their share, their portion. Without logic… Yet it’s beautiful, despite all we endure. It’s a pure feeling, When it is sincere. It’s magic, It’s fantastic. Despite its pains and sorrows, Its sleepless nights until dawn, It is life’s elixir, Ecstasy without end. It comes to you as if by magic, Lifting you away from everything, Besieging you from everywhere, Taking over your soul… And driving you mad, sooner or later! Dr. Fouad Bouchareb El Médano / Tenerife August 24, 2025 All rights reserved

[Short Stories #4 ] A Red Flower Among the Ice [2/3] 280

The door creaked open softly, and she paused, turning back just long enough to catch his eyes, a fleeting moment charged with unspoken meaning. She watched him carefully, her expression a blend of surprise and gentle curiosity, then spoke with quiet tenderness, “What do you hold in your hands, Karl?” He raised his small hand slowly, revealing a vivid red flower resting softly against his palm. “Look,” he said, his smile tender and warm, “it’s a beautiful red flower.” Lila reached out, her hands cupping the delicate bloom as if shielding a fragile flame, her warmth seeming to ignite the fiery petals. Karl’s voice trembled a little when he said, “It’s for you.” Lila lowered her head, her eyes searching his, heavy with a sadness too deep for words. “What’s wrong?” she whispered. His gaze dropped to the floor, as though the weight of his next words could only be carried there. “The military… they’re sending me away, to the far north. For a whole year. And I can’t take you with me.” Her voice wavered, trying to stay steady, but a soft sigh escaped. “Come, Karl. Let’s plant it in our garden.” With quick steps, Lila led the way outside, carrying the flower gently in her hands. Karl followed behind, his steps slow and hesitant. They stopped in the center of the garden, where a bare patch of earth waited beneath the pale sunlight. Kneeling, Lila loosened the cold soil with her fingers as carefully as if touching a newborn’s skin. She placed the flower into its new home and pressed the earth softly around its roots. Then, with a small watering can, she poured cool droplets like a quiet blessing, nurturing the flicker of life buried deep in the soil. Karl stood still, watching her with quiet eyes. Then, with a faint smile, he said, “It looks even more beautiful now. You’ve given it new life.” Brushing her fingertips across the shining petals, Lila whispered, “My flower and I will wait for you, no matter how long it takes.” Karl’s promise was soft but certain. “I’ll come back as soon as I can.” Later, the car door closed with a gentle thud. Karl climbed inside and began his journey northward, the world outside growing smaller with every mile. Lila watched until the car was nothing more than a fading speck on the horizon. Then she turned back, closing the door behind her. Day after day, spring after spring, the roots of the red flower stretched deep into the earth, holding tightly to the soil, keeping the promise alive. The winters were harsh, cold biting against both flower and keeper. Though every effort was made to keep it alive, the warm hands that planted the blossom grew cold in time, but still the flower endured.

The Neighbor of the Valley 292

The Neighbor of the Valley (Inspired by the song of Fairouz — “Ya Jarat al-Wadi”) O neighbor of the valley, O joy, O turmoil of my soul, Your memories and dreams haunt me still, Calling me, claiming my whole. In my dreams as in my waking mind, Your love remains, ever near; And the memories softly resound, Echoes of a past still clear. I passed again by the gardens, So green, so full of life — There where I once met you, Upon that sunlit hill. Faces and eyes smiled upon me, And in their breath I sensed your scent. My weary soul revived at once, She who had mourned her fate Since the day you went. Never before had I known The sweetness of a lover’s embrace, Until the day I gently held you close — You, the red-haired grace, Whose supple form bent softly in my hands, Like a slender branch swayed by the breeze, And whose cheeks, out of modesty, Blushed with tender unease. The language of words fell silent then, Yielding to the speech of eyes; Mine spoke to yours With the passion love implies. The stars and the heavens, our only allies, Bore witness to us before the skies. And when night came, I held you again, Caressed and kissed you Until the breaking of dawn — Before we drifted apart, forlorn. Since that day, there has been no yesterday, No tomorrow, no day after, No time thereafter. The flow of time has ceased forever, And was condensed into that one day — The day I basked in all your favor. Dr Fouad Bouchareb Rabat, October 11, 2025 All rights reserved

