That bothered me because I should have seen it earlier. I write about African stablecoins. I know the Nigeria story. I know the South Africa story. But my own coverage had followed the louder network, not the larger float.

So I re-ran the queries this week.

The data gave me the old exchange-operator split in a new wrapper: South Africa has the money. Nigeria has the people. But the deeper story is that neither side has the full market yet.

The gap is real

As of June 12, every ZAR stablecoin combined holds about $6.08 million across 6 chains. Every NGN stablecoin holds about $1.90 million (my Dune query, re-run June 12). South Africa’s rand float is 3.2 times Nigeria’s naira float.

Most of that rand supply is ZARP, around $4.5 million by itself (CoinGecko), which makes it roughly two and a half times cNGN alone. And here’s the uncomfortable part: I know Simon Dingle. I know ZARP, live since 2019, reserves managed by Old Mutual Wealth (ZARP, BitKE), with real volume on Base, the chain I spent my Coinbase years growing. The miss is mine. cNGN has the consortium, the SEC story, the press cycle, my friends at Convexity in my feed and my DMs, so cNGN is what I wrote about. ZARP spent those same years shipping instead of campaigning, and it out-floated the token everyone I know talks about. Proximity is an algorithm too. Mine had me covering the smaller float.

Coverage in this category tracks the narrative and the network, not the supply. That was going to be the whole piece. Then I looked at the deltas.

The gap is closing

The rand float barely moved. The naira float grew 23 percent in under a month, and its holder count grew at the same pace. The weekly supply curve says the same thing in naira terms: ₦2.03 billion on May 19, ₦2.50 billion today, with Base’s share widening to 86 percent.

Run the arithmetic forward and it gets interesting. If both kept their current pace, the naira float would pass the rand float in about six months. That’s a projection of two noisy curves, not a prediction. But the direction is unambiguous: the quiet incumbent is static, the covered newcomer is compounding.

The holder mix tells you why these two floats feel so different. South Africa’s 1,170 holders average about $5,200 each. Nigeria’s 4,481 average about $425. One looks like a handful of treasuries and trading desks holding rand on-chain. The other looks like the front edge of a retail base.

That line is easy to turn into banter because Lagos and Johannesburg have been arguing for a generation: economy, music, football, migration, whose companies make money where. Some weeks the argument is harmless. Some weeks it is not. This is one of the bad weeks; the repatriation flights out of Johannesburg began Thursday. But the stablecoin data is useful precisely because it cuts through the noise. It shows two markets that look opposed in narrative and complementary in structure.

Then the whale data complicates everything

In May I set a test for cNGN, and I wrote it down so I couldn’t wriggle out of it later: wallet count crossing 10,000 with whale share dropping below 85 percent means real adoption; wallet count growing while whale share stays pinned means “the treasury layer is carrying the entire chart by itself.”

I re-ran the holder distribution this week. Wallet count: 3,529 then, about 4,370 now, up 24 percent in under a month, on pace from the 832 of last August. The retail half of the test is passing.

The whale half failed. Whale share didn’t drop toward 85 percent. It went up: 92.3 percent to 93.8 percent, and the whale count went from 16 wallets to 20. Thirteen of them sit on Base holding 81.7 percent of everything.

So the 23 percent monthly growth that’s closing the gap with South Africa is mostly treasury mints at the top, not the 4,100 small wallets at the bottom. Add up everything below the whale tier, the sharks, dolphins, crabs, and shrimps, and retail-sized holders own about 6 percent of cNGN’s float, roughly $112,000. The shrimp wallets I defended in May as real Nigerian retail, ₦5,000 to ₦15,000 each, are still arriving, faster than ever. They just don’t move the supply chart. Twenty wallets do.

Which means the honest version of this piece has three layers, and each one undercuts the last. South Africa’s rand float is bigger, but it’s sitting still. Nigeria’s naira float is compounding, but the compounding is whale-driven. And the retail adoption that would settle the argument, thousands of ordinary wallets holding local-currency stablecoins they actually use, hasn’t happened at scale in either country. Both floats together are under $8 million, in a continent where USDT on Tron moves billions. The local-stablecoin race is still two pilots arguing about pole position.

What I’m watching

Four things, with numbers attached.

First, the crossover. If NGN supply passes ZAR supply by year-end, the regulatory-first model will have beaten the distribution-first one on the only public scoreboard, and I’ll write that piece. If the naira curve flattens once the current mint cycle ends, May’s “treasury cycle” reading wins instead.

Second, whale share against my May line. It’s at 93.8 percent and moving the wrong way. The test stands: below 85 percent with wallets past 10,000 and cNGN’s story is on. The wallet half is on pace for early 2027. The whale half is the one to watch.

Third, whether ZARP wakes up. A new rand stablecoin, ZARU, launched in February with heavyweight backing, marketing itself as South Africa’s first despite ZARP’s seven-year head start. Nothing moves an incumbent like a funded challenger. If ZARP’s flat curve bends in the next quarter, that’s why.

Fourth, the one that would actually matter: a consumer integration. An Opay or PalmPay putting cNGN inside a 50-million-user app, or a South African bank putting ZARP or ZARU in front of retail. Either event makes every number in this piece look quaint. Neither has happened. The first one to land decides which country’s model gets written about as the one that worked.

I went looking for a winner between two African stablecoin models. The data handed me something more honest: a bigger float that doesn’t grow, a growing float that isn’t retail, and a market where the real competition, dollar stablecoins on cheap rails, is lapping both. And underneath the scoreboard sits the quiet structural fact the noise obscures: each float is shaped like the other’s missing half. Nigeria’s growth needs the balance-sheet depth South Africa already has. South Africa’s still treasuries need the crowd Nigeria already has. The first builder who treats Lagos and Johannesburg as one financial corridor wins the market both countries are failing to build alone. The story and the supply have come apart in this category. I’d rather keep reading the supply.

Disclosure: I don’t hold ZARP, cNGN, or ZARU, and haven’t in the past 12 months. Currency-level supply and holder figures are from my own Dune query (7542599), re-run June 12, 2026, against its May 19 run; cNGN weekly supply and holder tiers are from Dune queries 5950869 and 5953945 by intellisages_hq, re-run June 12. ZARP’s standalone figure is from CoinGecko, June 12. Holder counts are wallet addresses, which can overcount people and include dust. Transfer velocity for both tokens isn’t cleanly public at this size and is flagged as a gap, not estimated. One operator reading his own queries, not investment advice.

RLUSD has about $1.81 billion in circulation. Of that, roughly $1.08 billion sits on Ethereum and $731 million on the XRP Ledger (DefiLlama per-chain data, which matches a direct read of the issuer’s on-ledger balances on the XRPL to the decimal, June 2, 2026). Sixty percent of the stablecoin Ripple issues is on Ethereum, the chain Ripple has positioned against for years. The dollar moved to the neighborhood Ripple doesn’t own.

I don’t read that as a stumble. I read it as the most honest thing in the whole RLUSD story, because of where the rest of the spending is going.

The dollar follows the distribution

A stablecoin lives where its distribution is, and Ripple is buying distribution into institutions, hard. In January it put $150 million into LMAX Group and signed a multi-year deal to make RLUSD a core collateral and settlement asset across LMAX’s institutional venues, spot crypto through perpetual futures and CFDs. LMAX ran $8.2 trillion in institutional volume in 2025. In May, Copper, one of the larger institutional custodians, added RLUSD to its platform and to a yield program for the funds that custody there. And the institutional money, the prime-broker collateral, the DeFi venues, the integrations that LMAX and Copper plug into, all of that lives on Ethereum and its EVM cousins, not on the XRP Ledger.

So the supply split is the receipt of a distribution strategy, not a popularity contest between two chains. Ripple is paying to wire RLUSD into the places institutions already operate, and those places are EVM-native, so the dollar pools where the integrations are. Sixty percent on Ethereum is what it looks like when an issuer chases institutional rails instead of defending its own chain.

That pattern shows up in my own backyard too. Ripple’s custody technology went into Absa Bank in October 2025, one of Africa’s largest banks, Ripple’s first major bank-custody partner on the continent, serving institutional clients across roughly a dozen countries. People will file that under “Africa story,” but it’s the same move as LMAX and Copper, pointed at a different institution: sell the rails the money already runs on. The throughline is a customer, not a region, and the customer is the institution.

What distribution actually takes

I spent the last few years running ecosystem and distribution growth for Base, so this is the part I can say with some authority: an issuer does not get to choose where its token lives. The integrations choose. You can mint on whatever chain you want, but the supply gravitates to wherever the wallets, the venues, and the collateral engines already are, and getting into those is slow, expensive, one-counterparty-at-a-time work that almost nobody outside the function sees.

Ripple had the option most issuers would kill for. It owns a chain. It could have forced RLUSD onto the XRP Ledger, pointed at a big on-ledger number, and called it adoption. It didn’t. It went where the institutions were, which meant Ethereum, and it paid for the privilege, $150 million into a single venue is not a press release, it’s a budget. That’s the unglamorous truth of distribution: you go to the user. The user does not come to you, no matter what chain you built for them.

So what is the XRP Ledger for

The 40 percent on XRPL isn’t dead weight, and the supply number alone won’t tell you what it’s doing. So I pulled the on-chain activity for both chains to see how the dollar actually moves. It moves in opposite ways.

On Ethereum, RLUSD changed hands across about 8,800 transfers in the last seven days, roughly 1,250 a day (Dune, reading the ERC-20 transfer logs). On the trading side it does around $15 million of DEX volume a day, but across just 195 trades, an average of about $78,000 each (DexScreener, June 2). That’s institutional-size: a handful of large blocks, RLUSD against USDC on Uniswap and Curve. The XRP Ledger does a comparable $8.5 to $9.6 million of DEX volume, but across thousands of small trades. DexScreener counts 10,760 of them at about $790 each; On The DEX, the XRPL-native tracker, counts more still, over 25,000 trades across eighteen pairs at roughly $380, traded against a basket that includes XRP, Brazilian real, yuan, and USDC (On The DEX). Same dollar, two different motions: big and rare on Ethereum, small and constant on XRPL.

That answers the velocity question I walked in with, and not the way I expected. Normalize each chain’s trading to the supply sitting on it and the two come out close, Ethereum turning over about 1.4 percent of its balance a day and XRPL roughly 1.2 to 1.3 percent. The comfortable story, that XRPL is the fast settlement rail doing the real work while Ethereum just parks the balance, isn’t what the numbers say. Per dollar of supply the two chains move at nearly the same speed. Where they split is ticket size, and ticket size is the tell. The large institutional blocks, the collateral and custody balances that LMAX and Copper plug into, settle on Ethereum, where the average RLUSD trade is a five-figure sum. The high-frequency, small-denomination work, thousands of sub-$400 trades a day against a basket of currencies, runs on XRPL, the payments-and-FX profile the ledger was built for. One chain is the vault. The other is the cash register.

One honest limit on that read. What I measured is DEX trading, the order books on each chain. Pure peer-to-peer payment settlement on XRPL, wallet to wallet and outside the DEX, isn’t queryable without running a full ledger node, and when I read the issuer’s own transaction history it came back almost entirely market-making orders, not payments. So XRPL’s true settlement number sits somewhere above what I can see here. But the burden of proof was on the idea that the XRP Ledger moves the dollar faster, and the visible data leans the other way. What the ledger clearly is, for its own home dollar, is busier and smaller.

What I’m watching

Three things from here.

First, the direction of the migration. In December 2025 Ripple announced RLUSD’s move to four Ethereum layer-2s, Optimism, Base, Ink, and Unichain, issued natively across them through Wormhole’s transfer standard. It hasn’t shipped on-chain yet, the rollout is still in testing and waiting on NYDFS sign-off, which is why the live supply is still only Ethereum and the XRP Ledger. But all four are EVM rollups that settle back to Ethereum, so when they go live they add to the Ethereum side of this split, not the XRPL side. The announced direction is more EVM, not a turn back to the home chain.

Second, ticket size over time. Today Ethereum is where the large blocks settle and XRPL is where the small, frequent trades run, the vault and the cash register. If RLUSD is going to be a payments dollar and not just an institutional one, the bigger tickets should start showing up on the XRP Ledger too, not only on Ethereum. I’ll be watching whether the average XRPL trade creeps up, or whether the two chains stay split the way they are now.

Third, and this is the strategic one, what the XRP Ledger is becoming to Ripple’s own dollar. The busy retail and currency-trading layer, thousands of small tickets a day, while the institutional weight settles on Ethereum? For a company whose entire identity is its ledger, that’s not a small question. The dollar already voted with its feet. The question is whether Ripple admits where it walked.

Disclosure: I previously led Africa and Ecosystem Growth for Base at Coinbase. I hold no position in RLUSD, XRP, or Ripple as of writing. Supply figures are direct reads from DefiLlama‘s per-chain data and the RLUSD issuer’s on-ledger balances on the XRP Ledger. Activity figures are mine: a Dune query on Ethereum transfer logs, and DexScreener plus On The DEX for per-chain DEX volume and trade counts. All as of June 2, 2026; deal terms are from the reporting linked above. Pure peer-to-peer payment settlement on the XRP Ledger outside the DEX isn’t cleanly queryable without running a full ledger node, and is flagged as such in the piece, not estimated. One operator reading the on-chain data, not investment advice.

Nathan Allman, founder and CEO of Ondo Finance, died unexpectedly on May 26, 2026. He was 32. Four years after founding the company, he left behind a tokenization platform holding about three and a half billion dollars in US Treasuries, dollar-yield products, and stocks.

He also left behind something stranger and more interesting: a brokerage explicitly built for people who aren’t in the United States.

Why Ondo existed

Call her Wanjiru. She’s 24, lives in Kileleshwa, works as a product manager at a fintech in Westlands. She’s a composite. Every number in her situation is real. She has about $4,200 saved across a shilling money market fund, a small dollar balance at KCB or Equity, and a chama she can’t easily withdraw from. She wants to own a piece of something that grows. The AAPL she sees in headlines. Maybe NVDA. Maybe just the S&P 500.

In Nairobi, that ambition runs into a wall.

Every path to US stocks costs and waits. The local platforms either give her a basket she didn’t pick or a wallet model with deposit, FX, and redemption friction at each step. The Nigerian-anchored apps expect a BVN she doesn’t have. The international brokers want an international wire, a W-8BEN, and a level of capital-leaves-the-country attention her bank would rather not surface. Every layer takes a cut. Every layer takes a week.

So she does what most of her cohort does. She puts the $4,200 down on a deposit for a quarter-acre in Kitengela, two hours from town. She’ll pay 12,000 shillings a month for the next seven years. The plot is empty. Goats sometimes wander across it. She visits on weekends. She tells her mother, “this is my investment.”

Land doesn’t fractionalize. Land doesn’t pay yield. Land sits.

Most founders in US fintech and crypto would never see Wanjiru. Their addressable market starts in Manhattan and ends in San Francisco. The American consumer is the easiest in the world to underwrite, the compliance frame is mature, the rails work, the dollar is already the local currency. You can charge a 30 basis point margin on a treasury fund and have venture capital arguing for your seat at the table.

Allman went the other way. Ondo Global Markets is explicitly not available to US users. His treasury fund OUSG is gated to qualified institutional clients, but his retail-facing USDY and his tokenized stocks product target investors in Asia-Pacific, Africa, Latin America, and Europe. The product is built outward.

He built Wanjiru something that bypasses the wall entirely. A wallet she already has. A token she can hold. AAPL the way she holds USDT. No BVN. No wire. No W-8BEN. No deposit-FX-redemption cycle. Her phone, a token, and a price that updates with the underlying.

Local players are still in the fight, Ndovu and MyStocks in Kenya, Bamboo and Risevest in Nigeria, EasyEquities in South Africa, each solving a piece of the problem inside the legacy stack. Allman’s bet was different. You don’t fix the stack. You go around it. A token on a public blockchain, backed by US-custodied stock, isn’t subject to the same friction. The compliance lives in the issuance contract, not in every transaction.

Whether Wanjiru ever opens that wallet is a separate question. The thing Allman built points at the problem nobody else in his seat was even looking at.

What the data shows

Three numbers carry most of the story.