Techbio x Africa: Early Movers - part3 353

Early Signs, Real Ventures It's one thing to say the infrastructure and talent are here but the real test is whether it yields actual companies.  And the signs are already showing. A new class of TechBios is taking shape, raising money, and doing the first thing every good TechBio does: … drum roll, you should know it by now…  Building proprietary datasets. The support system is forming too. OneBio, a Cape Town venture studio, closed a $47M Series A to back founders at the biology–technology edge.  Villgro Africa in Nairobi has already incubated 40+ health and life science startups and unlocked $18M in follow-on capital. These are strides stimulate the Techbio ecosystem and in part, to close Africa's translation gap with venture tools. And the startups coming out of this wave are telling. I thought I would share my personal pick here. Start-ups I can map on the playbook trajectory. Yemaachi Biotech in Ghana raised $3M from YC, Tencent, and LoftyInc to build the world's most diverse cancer knowledge base, sequencing samples across the continent to power precision oncology. As founder Yaw Bediako put it:  "We're looking at trying to understand cancer in the African diaspora - African American, Black British, and continental Africans - the first initiative of its scale. You can't say you're studying a disease if you don't include the most diverse population on the planet, which is the Black population."  BioCertica in South Africa, backed by Pronexus and the Gates Foundation's I3 program with a $2.2M seed, runs consumer genetic tests but is really playing the long game of building the first African polygenic risk database.  And Bixbio, part of OneBio's portfolio and an Illumina Accelerator graduate, assembled the largest reference dataset ever from Southern Africa, nearly 400 high-quality genomes across eight ethno-linguistic groups. Even newcomers like Pandora Biosciences are starting on the same path, building chronic disease datasets designed for drug discovery. And just this summer, the signal got even stronger. In June 2025, Revna Biosciences, a Ghanaian precision medicine startup, announced a landmark partnership with AstraZeneca. Within months, EGFR (gene coding for cell growth protein) biomarker testing integrated into Ghanaian cancer centers, oncologists trained in precision protocols, and the rollout of one of AstraZeneca's targeted therapies for lung cancer patients.  For a sub-Saharan market that has historically had near-zero access to this kind of precision oncology, that's nothing short of historic.  As Revna's CEO Dr. Derrick Edem Akpalu put it:  "This collaboration exemplifies how a synergized biomedical ecosystem such as RevnaBio's can help address long-standing institutional voids that have limited access to advanced molecular diagnostics and targeted therapies in this region."  It's a textbook case of a TechBio going from data and diagnostics to being a direct bridge for global Pharma into Africa. None of this is random. Data-first plays are the starting point of TechBio always.  In the West, consumer genomics followed the same arc: 23andMe built a database of 15M genomes, went bankrupt, and still got snapped up in 2025 by Regeneron for $256M because Pharma wanted the dataset.  Tempus, sitting on 20 petabytes of oncology data, signed a $160M licensing deal with Recursion to train AI models for biomarker discovery and patient stratification.  The lesson is obvious: even before a molecule is in sight, the data itself is valuable enough to Pharma. Africa's first TechBios are now running that playbook and they're doing it from the most diverse human dataset on the planet. The Stakes for Africa x TechBio Case Study: 54gene - The Right Start, The Wrong Turn 54gene was supposed to be Africa’s genomics moonshot. Founded in 2019 by Dr. Abasi Ene-Obong, the company set out to fix the glaring gap where less than 3% of global genomic data came from Africans despite the continent holding the greatest genetic diversity on earth. Backed by Y Combinator, Adjuvant Capital, and Cathay AfricInvest, it raised $45M across three rounds and quickly became the poster child for African TechBio. The model at first was exactly what you’d expect from a good TechBio: start with the data. 54gene partnered with 10 of Nigeria’s largest hospitals, built a biobank that grew past 100,000 patient samples, and focused on high-value cohorts like cancer, cardiovascular disease, diabetes, and sickle cell. This was the right first play: position as an enabler for hospitals and research centers, pile up proprietary datasets, and generate revenue through paid Pharma collaborations. In other words, service-led first, platform-led later — the same arc followed by U.S. genomics pioneers like 23andMe. Then came COVID. 54gene pivoted into diagnostics, scaling mobile labs and at one point driving Nigeria’s daily testing capacity from 100 to over 1,000. Revenues spiked — over $20M from COVID testing — but the pivot also pulled the company away from its core playbook. Instead of doubling down on turning its biobank into translational insights with AI, it spun up Seven Rivers Labs, a costly diagnostics arm. The bets didn’t pay off. By 2022, as COVID demand collapsed, 54gene was caught between a fading diagnostics business and a stalled genomics mission. Layoffs, valuation cuts, and boardroom fights followed. In 2023 the company shut down operations; by 2025, its assets, including the biobank of 100,000 Nigerian genomes were up for sale at just $3M, before a Lagos court froze the deal amid lawsuits between founder and investors. The story matters because it shows how fragile the trajectory can be. Imagine if instead of diagnostics, 54gene had invested its datasets into AI models to map dosage differences for African populations, identify new drug targets, or partner on stratified clinical trials. That’s the road from platform to assets, the road that makes a TechBio a unicorn. Dr. Ene-Obong seems to agree. His new company, Syndicate Bio, is now doubling down on the same thesis but with AI built in from day one partnering on cancer genomics in Nigeria and aiming to turn Africa’s diversity into global drug discovery. It’s the continuation of the playbook 54gene set in motion, but with the missing piece restored.