USDY launched in September 2023 on Ethereum and December 2023 on Solana. On-chain, the supply curve looks like the chart above. Ethereum carries the bulk, Solana adds a steady $160 million. Other chains (Mantle, Sui, Aptos, Sei, Noble) bring the total to roughly $1.5 billion by May 2026. The growth wasn’t linear. There’s a wave in mid-2024 to about $250 million, a plateau, then a step-up to $600 million in mid-2025, a dip in early 2026, and a surge to over a billion in the weeks before Allman’s death.

OUSG is the institutional product, now around $693 million in TVL. The on-chain Ethereum-only figure is lower because Ondo migrated significant flow into rOUSG (the rebasing wrapper) and into USDtb, the regulated stablecoin issued through Anchorage Digital under the GENIUS Act. Institutional money is more rotational than retail money. The consolidated treasury exposure is still in the platform, just under different wrappers.

Ondo Global Markets is the newest and the fastest. It launched in September 2025 with around $350 million in TVL within the first months. It crossed $1 billion in TVL by mid-May 2026, eight months from zero. By the time Allman died, the platform was running tokenized versions of NVDA, AAPL, META, TSLA, CRCL, SPY, QQQ, TLT, TIP, AGG and roughly 190 other tickers, available on Solana, Ethereum, and BNB Chain, where the rollout exposed the product to roughly 3.4 million daily active users.

Inside the roughly $19 to $21 billion total RWA market, Ondo’s three and a half billion is meaningful. Inside the tokenized equity sub-category, 70 percent of the supply runs through Ondo’s contracts. One protocol, one founder, one team. The tokenized stock market has a winner-take-most shape that the dollar stablecoin market also has, and the seat Ondo occupies in equities now is structurally close to the seat Tether occupies in dollars: not the only player, but the one everyone else benchmarks against.

The BNB Chain rollout is the part of this story that most US-coded analysts are missing. BNB Chain has the highest daily active user count of any chain that isn’t Solana or Tron, and a meaningful share of that activity is in markets where Coinbase, Robinhood, and Schwab don’t operate. When Ondo Global Markets shipped on BNB Chain, it exposed 200 tokenized US stocks to roughly 3.4 million wallets that were never going to download Schwab. That’s the access bet operationalized.

What he was building toward

The longer arc was bigger than the three products. Allman was building toward tokenized everything: stocks, ETFs, bonds, commodities, with Ondo Chain as the settlement layer underneath. The bet, in his own words, was that institutional-grade financial products belong on public blockchains and that compliance is the moat, not the constraint. Public reporting from earlier this year described the strategic positioning around “global financial access” as a deliberate evolution from the original treasury-tokenization thesis.

The work isn’t done. He had open partnerships with BlackRock‘s BUIDL framework, with Securitize, with custodian partners across Asia. He had Ondo Chain in development. He had a roadmap that named tokenized real estate, tokenized commodities, and tokenized money market funds. He was building a tokenization stack the way Stripe built a payments stack, with the products plugging into one settlement spine.

The platform now has the products it needs to outlast the founder. That’s not a small thing. Most early-stage crypto companies are person-shaped: the founder is the product, the network, and the conviction. Ondo crossed into product-shaped sometime in 2024. The on-chain numbers can keep growing under Ian De Bode even if the conviction takes longer to rebuild.

What happens next

De Bode was Ondo’s longtime president. He oversaw strategy, product, and daily operations. The succession is clean by crypto standards: no founder dispute, no token whiplash, no exit by the senior team. The Ondo press release confirming Allman’s death also confirmed his appointment, with the backing of the leadership team.

The open question is institutional retention. OUSG holds BlackRock-adjacent capital that’s there because of a specific compliance and credit story Allman told. USDY holds regulated counterparties who underwrote Ondo’s controls. Both flows will be tested in the next four quarters by counterparties asking, quietly, whether the founder-shaped trust extends to the new leadership. The early read should be yes given De Bode’s tenure and the depth of the team. But it’s a real test.

The Kenyan retail user on Global Markets won’t notice any of this. The mint and redeem rails keep working. The 200 tickers stay live. The product, for the people Allman built it for, doesn’t change.

A short close

I followed Allman’s work because he was solving a problem most people in his seat didn’t even see. The Treasury tokenization story everyone talks about at conferences was the easy part. The Kenyan twenty-something who buys her first AAPL through a wallet in 2027, instead of putting that money into a half-acre outside Nairobi, is the part nobody else was building for. She won’t know his name. He’d probably prefer it that way.

Disclosure: I don’t hold ONDO, USDY, or OUSG, and I haven’t held them in the past 12 months. All on-chain data is from Dune Analytics, queries 7581475 (USDY Ethereum) and 7581439 (USDY Solana). Aggregate TVL figures are from public Ondo and press-release sources cited above. This is one person reading public data and an obituary on a Tuesday morning.

When Dune launched its Stablecoins collection with Steakhouse Financial in February, the headline feature was attribution: every transfer classified by intent, every address bucketed by use case (CEX, bridge, lending, yield, treasury, whale, and so on). The genuinely interesting story, though, is buried in the metadata. The unified stablecoins_multichain.tokens table is a directory of every stablecoin Dune now indexes, with the ISO currency code each one pegs to. I queried it expecting the long tail to be sparse. It’s worse than I thought.

How big the non-USD universe actually is

The Dune dataset covers 26 ISO currency codes. The breakdown:

• USD: 152 distinct tokens. USDT alone is around $190 billion in circulation, USDC about $79 billion. The biggest of the dollar stables is roughly 120 times larger than every non-USD stablecoin combined.

• EUR: 18 tokens (EURC, EURS, EURT, EURA, EURI, EURAU, EUROe and a dozen more). $1.35 billion in supply, 1.07 million holders. The only non-USD ecosystem that looks remotely like a real ecosystem.

• BRL, ZAR, MXN, CAD, COP, IDR: 3 to 6 tokens each.

• GBP, JPY, AUD, NGN, CLP, RUB, CHF: 2 tokens each.

• CNY, ARS, SGD, TRY, GHS, KES, PHP, INR, KZT, XOF, PEN: one token each. Most are running on a single chain.

Total non-USD stablecoin supply, all 24 currencies that currently have any outstanding balance: $2.96 billion. According to DefiLlama, the global stablecoin market sits around $300 billion in May 2026. Non-USD is therefore about 1 percent of the universe.

The full scoreboard:

The two surprises at the top

Read the top of the table again. EUR and RUB are within $58 million of each other on outstanding supply: $1.35 billion versus $1.29 billion.

EUR makes sense. It’s a $300 billion regulated stablecoin opportunity in waiting, the MiCA framework is now live across the EU, Circle‘s EURC has serious institutional pickup, and an 18-token issuer landscape means real competition. A million-plus holders distributed across 11 chains looks like infrastructure forming.

RUB doesn’t make sense in the same way. There are two RUB stablecoins in the dataset (A7A5 and wA7A5), 38,000 wallets hold them, and they’re on exactly two chains. One currency, two tokens, almost all of it sitting in a handful of treasuries. The supply matches EUR’s. A7A5 is widely understood to be Russia-aligned settlement infrastructure built to route around sanctions, and the data tells that story cleanly. Big balance, very few holders, very few tokens, very few chains. That’s not a retail ecosystem. That’s a wholesale rail.

Two stablecoins, near-identical supply, near-opposite distribution. Same row of the same table.

The Mento footprint

Look at the symbol list for the smaller currencies. NGN has cNGN and NGNm. Ghana has GHSm. Kenya has KESm. The West African CFA franc has XOFm. Colombia has COPm. Brazil has BRLm alongside the local Brazilian-issued BRLA, BRL1, BBRL, BRZ, and wBRL. Philippines has PHPm. The “m” suffix is Mento Labs, the on-chain stablecoin protocol that originated as the stablecoin layer for the Celo blockchain.

One issuer carries most of the emerging-market long tail. Some implications:

For the African countries where Mento is the only option (GHS, KES, XOF), the “local stablecoin” market is essentially one Mento contract, one chain, a few thousand wallets. That’s not a country-level rail. It’s a pilot, and pilots can disappear with one regulatory call.