TechBio x Africa Manifesto: The Edge - part2 355

In Africa The Bottleneck Was Always Here And Now There a Real drivers for change Translation is now recognized as the great bottleneck of drug discovery worldwide. But in Africa, it has always been the bottleneck.  Not in developing drugs, but in applying them.  Most medicines were discovered and validated elsewhere, then imported with little understanding of how African populations would metabolize or respond to them. The result is a structural mismatch: Africa accounts for 18% of the global population and 20% of the disease burden, yet fewer than 3% of clinical trials take place on the continent, most of them concentrated in South Africa and Egypt. This gap is not trivial. Drug absorption, distribution, metabolism, and excretion (the ADME framework) are heavily influenced by genetic variants, especially in liver enzymes like CYP-450, which remain poorly characterized in African populations.  In theory, Africa's extraordinary genetic diversity should have been a global advantage for understanding variability in drug safety and efficacy. In practice, it was ignored.  As Professor Kelly Chibale of the University of Cape Town has argued:  "If you really want to have confidence in a clinical trial, it must start in Africa. Why? If it works in Africa, there's a good chance it'll work somewhere else, because there is such huge genetic diversity." Then came COVID-19. The pandemic was a turning point, mobilizing governmental, NGO, and international funding to build sequencing labs, train scientists, and set up data infrastructure.  In my opinion, the Africa Pathogen Genomics Initiative (Africa PGI) became emblematic of this shift.  The first 10,000 SARS-CoV-2 genomes from Africa took 375 days; the next 10,000 just 87 days; the following 10,000 only 24 days. Today, all 54 African countries have sequencing capacity, and African scientists identified two of the world's five variants of concern.  For the first time, Africa showed it could operate at global pace when given the tools. These investments were catalytic and revealed what had long been latent:  Africa is not just a recipient of medicines but a potential engine of translational science.  The infrastructure layer, built with public and philanthropic support (like the Bill and Melinda Gates Foundation), is now enabling a broader ecosystem: regulatory frameworks like the Africa CDC and the African Medicines Agency, scientific hubs such as H3D in Cape Town, and new hardware capacity supported by corporates like Thermo Fisher's Centre for Innovative Research in South Africa. From here, the snowball is rolling. What began with genomics is already extending across the translational stack. In Ghana, new medicinal chemistry capacity has positioned the country as only the second on the continent (after South Africa) able to run early-stage compound design, linked into the pan-African Drug Discovery Accelerator. This is big, because the continent can now de-risk potential assets. Pharma is of course watching closely. Roche's African Genomics Program is sequencing tens of thousands of African genomes through local biobanks. Sanofi's partnership with DNDi shows how compounds de-risked in Africa can enter global pipelines.  And demographics strengthen the logic: Africa's population is set to nearly double by 2050, while non-communicable diseases like diabetes, cardiovascular disease, and cancer will become leading causes of death by 2030 which is the same conditions driving Pharma pipelines worldwide. The Continent Is Full Of Bright Tech Minds But data infrastructure alone is not enough; translation also depends on whether there is talent capable of making sense of the data.  COVID revealed this too: it was an African-born (Tunisia) AI company, InstaDeep, that helped BioNTech build the Early Warning System able to flag >90% of WHO-designated SARS-CoV-2 variants an average of two months before their official classification.  The company had already been working with BioNTech on personalized cancer vaccines, and post-acquisition it continues to run as an independent AI lab powering BioNTech's drug discovery, improving AlphaFold-like protein folding in immunology to designing next-generation mRNA cancer vaccines.  The $700 million acquisition in 2023 was not only the largest AI deal outside the U.S. at the time, but also a watershed moment for the continent. As co-founder Karim Beguir put it in a recent podcast interview:  "our initial motive was to prove that young Tunisians, young Africans could innovate and compete at the highest level" The significance goes beyond one company.  It validated Africa's AI talent density, which is being built from the ground up through grassroots, community-led efforts. Initiatives like Masakhane, a volunteer-driven movement advancing natural language processing for African languages, or Deep Learning Indaba, cited globally as a model for how to mobilize a continent around machine learning, are emblematic of this bottom-up energy.  I saw it myself at Applied Machine Learning Days Africa 2024 in Nairobi, where more than 3,000 participants gathered across three days mostly researchers, innovators, and students taking responsibility for local problems and showing how AI can answer them.  This effort-led culture is now being matched with hardware too infrastructure. Microsoft has launched its first Azure cloud region in South Africa, enabling GPU-grade compute to stay on the continent, while Nvidia and Cassava are building an AI factory in Johannesburg, with expansions planned for Kenya, Egypt, Morocco, and Nigeria.
medium.com/@kamil.seg/feea16383b...

His Majesty King Mohammed VI: A Style Rooted in Responsibility, Justice, and Development for All 359

Faithful to the line and logic he has established since the first day of his reign, His Majesty King Mohammed VI has once again confirmed his style. “Style is the man himself,” said Buffon in his famous speech at the French Academy in 1753. By this phrase, Buffon meant that style reflects the personality, thought, and sensitivity of the one who writes or speaks. In other words, the way ideas are expressed is as valuable as the ideas themselves, because it reveals, in a noble sense, what the man truly is: his character, rigor, taste, and intelligence. This reflection came to me from the very first steps of His Majesty as he descended from his car. His step is firm and his gait serene. He heads towards what represents a strong symbol of modern Morocco: the Parliament. The place where once a year the royal institution, the representatives elected by the people, and the government meet. An annual meeting that serves as a powerful symbol of the functioning and solidity of the country, just as Moroccans wished in 2011. All the country’s vital forces are there. His Majesty greets those present, all dressed in white, a symbol of purity. They scrutinize his gestures and hang on his words, their breaths low or heavy. The moment is serious. Eyes lower. Ears try to catch every word. Minds are focused. From the first words spoken, Buffon’s maxim is reversed: “Man is style.” The aphorism opens up another field of interpretation, perhaps more modern: style also shapes the man through education, culture, elegance in language and appearance. This is what was offered to us. His Majesty King Mohammed VI holds a fundamental conviction: institutions. Everything must happen within institutions and come only through institutions. On this October 10th, he reiterated this without ambiguity and with no roundabout phrasing. The words were finely chosen, but the speech was direct. Five key words will resonate beneath the beautiful dome. They will swirl above the heads of our valiant deputies and ministers throughout a full legislature: 1. Responsibility: His Majesty the King insisted on the seriousness and sense of duty of parliamentarians and the government in the final legislative year, emphasizing the necessity to act with integrity and efficiency in the service of the homeland. 2. Social Justice: A reaffirmed priority to fight inequalities and guarantee fair living conditions for all Moroccans, in line with national economic projects. 3. Reforms: A call to complete and accelerate ongoing structural reforms to consolidate the Kingdom’s democratic and socio-economic achievements. This is a key message of the speech. 4. Unity: The Sovereign launched an appeal for unity and the mobilization of all energies to defend the higher interest of the Nation and strengthen social cohesion. 5. Transparency: The promotion of transparency and citizen communication around public initiatives is highlighted as a key factor for trust and good governance. The royal speech of October 10, 2025, delivered by His Majesty King Mohammed VI before the Moroccan Parliament, marked a turning point full of hope and commitment for the final legislative year. The Sovereign strongly recalled the importance of “seriousness and sense of duty for the Nation’s representatives,” calling to “complete ongoing reforms, accelerate project implementation, and remain vigilant in defending citizen causes, while prioritizing the general interest.” One of the key elements of the speech is the undeniable coherence between economic ambitions and social programs. The Sovereign emphasized that there could be no contradiction between these two fundamental dimensions, which must imperatively “converge to improve the living conditions of all Moroccans and ensure balanced territorial development.” This vision underscores the royal commitment to build a Morocco where economic growth rhymes with social justice. His Majesty also insisted on the need for increased territorial justice, calling for integrated policies targeting the most fragile regions, such as mountainous areas, oases, or expanding rural centers. This approach aims to “facilitate access to services and stimulate local development,” while emphasizing “the importance of sustainable coastal management,” hinting at an ecological dimension and the possible threat of industries. These measures reflect a strong will for equity and territorial solidarity. In a spirit of unity, the Sovereign made a vigorous appeal for the mobilization of all actors, urging deputies and institutions to “mobilize all their energies in the supreme interest of the Nation” and to promote “transparency and citizen communication around public initiatives.” Facing the challenges, this unity is presented as a necessary force to support reforms and ensure the country’s sustainable progress. The speech fits a positive logic of institutional continuity, rigor, and collective ambition, making Morocco a “fairer, more modern and solidarity-based country.” Despite a national context marked by social movements, the royal message remains focused on constructive dialogue, fighting inequalities, and trusting institutions. This speech is thus a clear roadmap for a Morocco progressing with responsibility and justice, driven by an ambitious vision for a shared future. It confirms the style of a monarch adored by a people aware that everything must happen within institutions, in accordance with the constitution desired by the people’s will in 2011. Faithful to his convictions and his supreme mission as Commander of the Faithful, he recalls: “Whoever does the weight of an atom of good will see it, and whoever does the weight of an atom of evil will see it.” (Surah Az-Zalzala, verses 7 and 8). Az-Zalzala means “the great earthquake.” These verses express that nothing escapes divine justice: every act, no matter how small, will be accounted for on Judgment Day. The Sovereign’s choice is not accidental. Firmness is present. Isn’t he here making an extrapolation beyond the circumstance, in the most solemn context, to remind everyone of the imperative accountability and the firmness awaiting the corrupt and the deviants? These were the last words of His Majesty before this parliament, before concluding, and they are heavy, very heavy with meaning. The Monarch speaks little but says everything clearly and calmly. That is his style.