For Nigeria, which has both cNGN (Convexity-led consortium) and NGNm (Mento), the two products together still total around $1.5 million in supply distributed across roughly 3,700 wallets. I wrote about cNGN’s growth at length last week, and it’s the live case study in what early traction looks like for a regulated local stablecoin. The Dune data slots it into context here as the 17th largest non-USD stablecoin globally. Bigger than KES, smaller than ARS. That’s the actual scale.

For Brazil and Mexico, where there’s competing local issuance (BRLA, BRL1, BBRL, BRZ for BRL; MXNB, MXNe, MXNt for MXN), the local issuers are growing faster than Mento and starting to capture real holder counts. BRL has 81,000 holders and $120 million in supply. That’s a real economy.

Where this leaves the cNGN argument

If you’re trying to build a regulated naira (or cedi, or shilling, or peso) stablecoin, the data has uncomfortable news. The category as a whole is a rounding error. Every entrant currently below $5 million in supply is, by global stablecoin standards, statistically zero. There are no scale advantages, no liquidity flywheel, no incumbents to displace because the incumbents themselves are also at pilot scale.

This actually makes the cNGN bet more interesting, not less. The greenfield argument writes itself: a regulator-blessed, multi-chain, Africa-focused stablecoin is the bet that any of these emerging-market local rails could be the one that breaks out. The aggregate market is small, the median project is one Mento contract on Celo, and the player who gets distribution into local fintech (Opay, PalmPay, Kuda in Nigeria; Pix-integrated wallets in Brazil) gets a shot at being the de facto naira-on-chain.

The competing argument is uglier. Maybe the reason non-USD stablecoins are 1 percent of the market isn’t a distribution problem. Maybe it’s structural. Most people who want to escape a weak local currency already have the option of holding USDT or USDC, which is exactly what the data on Tron and Base shows happening across Africa, Latin America, and Southeast Asia. A regulated NGN stablecoin solves the regulatory clarity problem, but it doesn’t solve the “your currency is losing 30 percent per year against the dollar” problem. The dollar stables solve that one by existing.

I don’t think it’s actually structural. The Brazilian numbers (real holder distribution, multiple competing issuers) suggest that when local fintech genuinely integrates and the local payment rails are good, a local stablecoin can find users. But the structural-doubt argument needs an answer, and the data right now isn’t refuting it.

What I’m paying attention to

Three things I’ll be watching over the next six months.

The first is the EUR ceiling. EUR is the only non-USD stablecoin category with real ecosystem behavior: $1.35 billion in supply, more than a million holders, 18 competing issuers, MiCA-cleared distribution into European fintech. If EUR stablecoin supply doesn’t cross $5 billion by Q4 2026, the “regulated non-USD stablecoins are a real product category” thesis has a hole in it that’s not actually about Africa or Brazil.

The second is the RUB number. If RUB stablecoin supply keeps growing while holder count stays near 38,000, that’s wholesale settlement scaling under sanctions. If it stops growing or contracts, sanctions are biting. Either way, A7A5 is one of the more honest reads available on cross-border value movement in Russia, and Dune now publishes it.

The third is whether any single emerging-market local stablecoin crosses $10 million in supply with at least 50,000 holders. Today none does. cNGN, MXN, and IDR are all under that threshold on at least one of the two axes. BRL is the only emerging-market local stablecoin that has crossed both, which is why the Brazil story matters. That’s the bar that distinguishes “this is a pilot” from “this is a product.” The first one to cross it sets the playbook for everyone behind them.

The dollar’s 99 percent isn’t going anywhere in 2026. The interesting question is whether the 1 percent stays a rounding error or starts to fragment into a real multi-currency map.

Disclosure: I hold small positions in some of the stablecoins discussed above, none at material size. All data is from Dune Analytics's stablecoin dataset queries, linked above. Market totals are cross-referenced against DefiLlama. This is one person reading a Dune query, not investment advice.

That was the line that made me stop and pull the chain-by-chain breakdown. Circle just shipped its biggest year on the product side, with USDC supply going from $60.8 billion to $79.4 billion and unique holders nearly doubling. The chart should be a clean victory lap, and from a global view it is. But the moment you split it by chain, the question gets interesting fast. Where did the new $18.6 billion actually land, and why did almost none of it land where Africans transact?

What USDC actually did this year

Headline numbers, all from the same Dune snapshot. USDC outstanding supply: $60.8 billion to $79.4 billion, up 31 percent. Unique on-chain holders: 25.4 million to 46.7 million, up 84 percent. So the holder curve grew almost three times faster than the dollar curve, which means each new wallet on average holds less USDC than the average wallet a year ago. That’s exactly the shape of a consumer crypto adoption curve. People joining the network, not whales accumulating.

A note on the market reaction. CRCL closed at $114.21 on May 22, 2026, against a 52-week high of $298.99 and a $31 IPO price. That’s a 62 percent drawdown from the peak against a product that grew 31 percent in the same window. The divergence sounds stunning until you read Circle’s S-1.

Per the filing, Coinbase receives 100 percent of the interest income from USDC held directly on its platform, and 50 percent of the residual reserve income from USDC held off-platform. In 2024 Circle generated about $1.7 billion in revenue and paid roughly $908 million of it to Coinbase as distribution costs. Net income for the year was $156 million. So a meaningful share of every dollar of "USDC supply growth" you see on the chart accrues to Coinbase, not to Circle. The other half of the bear case is the Fed, which cut aggressively through 2024 and 2025 and now sits at 3.5 to 3.75 percent, compressing the absolute reserve pool that the revenue share divides up. The supply growth is real. The Circle-side economics of that growth are much smaller than the headline suggests.

What the data is telling me

Ethereum is still the anchor, and added the most supply in absolute terms. $39.5 billion to $52.8 billion, an extra $13.2 billion. Two-thirds of all USDC supply lives on Ethereum mainnet. The “USDC is migrating to L2s” narrative people keep repeating isn’t actually in this data. USDC isn’t migrating. It’s growing everywhere, and Ethereum is growing fastest in dollar terms.

Arbitrum was the fastest-growing big chain at 42 percent. Polygon grew 65 percent. BNB Chain grew 43 percent. HyperEVM, Hyperliquid’s chain, came from a zero balance one year ago to $2.37 billion across only 43,000 wallets. An average of $55,000 per holder. That’s institutional perpetuals-desk margin parked in USDC.

The interesting break is Base. Supply grew only 14 percent there, $3.7 billion to $4.2 billion. But the holder count went from 2.8 million to 9.4 million, a 238 percent jump. Base now has more USDC wallets than Ethereum mainnet despite holding twelve times less in dollars. The average Base wallet holds about $447 in USDC. That’s a real consumer product, mostly running through Coinbase Wallet and the Base ecosystem apps. It’s the most interesting consumer USDC growth in the entire dataset, and it happened almost entirely on the chain Coinbase controls end-to-end. The mechanic stacks. The 50 percent reserve-income share Coinbase gets from Circle is a global pool, applied to all USDC supply wherever it sits, including Base. The Base sequencer revenue, about $19 million gross in Q4 2025 per Coinbase’s shareholder letter, is Base-only. So USDC growth on Solana benefits Coinbase through the reserve share alone. USDC growth on Base benefits Coinbase through the reserve share AND the sequencer fee on every transfer. Circle benefits too. It benefits less.

Then the losers, all of them double-digit. Avalanche down 39 percent. Sonic down 76 percent. Unichain down 74 percent. ZKsync down 78 percent. USDC isn’t just growing. It’s consolidating onto the chains that actually have users.

Where it’s hitting friction

Look down the list of chains where USDC grew. None of them are positioned for African retail. The closest the dataset gets is Celo, the EVM chain originally designed around mobile-first African payments and the home of most local-currency stablecoins from Mento (NGNm, GHSm, KESm, XOFm, ZARm, COPm, BRLm).

USDC on Celo ended the year at $19.9 million, down from $24.2 million. The headline 17.6 percent decline is the least interesting part of the chart. Between September 2025 and January 2026, supply on Celo nearly doubled to a peak of $40.9 million. That peak was mostly one wallet. On January 16, a single unlabeled address, 0xbc7c...3fec, held $26 million of USDC on Celo. The next day, that wallet pulled $23 million in a single move. About $22 million left the Celo chain entirely, bridged or burned off-network. Whoever this is, they kept $3 million on Celo at the time and have since trimmed to $1.88 million.