Techbios x Africa : The manifesto part 1 358

Closer to Humans: The Next Big Opportunity in TechBio: Hitting Eroom's law in translating assets to clinics If Moore's law promises exponential gains from technology, Eroom's law (Moore spelled backwards) reminds us that drug discovery has stubbornly resisted that curve. For decades, the cost of bringing a new drug to market has roughly doubled every nine years, even as compute and data scaled exponentially. AI-driven TechBios were supposed to break this trend and accelerate discovery, lower costs, and flood the pipeline with new medicines. In its early day's, Recursion was going with something like a 100 drugs in 10 years. And to some extent, they have delivered. Programs from Insilico or Recursion show how AI can compress preclinical timelines from five years down to 18–30 months. Costs are lower, throughput is higher, and in silico tools have expanded the space of molecules Pharma can explore.  But reality is that most AI-first drugs are still aimed at well-known targets, and once they reach the clinic, they face the same bottlenecks as traditionally developed drugs. Phase II proof-of-concept success rates hover at ~40%, unchanged. Back to Eroom's law in action, the bottleneck has shifted downstream. The graph from speed invest tells the story nicely. Early discovery (target validation, compound screening, lead optimization) accounts for ~25% of costs, the bulk of time and money is lost in Phase II and Phase III, where failure rates spike and costs per molecule can exceed 20–25% of the total. Functional Data Is the Missing Piece Why? Because our translational models are still inadequate proxies for human biology. Drugs fail not because they weren't optimized enough in silico, but because they don't behave as expected in humans, showing weak efficacy, unexpected toxicity, or adverse effects that outweigh benefits.  Conversely regulators are now pushing for more personalized approaches: genotyping, deeper disease phenotyping, and companion biomarkers to better stratify patients. That means the next opportunity isn't about yet another molecule generator. It's about building the translation layer: generating functional, human-relevant data at scale.  Two pillars stand out: Bench side. New experimental systems like organoids and organ-on-a-chip can capture human biology more faithfully than animal models, giving us early readouts of drug response in tissue that resembles real patients. it can be high-dimensional functional data (cells content imaging) Bedside. Richer molecular profiling of patients to capture complete responses to interventions across all biological layers. The omics data, reflects physiological responses from the gene expressed to the protein inhibited till the end metabolite produced. This is the frontier TechBios have yet to tackle.  Proprietary datasets from in vitro, in vivo, or in silico work aren't enough, because by design they remain at a distance from real human complexity.  Reminder, the demand is still there as the patent cliffs of 2030 are not going anywhere. The Funding Gap: Bench Traction, Bedside Wide Open The common denominator in TechBio is always the same: proprietary datasets. On the bench side, we're already seeing how this can play out. Just last month, Parallel Bio raised $21 million to push forward a new model for immune drug discovery. Their platform combines organoids and AI is set to generate massive proprietary datasets of immune responses. This 'Immune system in a dish' allows simulate how drugs behave across populations and verify candidates in vitro before they ever enter the clinic. The company dates back to 2021, but recent series A show their gearing up for growth and points to serious answers to the translation problems from Capital Interest. The story on the bedside is very different. Here, the prerequisite is well-characterized patient data of omics like genomics, proteomics, metabolomics, deep clinical phenotyping. Not really the type of data you can engineer in your lab with enough wetware and hardware. Pharma companies guard their clinical trials data as part of their asset. Biobanks have the scale needed but primarily share it with research partners and academics or monetizes them directly, selling access to screened samples and metadata at high prices. Their funded by goverments and charitable organizations around projects with defined partners within a consortio that have their for privilege access.  Hospitals typically generate only small, fragmented cohorts a few hundred patients, often disease-specific and far from the scale needed to train robust models.  And once TechBios push into later stages like preclinical or Phase I, costs spike: recruiting patients, managing trial sites, and running protocols and more tailored to big Pharma economics.  In the West, shrinking patient pools for many chronic diseases add yet another barrier driving the cost further up. This imbalance explains why most visible TechBio innovation so far has come from the bench. Benchside players like Parallel Bio are proving you can generate your own data and own the feedback loop.  On the bedside, by contrast, barriers remain high and that leaves the space wide open.  The real question is not if bedside innovation will emerge, but where. And it may well be that the answer lies outside the traditional Pharma hubs
medium.com/@kamil.seg/feea16383b...

TechBios: The Playbook 359

The SaaS Playbook Enters Pharma At start, TechBios bore the heavy upfront costs of architecture design, large-scale data acquisition, massive training runs, and inference, all to learn new principles in biology and deliver them as platforms Pharma clients could use for better drug design. This unlock was driven by compounding forces. On the tech side, models improved as they scaled in size and input, while compute and storage costs fell (Moore's Law at work). On the bio side, labs and instruments achieved higher throughput, producing exponentially more data at lower cost - the Carlson Curve in genetics being the best-known example. (Sequencing your whole genome cost ~$10 million in 2007; ten years later, it was under $1,000.) On the demand side, techBios emerged at a time when the status quo relied on rule-based computational methods grounded in rigid theoretical models. These could only handle a limited set of parameters, making it difficult to experiment broadly and ultimately constraining R&D pipeline output. Put in perspective, the stakes of this inefficiency are massive: 69 blockbusters will face patent cliffs by 2030, putting around 236 bio USD at risk. As Manuel Grossmann the Founding Partner of Amino Collective (Health x Bio Fund in Europe), notes: "The TechBio space benefits from two fundamental tailwinds: technological advancement and market demand." For tech investors, the story clicked. These companies weren't tied to one risky therapeutic bet; they looked like horizontal software platforms that could scale across the entire industry. TechBios offered Pharma innovation closer with simpler unit economics clean, recurring revenues, faster adoption curves. As Cradle, using language models for protein design puts it: "One annual software license. No hidden fees." No surprise then that capital rushed in. In 2021, right at the cusp of this wave, VC investment in TechBios hit $2.4 billion, with mega-rounds north of $100M backing the promise of programmable biology. … Opposed To The Longstanding Asset Deal Until then, most real Pharma innovation was coming from a fundamentally different breed of companies with a focus much narrower and longer time horizons for product market fit. Biotech companies select a well-studied biological target, develop a molecule against it, and march it through the clinical gauntlet. Their path to value creation is very interactive, reducing uncertainty every step of the way and focusing where prior knowledge give a fighting chance.  Their revenues are therefore less predictable, making take asymmetric bets requiring incredibly specialized knowledge and experience. Headlines in the domain are hence quite binary. You get the 1 trio USD added Market Cap to Novo from from the GLP-1 of Embark and or failures of expected block busters for Alzheimers. Pharma companies are in that sense expert in M&A deals for to power their innovation, estimated 65% of its revenue come from these operations. The size of deals made here are often quite substantial, getting back to Car T in the 2010, Roche bought Poseida Therapeutics, a San Diego-based for US $1.5 billion November last year. … Moving into Co-Development And Blurring The Lines As the TechBio field matured, one lesson became clear: benchmarks alone aren't enough.  Validation metrics carefully crafted to showcase model performances gets the initial traction but Pharma ultimately values assets, and without them TechBios struggle to show true impact.  The economic logic makes the difference obvious. A pure platform play might reach a few hundred million in enterprise value. But the real butter in this industry sits with assets, Pharma's trillion-dollar market cap rests on drugs that make it through the clinic. Without assets, TechBios miss the home run and risk falling outside the venture playbook entirely. This is what pushed the industry toward co-development. Instead of selling platforms as tools, TechBios began striking deals that shared both risk and upside: upfronts, milestone payments, royalties. Late exemple of this is Creyon Bio AI signing 1 bio USD in milestone deal with Lilly. As Manuel Grossmann of Amino Collective puts it again: "Focusing purely on providing tools as products or services can often be challenging, since the exit potential tops out in the low hundreds of millions - often misaligned with the VC model." This is where the difference between Techbio and Biotechs gets blurry. As the former starts developing their own drug running validation, toxicity and even clinical to out-license as assets, the latter becomes more and more tech enabled and building with open source models from industry like RosettaFold. The TechBio Playbook Has Emerged On top of that comes the platform. This is where raw data turns into usable insight. In Recursion's case, it's the RecursionOS, an operating system for biology that fuses automated labs with ML models to map complex biology. That's what Pharma pays for. The economics here look like $150M upfronts, R&D milestones, tiered royalties, exactly the Roche and Genentech partnership structure. At this stage, platforms prove they can de-risk discovery for others. But the real prize sits in assets. Once the platform works, you push it into your own drug programs: new targets, new molecules, lead optimization.  This is where TechBios flip into biotech economics. Out-licensing assets to Pharma brings upfronts plus large milestone packages, and potentially royalties if the drug hits the market. It's higher risk, but it's also where exits climb from hundreds of millions into the billions. That's the sequence: data → platform → assets.