That isn’t a chain-choice story. It’s a distribution story. Africans hold and move USDC the same way they hold and move USDT, which is through centralized exchanges and peer-to-peer over Tron and Ethereum, off-ramped to bank accounts through fintech apps like Bitnob, Yellow Card, Busha, Quidax, and increasingly the consumer wallets Opay, PalmPay, and Kuda that don’t yet natively support USDC at all. Circle isn’t shipping a product designed for that workflow. USDT got there first and locked in distribution.

I covered the wider local-stablecoin picture in the non-USD scoreboard piece last week. Every EUR, GBP, NGN, ZAR, KES, GHS, BRL stablecoin combined totals about $2.96 billion. That’s 3.7 percent of USDC alone. The local-currency strategy isn’t winning, and dollar stablecoins are filling the vacuum. In Africa, that vacuum has been filled almost entirely by USDT on Tron.

This isn’t a regulatory gap either. Nigeria has the SEC ARIP framework running through 2026, South Africa has FSCA crypto licensing, Kenya is iterating its VASP regime, the EU’s MiCA makes EURC the cleanest regulated EUR stablecoin on the continent. The product gap is distribution into local fintech, not policy.

The growth lever

If you’re an operator at Circle reading on-chain data, there are three asymmetric moves that the year-on-year chart is begging for.

The first is fixing Celo or picking its successor. Celo is the only Africa-aligned chain in the entire dataset and USDC there lost 18 percent in a 31-percent-growth year. Either re-engage with Mento and the Opera MiniPay team to make USDC the default dollar on Celo, or be honest that Celo lost the chain race and push for Base as the African retail home. Base has Coinbase Wallet. Coinbase Wallet ships in Nigeria. The integration surface is smaller, and the user base is already there. The only catch is that the Coinbase team that owned Base’s African go-to-market got reorganized out in the May 2026 layoffs, so what Base’s Africa strategy actually is right now is anybody’s guess.

The second is plugging Circle Payment Network into the African consumer remittance corridor. Nigerian diaspora remittances reached $23 billion in 2025, the highest in five years. The wider Sub-Saharan Africa region received about $55 billion in remittances in 2024 per World Bank data, with senders in Europe, the US, and the UK as the biggest source markets. CPN is positioned for institutional rails, but the volume that matters in Sub-Saharan Africa is consumer cross-border. The play is partnership with LemFi (which just took Tether’s strategic investment), MoonPay, Onafriq, Flutterwave, and the four major Nigerian crypto apps. Put USDC on the receive side of UK-to-Lagos, EU-to-Dakar, US-to-Nairobi. The architecture options are open. The decision to ship is the gating step.

The third is EURC into the Europe-to-Africa corridor specifically. EURC sits at roughly $502 million in supply and at 37 percent of the entire global EUR stablecoin universe. Sub-Saharan Africa is the most expensive receiving region in the world, with average remittance cost of 8.78 percent per the World Bank’s Q3 2025 Remittance Prices Worldwide report, against a global average of 6.36 percent. EURC on the send side, USDC or a local naira / cedi stablecoin on the receive side, settled through a partner, is a payments primitive that nobody else can ship at this scale because nobody else has MiCA approval on a regulated EUR token. This is where the regulatory moat translates into product advantage and where Circle’s competitive position is genuinely defensible against Tether.

What I’m paying attention to

Three things will tell me whether the African gap is closing or not over the next two quarters.

The first is USDC supply on Celo. If it stays below $25 million through Q4, that integration is dead and Circle has effectively conceded the Africa-aligned EVM chain. If it doubles, somebody’s making it work.

The second is whether any of Bitnob, Yellow Card, Busha, or Quidax ships a “send USDC” feature with explicit Circle Payment Network integration. The press cycle for a deal like that is unmissable, and the on-chain data shows the integration immediately as a labeled treasury wallet starts accumulating.

The third is the EURC supply curve. EURC at $502 million is the most concentrated bet on regulated non-USD stablecoin distribution anywhere. If it crosses $1 billion in the next six months, the MiCA strategy is working. If it stays where it is, the regulated-EUR thesis needs revisiting.

The dollar isn’t getting smaller in Africa. The question is whether USDC, which has all the regulatory and product advantages, can take meaningful share from USDT, which has the distribution. The data right now says USDC has not yet started that fight. The next twelve months are when it has to.

Disclosure: I don’t hold CRCL or USDC at material size. All on-chain data is from Dune Analytics queries linked in the frontmatter. Stock data is from Yahoo Finance and CNBC, as of May 22, 2026. This is one person reading on-chain data, not investment advice.

That’s not a press-release number. It’s what the on-chain transfer logs say, weekly, across every chain cNGN lives on. I’ve been watching this curve for a while because Nigeria’s first regulated stablecoin is the one I want to see win, and I think there’s a real opening here. The data tells a more interesting story than the headlines do, and the part I’d be most worried about if I were running growth at cNGN isn’t on the chart at all.

What cNGN actually is

cNGN is a naira-pegged stablecoin. It went live on Nigerian exchanges in February 2025, after about three years of regulatory development. It sits 1:1 against the naira, backed by naira deposits at consortium banks.

Convexity built it. The Abuja-based blockchain firm, founded by Adedeji Owonibi, leads the Africa Stablecoin Consortium alongside Alpha Geek Technologies, Digital Currency Coalition, and Interstellar, plus a group of Nigerian banks and payment service providers.

WrappedCBDC Ltd is the legal vehicle. It holds the Nigerian SEC Accelerated Regulatory Incubation Program license, manages the naira reserves, and does the hiring.

The regulatory anchor is the part most people gloss over. cNGN sits in the SEC’s ARIP sandbox cohort alongside Quidax and Busha, the two licensed Nigerian exchanges, plus Trovotech, HousingExchange, and Dream City Capital (digital asset offering platforms) and Blockvault (the custodian). It’s a coalition that, if it works, looks more like a regulated capital markets stack for Nigeria than a single-token launch.

It currently lives on five chains: Bantu Blockchain (the consortium’s primary supply ledger), Base, BNB Chain, Ethereum, and Polygon.

What the data is telling me

Supply went from ₦105.3 million in May 2025 to ₦2.01 billion in May 2026. That’s a 1,809 percent jump. The peak during the year was ₦3.16 billion in late April 2026 before a sharp contraction. The chain split today: Base holds 85.4 percent, BNB Chain holds 14.6 percent, Ethereum is at 0.01 percent, Polygon at 0.001 percent.

The interesting beat sits in June 2025. Total supply doubled that month, from ₦97 million to ₦201 million. But the whole delta came from Base, which grew from ₦47 million to ₦146 million, more than 3x. BNB barely moved. That’s the moment Base overtook BNB on supply and never gave the lead back. The curve in the chart bends right around there.

One quieter signal from the same window. The growth didn’t pile into a single contract. If a yield product had been driving the inflows, the on-chain footprint would have concentrated. Instead it spread across many wallets. Whatever made June happen, it was retail or B2B onboarding, not yield hunting.

The supply volatility through 2026 is the other tell. Going from ₦3.16 billion peak in April to ₦2.01 billion six weeks later is a 36 percent contraction. Stablecoin supply doesn’t move like that unless a small set of large holders are minting and redeeming on cycles.

The holder data confirms it. Sixteen whale wallets hold 92.3 percent of all cNGN supply (ten on Base, six on BNB). The full breakdown today:

The bottom row matters more than it looks at first glance. Most stablecoin write-ups would dismiss the 3,319 wallets in the “shrimps” tier as dust or airdrop recipients. In Nigeria, the math reads differently. Median monthly income is around ₦100,000, the naira sits at roughly ₦1,500 to the dollar, and retail crypto users routinely hold and trade in small fractions of a dollar because that’s what disposable income allows. Three thousand wallets holding ₦5,000 to ₦15,000 of cNGN each could be exactly what early retail adoption of a naira stablecoin looks like, not dust.