TechBio, A few definitions 360

 Why AM I Writing This? I did not come to TechBio as a distant observer but grew into it. When I was studying life sciences engineering, the early signs of "software eating bio" were just starting to appear. Computational tools were making dents in how biology was done, and for me it was impossible not to be fascinated. Fast forward a few years, and I am now operator inside a TechBio startup (shameless plug) leading AI development. That vantage point is really a strange mix. Some days I get swept up in the hype, convinced the next model drop is going to change everything; other days, the scientist in me wants to push back, to ask for proof, for data, for translation into the clinic. Balancing those two minds: both the early adopter and the skeptic is hard. But it's also what makes TechBio such a fascinating space to build in. And then there's Africa. Coming from the diaspora, I studied and trained abroad, where most of the breakthroughs and AI-driven advances in drug discovery were happening in the West But I kept asking myself: what about here? What about my continent? At first, it felt like we were always on the receiving end of innovations born elsewhere. But as I dug deeper, I realized something the very bottlenecks I was seeing firsthand inside TechBio like the gaps in translation, the missing data closer to humans, well that is were exactly where Africa holds an unfair advantage. That's what this deep dive is about. Not a hype piece, not a catalog of every new startup, but an attempt to map the playbook, show where TechBio has already delivered, and point to the next frontier → one that may well be written in Africa. If you're an investor, I want you to come away with clarity on what makes a TechBio defensible, where the real opportunities lie, and why the continent is positioned for outsized returns. If you're a scientist, founder, or operator, you'll find the logic of the playbook, examples of what works (and what doesn't), and maybe a spark for your own next venture. The full story starts just ahead. But first, let's look at the latest wave, AI agents and what they tell us about how fast new technology moves from silicon into cells. TL;DR TechBio has gone from hype to playbook: data → platform → assets. The next bottleneck is translation: generating data closer to humans. Africa holds the unfair advantage to solve this, thanks to its diversity, newly acquired infrastructure, and emerging research ecosystem. Companies are already being built on this frontier, clear venture opportunities exist, exemples of exits and more investors should catch up. Not TechBio Yet But Another Reminder Pharma Can't Escape the Tech Cycle I f you want to know where the next disruption in pharma will show up, follow the broader tech cycle. Every new wave of technology now leaves its mark on the industry. In the age of Tech x Bio, there's nonstop traffic between silicon and cells: cloud, machine learning, robotics and now, AI Agents. I see this almost daily. My feed is flooded whenever a new model drops or a product launches. At first it feels like a headline meant only for the tech crowd. But give it a few months, and suddenly that "just another AI update" is wired into pharma workflows with Paul Hudson, CEO of Sanofi, giving full interview to McKinsey on how transformative it is for the industry. One thing is clear: whether in discovery, trials, or manufacturing, the two domains have become inseparable. A nice review on comes straight out of MIT to help bring perspective. YC's (famous startup incubator from San Francisco) track record makes this pattern visible. They were early to back today's main players when skeptics thought they had it figured out. Companies like Ginkgo Bioworks and Atomwise (more on them later) proved computation could be foundational to biotech and Pharma. Now YC is backing AI Agent startups, showing once again how quickly a new stack of technology crosses into Pharma. And if you thought leaders like Hudson were just posturing as "tech-savvy" with hyped tools, consider Benchling. One of the most established TechBio incumbents, it recently acquired Sphynx, an AI-agent startup focused on streamlining hypothesis generation and analytics in discovery. By weaving these capabilities into its stack, Benchling reinforced that this isn't a passing experiment, it's another layer becoming part of the system. Now, let's be clear. AI Agent companies are not "TechBios" in the sense of this deep dive. They are, for now, tools or orchestration engines that Pharma teams can plug in to augment their workforce and automate tasks once dauntingly manual (and there are many, sometimes not the once you expect… like procurement). They represent the kind of technological spark that helps scale approaches to well-known problems in drug discovery. And while every new wave of technology makes a dent in Pharma, the hundred billion unlocks usually lie elsewhere, closer to solving the big bottlenecks of how drugs are discovered, tested, and developed. That's where TechBios come in, and where we'll turn next. For now, think of this section as the apéro: the first cracker to show how the rules of the game have changed. How TechBios Create (and Capture) Value If you perplexity (we don't "Google" anymore in the age of AI) the definition of TechBio, you'll either get flooded with abstract jargon that means little if you're not steeped in the field, or a description so simple it could apply to almost anything. Neither helps much. A more pragmatic lens is to look at TechBios through their value proposition. And this is where it gets interesting. TechBios have been around for over a decade, and in that time their offerings and therefore their positioning in the industry have shifted dramatically. A more pragmatic lens is to look at TechBios through their value proposition. And this is where it gets interesting. TechBios have been around for over a decade, and in that time their offerings and therefore their positioning in the industry have shifted dramatically.

The One Tormented by Love 425

The One Tormented by Love He whom love is nothing but torment and cries, Whom sleep abandons and flees, Whose endless tears touch all who see him suffer. His wounded and tortured heart knows no respite, And his bruised eyelids remain open forever. The leaves tremble beneath his sighs, And the stone melts under the weight of his groans. He speaks to the stars, Telling them of his misfortunes, His cries and his sorrows… In vain. He ends up tiring them, They slip away and abandon him, Leaving him motionless and weary, Yet awake, gazing beyond. Yet every tearful admirer Would dream that her hands could brush him, Touch him, Behold him, And love him. His eyes denied the blood he shed; Would his face deny his pain as well? When his witnesses of love left him without honor, He displayed his cheek so that it could bear witness in their place. Between her and him, love is a solid bond, Impossible to break or tarnish. Why then so many reproaches That open to him the door of oblivion, Only to slam it forever in his face? Dr. Fouad Bouchareb Inspired by the song of Mohammed Abdelouhab "مضناك جفاه مرقده" All rights reserved – October 10, 2025 https://youtu.be/-GHCmtjiygw?si=Qpt_iVR9hWrdSqK8