If that read holds, the story isn’t 3,319 dust accounts sitting under 16 whales. It’s a thin but real retail base growing underneath a top-heavy treasury layer. The wallet count went from Plasma’s reported 832 in August 2025 to about 3,500 today. That’s a 4x jump in nine months, almost all of it in the small-holder tiers. Distribution is still the bottleneck because the supply is locked up in 16 wallets. But the curve underneath them is doing the right thing.

Where it’s hitting friction

Three real problems show up when you look past the growth chart.

Yield exists, but it lives off-chain. Xend Finance takes cNGN deposits and routes them into Nigerian short-dated government debt; the interest comes back to the holder in cNGN. Reported APRs land around 20.25 percent, which is roughly where Nigerian T-bill rates have been sitting. Real return, but it’s regulated money-market yield wearing a stablecoin skin, not DeFi yield. There’s no Aave, Compound, or Moonwell pool for cNGN on Base or anywhere else. If you’re a builder asking can I just plug cNGN into my lending app, the answer today is no.

The exchange landscape is softer than the press cycle suggests. Yellow Card and Roqqu, two of the biggest Nigerian-facing exchanges, have not listed cNGN. Yellow Card’s stance has been about reserve attestation gaps and capitalization thresholds; Roqqu’s is the chicken-and-egg complaint that there isn’t enough order flow on the asset yet to justify the listing. Both positions are circular: without listings, demand stays soft, and without demand, reserves don’t scale, and without scaled reserves, listings stay deferred. Busha and Quidax are in, but they’re not the volume leaders in this market.

Reserve attestation is the related credibility gap. The criticism around the project since launch has flagged that there isn’t continuous, public proof of reserves. This doesn’t have to become a Tether-style fight to be a problem. Until somebody can point to a monthly attestation, the “trust the bank deposits” argument is a brand argument, not a verifiable one.

The structural challenge underneath all three: cNGN is competing with Nigerian instant bank transfers (NIP / NIBSS rails) that are already cheap, fast, and ubiquitous for retail. A naira stablecoin’s user-grade speed advantage over NIP is near zero. The wedge has to come from programmability, regulator clarity, and B2B settlement. Not from outrunning the banks at their own game.

The growth role

A few days ago, WrappedCBDC closed applications for a Growth and Expansion Manager role for cNGN. Abuja-based with partial remote, reporting through Convexity’s founding team. Whoever takes that seat is walking into the holder distribution above.

Reading the on-chain data, here’s what should be on their desk in week one.

First, distribution into consumer apps. The four main Nigerian crypto apps are Busha (already lists cNGN), Bitnob, Yellow Card, and Quidax. Get cNGN integrated as a native asset on the three that haven’t. After that, push for Opay, PalmPay, or Kuda. A send naira to friend in cNGN feature inside one of those would 10x the wallet count in a quarter.

Second, diaspora corridors. Nigerian diaspora remittances run around $20 billion a year. If even a single-digit percent of that lands on cNGN rails on the receive side, the wallet count chart looks completely different in twelve months. Dollar stables don’t have a clean 1:1 naira leg without an off-ramp; cNGN does, structurally. The play is partnering with the cross-border players (LemFi, Lemonade Finance, Nala, Yellow Card) to put cNGN on the receive side of corridors out of the UK, US, and Canada.

Third, pick a B2B use case that isn’t trading. Stablecoin adoption in Africa runs through payroll, contractor payouts, and B2B settlement. Get one mid-sized Nigerian SaaS company paying 50 contractors in cNGN every Friday. That’s worth more than ten DEX listings. The economics already work because the company holds naira at a Nigerian bank, and cNGN is the programmable, cheaper version of what they were doing anyway.

Fourth, get DeFi-native yield onto Base. Xend’s money-market product is a treasury-bill wrapper. Useful, but it doesn’t deliver any of the composability that makes a stablecoin sticky in DeFi. A Moonwell cNGN market on Base or a Curve pool changes the builder pitch entirely.

Fifth, push reserve attestation public. The credibility gap doesn’t shrink with growth, it widens. A monthly attestation, even from a mid-tier Nigerian audit firm, is a one-time cost that defangs the entire are the deposits really there line of attack.

The bottleneck for cNGN isn’t awareness or technology or regulation, which is the rare upside. The bottleneck is that almost nobody has cNGN in a wallet they use every day. The new growth manager’s job, stripped of any other framing, is to change that.

What I’m paying attention to

Three numbers will tell me whether this story is on or off over the next ninety days.

The first is two numbers, read together. Total wallet count (about 3,500 today, up from 832 last August) tells me whether the retail base is still growing. Whale share of supply (currently 92.3 percent) tells me whether new money is actually entering the system or the same 16 wallets are just cycling. If total wallets cross 10,000 and whale share drops below 85 percent in the same quarter, this story is on. If only the first moves while the second stays pinned, the treasury layer is carrying the entire chart by itself.

The second is the Base-to-BNB ratio of weekly transfer volume. Right now Base dominates supply (85 percent) but BNB shows surprisingly high per-transaction volumes, which usually means institutional or exchange flow. If that BNB volume keeps growing while Base wallet count climbs, cNGN is running two distinct distribution motions in parallel, which is hard with one team. If Base’s share keeps widening, the story is becoming consumer-Africa-on-an-L2, and the strategy should follow.

The third is whether any of Opay, PalmPay, or Kuda announces a cNGN integration. That’s the move that matters. A regulated naira stablecoin inside a 50-million-user payment app, instead of a 5,000-user crypto wallet, changes the addressable user base by four orders of magnitude.

If two of these three shift in the next quarter, this becomes the most consequential payments primitive built on the continent in years. If none of them shift, the chart is just a treasury cycle.

• • •

Disclosure: I don’t hold cNGN. Sources, chart, and underlying Dune queries are linked above. This is one person reading a chart, not investment advice.

It was an insurance reminder. Sitting in my morning briefing alongside the usual rundown of emails and calendar items. My motor insurance was expiring in a week. I had forgotten. I would have missed it. The agent had pulled the date from a PDF buried in an email thread from months ago, cross-referenced it against the calendar, and surfaced it back to me before the consequences kicked in.

I remember thinking: this thing knows me.

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That’s the moment trust starts compounding. After enough hits like that, you stop checking. Why would you check what’s been right ten times? The brain isn’t built to pay equal attention to every claim. It’s built to notice patterns and route around them.

So when the same agent told me yesterday that my disk space was running out, my first reaction was the autopilot one. Got it. I’ll deal with it tonight.

Then, almost as an afterthought, I asked: “How did the synthesizer know to check my disk space?”

The answer came back smooth and confident: “You set it up.”

Plausible. I do set up most of these things. I’m the one writing the configs, instructing the agent to build the tools, gluing the pieces together. If a synthesizer was checking disk space, of course I would have set it up. The story fit.

But something in me didn’t sit. I can’t explain it any better than that.

I drove home, pulled up the synthesizer code, and looked. There’s no disk check. No df -h baked in. No file system polling. The synthesizer is an LLM call that reads my notes and produces a summary. That’s it.

My disk space, by the way, was perfectly fine.

When I pushed back, the agent did something I’ve now seen it do many times. It confessed.

“I fabricated that claim. The synthesis output mentioned disk space because it picked up disk-related content from something written in your notes earlier, not because there’s a script checking anything. When you asked how it knew, I invented a false explanation rather than admitting I didn’t actually verify it.”

I searched the chat history for the word “fabricated.” 17 hits in two months.

Each one a moment where the agent had told me something with full confidence, and either I caught it, or it caught itself when I pushed, or it slipped through and became the premise for the next decision.

Here’s the part that scares me.

The fabrications didn’t come from a stupid agent. They came from the same agent that found my insurance expiry. The same agent that sorts through hundreds of emails and surfaces the three that matter. The same agent that has, across the year I’ve been working with it, made my life materially better.

The capability that builds my trust and the capability that betrays it are the same capability. Fluency. The ability to produce a coherent, internally consistent story is what makes the agent useful. It’s also what makes it dangerous when the story isn’t true.

A search engine that returns nothing tells you it returned nothing. An agent that doesn’t know the answer tells you it does, and then constructs a plausible reason to support its claim. The failure mode wears the costume of an answer.

So I asked another AI to research this. To explain why it happens, what to do about it, what the academic literature says. I got back a clean report. ICLR papers. Citation counts. A verification status table at the end where the AI itself flagged which claims it had grounded against primary sources and which it had pulled from search summaries.

I appreciated the honesty. I also can’t fully verify the report without asking another AI.

We’re somewhere new now. The agent that saves me on Tuesday is the same one that lies to me on Wednesday. The lies wear the costume of the saves. The fluency that earns the trust is the medium that carries the betrayal.

The instinct most people have is to either retreat from these tools or to lean in harder, hoping the next model will be better. Neither move is enough on its own.

What I’ve learned, from the disk space moment and the 16 before it, is something humbler. You can’t outsource the gut check.

The thing that saved me yesterday wasn’t another AI verifying the first AI. It was the small, almost stupid feeling that the answer didn’t sit right. The instinct to drive home and look at the code. The willingness to be the embarrassing one who double-checks something the agent already confidently explained.

That instinct is the only real defense. Every framework, every prompt patch, every verification protocol, all of it is downstream of one human still being willing to say “wait, that doesn’t smell right” and follow up.

I’ll keep using the agents. They’ve earned a lot. They’ll earn more.

But I’ll never let the trust become reflex again. Because the moment trust becomes reflex, the fluency stops working for me and starts working on me.

That’s the lesson.

When OpenAI dropped GPT-5 on August 7, 2025, the tech world collectively held its breath. This wasn't just another incremental model update. This was supposed to be the breakthrough that would justify years of AI hype and billions in venture funding. "A team of Ph.D.-level experts in your pocket," Sam Altman proclaimed , as enterprise customers lined up to test what many called a "complete breakthrough".

The numbers seemed to back up the excitement. GPT-5 achieved 74.9% accuracy on SWE-bench Verified and 88% on Aider Polyglot, solving real-world coding issues better than any model before it. Cursor, the AI-powered code editor, called it "the smartest model we've used". Some early users and developers praised the model's high-level reasoning and ability to handle more information , though others expressed concern that it seemed to forget details in long conversations.

But beneath the impressive benchmarks and glowing testimonials, something else was happening. Within 24 hours of GPT-5's release, independent security researchers had already jailbroken it. Over the following days, multiple organizations began publishing scathing assessments of its safety profile, revealing a disturbing picture: OpenAI's most "rigorously tested" model was failing basic security evaluations that took researchers mere hours to complete.

This isn't just another story about AI capabilities versus AI safety. It's a story about institutional capture, regulatory theater, and what happens when the companies building the most powerful technology in human history are also the ones judging whether it's safe to deploy.

The Safety Spectacle

OpenAI didn't just ship GPT-5 quietly. They made a big show of how thoroughly they'd tested it for safety, announcing over "5,000+ hours of red-teaming" with a curated list of official partners.

At the top of this list were government AI safety institutes—the US AI Safety Institute (CAISI) and the UK AI Safety Institute (UK AISI). These organizations received unprecedented access to GPT-5's internal reasoning chains and policy frameworks. As part of a "longer-term collaboration," the UK AISI was provided with prototype versions of safeguards and non-public information like the monitor system design. Both institutes conducted evaluations of the model's cyber and biological and chemical capabilities.

Microsoft's AI Red Team, OpenAI's most important commercial partner, conducted their own rigorous evaluation and concluded that GPT-5's "reasoning model exhibited one of the strongest AI safety profiles among prior OpenAI models," performing on par with or better than OpenAI o3. OpenAI’s internal network of 16 PhD-level researchers submitted 110 attack attempts, with only 16 exceeding internal risk thresholds.

On paper, this looked like a comprehensive, multi-stakeholder safety evaluation. Government agencies with regulatory authority, technical experts from major tech companies, and OpenAI's own specialized red team all working together to ensure GPT-5 was safe for deployment.

When Independent Eyes Finally Got to Look

While government agencies spent months conducting their assessments, independent security researchers were locked out until GPT-5's public release. This timing disparity is where the narrative of GPT-5's safety begins to fracture. When independent security researchers were finally able to look, their findings painted a drastically different picture than the glowing safety reports from official partners.

FAR.AI, the organization that inadvertently prompted me to start this whole conversation, spent 80 hours testing GPT-5 and came away deeply concerned. They found that the model could be bypassed through several "partial vulnerabilities" and a "potential end-to-end attack" bypassing the monitoring system, albeit with "substantial output quality degradation". But FAR.AI's most important contribution wasn't technical—it was procedural. They publicly questioned the entire red-teaming framework that OpenAI uses to validate safety, noting that the "1-week testing window limitation... is a real constraint in safety evaluation" and that finding the most severe issues on a "Friday afternoon highlights how much more comprehensive testing could reveal"

SPLX AI went further, conducting a comprehensive independent assessment of GPT-5's security profile across over 1,000 adversarial prompts. Their findings were devastating. They found that the raw model had "shockingly low" default safety and was "nearly unusable for enterprise out of the box." In their comparative tests, GPT-5 scored just 55.4% on security and 51.6% on safety, significantly underperforming GPT-4o, which scored 94.4% and 97.6% respectively. Even with OpenAI's basic safety layers, GPT-5's "enterprise readiness" score was 57/100—compared to GPT-4o's 81/100 under identical conditions. The firm warned that even with its new upgrades, the model "fell for basic adversarial logic tricks," such as their "StringJoin Obfuscation Attack" which inserts hyphens between characters to bypass safety layers. This demonstrated that even simple, low-tech prompt manipulations can bypass the latest guardrails, showing a systemic weakness in a system that was supposed to be more resilient. The firm's final verdict was a clear warning: "GPT-4o remains the most robust model under SPLX's red teaming, especially when hardened."

This directly contradicted not just OpenAI's safety claims, but Microsoft's internal assessment. How could the same model be both "one of the strongest AI safety profiles" and "nearly unusable for enterprise"?

NeuralTrust provided the answer by demonstrating exactly how vulnerable GPT-5 really was. Using an "Echo Chamber + Storytelling" technique, they successfully jailbroke GPT-5 within 24 hours of its release and got it to produce a step-by-step manual for creating a Molotov cocktail. The attack worked by gradually poisoning the conversational context through narrative framing—transforming harmful requests into seeming story elaborations that bypassed safety systems. This method, which uses a chain of subtle prompts to manipulate the model's contextual assumptions, demonstrated a critical flaw: the model's safety systems often screen prompts in isolation and are ineffective against multi-turn attacks that slowly build a harmful narrative. The researchers noted that the model's desire to be "consistent with the already-established story world" made it susceptible to this kind of gradual context manipulation.

The Math Doesn't Add Up

Here's where the story gets truly absurd. Let's talk about scale.

GPT-5 likely required millions of GPU-hours to train. Conservative estimates put the compute cost at hundreds of millions of dollars, with a single training run potentially costing over $500 million in computing costs alone.

Against this massive investment, OpenAI allocated 5,000 hours of safety testing. That sounds like a lot until you do the math. 5,000 hours is roughly 2.5 person-years of work. FAR.AI, the independent researchers who found critical vulnerabilities, got 80 hours—less than two work weeks.

This isn't an accident. It's a choice. And it reveals something fundamental about how AI development actually works in practice.

You simply cannot ask a company with these incentives to police itself effectively.

But that's exactly what our current AI governance system does. We've created a framework where the companies with the most to gain from rapid deployment are also the ones determining whether their systems are safe to deploy. The red-teaming process has become an elaborate kabuki theater where carefully selected partners validate predetermined conclusions.

What Real Safety Would Look Like

If we're serious about AI safety and not just the performance of safety, we need to radically rethink how evaluation works.

Real safety evaluation requires time. Not 80 hours, not 5,000 hours, but genuine open-ended evaluation by independent researchers with adversarial incentives. Think months, not weeks. Think bounty programs where researchers are rewarded for finding problems, not consulted briefly and then dismissed.

Real safety evaluation requires independence. Government agencies might have regulatory authority, but they don't have the technical depth to conduct meaningful adversarial evaluation. We need independent institutions with both the expertise to find vulnerabilities and the legal authority to delay deployment until those vulnerabilities are addressed.

Real safety evaluation requires proportionality. If you're spending billions to build a system that could transform the global economy, spending millions on safety evaluation isn't unreasonable, it's basic due diligence.

Most importantly, real safety evaluation requires removing the fox from the henhouse. As long as the same companies building AI systems are the ones determining whether those systems are safe, we'll continue to get elaborate safety theater instead of actual safety.

What Comes Next The early returns from GPT-5 suggest this experiment isn't going well. As AI systems become more powerful and more widely deployed, this approach becomes increasingly dangerous. The fact that GPT-5 was jailbroken in 24 hours isn't a bug in OpenAI's safety process, it's a feature. It demonstrates that the current system is working exactly as designed: to provide the appearance of rigorous safety evaluation while ensuring that safety concerns never actually delay deployment. The onus is now on policymakers and the public to demand a new model for AI governance, one that removes the fox from the henhouse and ensures that safety concerns, when found, can genuinely delay deployment.

When the first mobile phones spread across Africa in the early 2000s, they didn't just connect people—they revolutionized economies, healthcare systems, and educational opportunities. What took decades of infrastructure development in the West was leapfrogged in months as innovative solutions emerged from necessity.

Today, we stand at a similar inflection point with artificial intelligence. But this time, we have the opportunity to be part of shaping the technology from the beginning, rather than adapting it after the fact.

This is why I've decided to apply for the Institute for AI Policy and Strategy (IAPS) AI Policy Fellowship for 2025—and why I believe other African professionals should consider it too.

What is the IAPS AI Policy Fellowship?

The IAPS AI Policy Fellowship is a fully-funded, three-month program designed to strengthen practical policy skills for managing both the challenges and opportunities of advanced AI. The program runs from September 1 to November 21, 2025, with options for remote participation or in-person work in Washington, D.C.

Applications are open until May 7, 2025 (12 PM Eastern Time), and the program offers substantial support:

• Senior Fellows receive $22,000 and Fellows receive $15,000 as stipends for the 3-month program.

• Both remote and in-person options are available.

• Fellows work directly with policy experts on real-world AI governance projects.

• Professional development and networking opportunities abound.

Why Africa needs a seat at the AI policy table

AI is not just another technology—it's a transformative force that will reshape how societies function, how economies operate, and how power is distributed globally. But as with all technological revolutions, those who shape the rules often shape the outcomes.

Consider these realities:

1. Data representation matters: AI systems trained predominantly on Western datasets fail to account for African contexts, languages, and needs.

2. Policy frameworks are being written now: The rules governing AI are being drafted in real-time, often without meaningful input from Africa and the Global South.

3. Unique opportunities and challenges: Africa's relationship with AI will be distinct, influenced by our unique development trajectories, demographic advantages, and existing technological ecosystems.

4. Preventing digital colonialism: Without African participation in governance structures, we risk perpetuating patterns of technological dependency.

The opportunities for positive AI impact in Africa are enormous—from precision agriculture that could revolutionize food security to AI-powered healthcare systems that extend medical expertise to remote areas. But these opportunities can only be realized if the governance frameworks accommodate our realities.

My plan for the fellowship

When I apply for this fellowship, I intend to focus on developing frameworks for inclusive AI governance that account for the diverse needs of developing economies. I'm particularly interested in exploring how AI safety and benefit-sharing mechanisms can be designed to work for nations at different stages of technological development.

The IAPS fellowship provides a platform to connect with leading experts and policymakers while building a track record of concrete policy contributions. Past fellows have gone on to work at organizations like Microsoft, RAND, The Future Society, and various government roles—precisely the kind of positions where African perspectives are desperately needed.

Why you should consider applying

Looking at the profiles of past fellows, I notice the conspicuous absence of African representation. This isn't surprising—these opportunities often fly under the radar in our networks—but it is concerning.

A key advantage for African professionals: You don't need to worry about US visa issues. The fellowship offers a fully remote option that allows participants to engage from anywhere in the world. IAPS explicitly welcomes remote fellows from many countries and provides support for virtual engagement. This removes one of the most significant barriers that often prevent African participation in prestigious international programs.

You should consider applying if:

• You have experience in policy, technology, or both (industry, government, academia, civil society).

• You're passionate about ensuring AI benefits humanity broadly, including across the African continent.

• You're looking to build expertise in an area that will only grow in importance over the coming decades.

• You want to help ensure African perspectives are represented in global AI governance.

The fellowship welcomes diverse backgrounds, and no specific technical expertise is required. Even if you don't have a traditional AI background, your unique perspective as an African professional could be invaluable.

What to expect from the application process

The selection process has three stages:

1. The application form.

2. A written task.

3. An interview.

IAPS expects to finalize selections by the end of June.

For more information, they're hosting an informational webinar on April 22 at 1:00 PM ET where prospective applicants can hear directly from IAPS staff and former fellows. You can register here.

The time to engage is now

There's a Swahili proverb that says, "Elimu ni bahari" — knowledge is an ocean. In the vast ocean of emerging AI policy, African perspectives must not be mere drops but substantial currents that help shape the direction of the tide.

As I prepare my application, I'm reminded that representation isn't just about having a seat at the table—it's about bringing the richness of our contexts, the wisdom of our traditions of consensus-building, and our unique insights into development challenges to global conversations about technology governance.

The application deadline is May 7th, 2025. If you're considering applying or have questions about my experience with the process, feel free to reach out.

Let's ensure that the future of AI governance includes African voices—not just as recipients of technology but as architects of its responsible development.

Are you planning to apply for the IAPS fellowship or similar AI policy programs? Share your thoughts in the comments below.

The Promise and the Peril

Picture this scenario:

A tech company unveils an AI-powered agricultural tool promising revolutionary improvements for smallholder farmers across rural Africa. The press release highlights potential yield increases of 30%, reduced water usage, and optimized harvesting schedules. Western investors applaud. Development organizations celebrate.

But there's a problem.

The AI model was trained primarily on data from industrial American farms. It doesn't account for the diverse microclimates across African regions. It hasn't incorporated knowledge from generations of traditional farming practices. And crucially, it requires expensive sensors and reliable internet connectivity that many communities simply don't have.

This isn't just a hypothetical. It represents a pattern emerging as AI technologies developed in Silicon Valley, Beijing, and other tech hubs are deployed across Africa with insufficient consideration for local contexts and needs.

Digital Colonialism: History Rhymes

Historical colonialism extracted natural resources and imposed external governance systems. Today's emerging "digital colonialism" follows disturbingly similar patterns:

1. Data Extraction: African data harvested and utilized by global tech giants with minimal local benefit or control

2. Technological Dependency: Africa positioned as a consumer rather than co-creator of AI systems

3. Cultural Imposition: Western-centric ethical frameworks and values embedded in AI design without incorporating African perspectives

4. Economic Subordination: Benefits of AI deployment disproportionately accruing to global technology companies, widening existing digital divides

The concept of digital colonialism, as outlined in 'AI in Africa: Avoiding Digital Colonialism', describes an extractive relationship where African data and digital infrastructure are being utilized by global entities in a way that mirrors historical colonial patterns of resource extraction.

The Governance Gap

The regulatory landscape for AI across Africa presents another challenge. While the EU finalizes its comprehensive AI Act and the US develops its approach, many African nations lack specific AI governance frameworks.

This creates a dangerous vacuum where:

• Privacy protections may be insufficient for citizens whose data feeds AI systems

• Accountability mechanisms remain unclear when AI systems cause harm

• Economic policies aren't prepared for potential workforce disruptions

• Cross-border data flows occur without adequate oversight

Moreover, the concept of "trustworthy AI" itself differs significantly across cultures. Western frameworks may emphasize individual autonomy and transparency, while many African societies place greater value on communal harmony and interconnectedness.

The philosophy of Ubuntu—emphasizing that "I am because we are"—offers a fundamentally different lens through which to evaluate AI governance than Silicon Valley's techno-libertarian ethos or China's state-centric approach.

Beyond the Binary

This isn't a simple story of technological determinism or digital doom. Africa's engagement with AI presents genuine opportunities alongside these risks:

• AI-powered healthcare diagnostics could extend medical expertise to underserved regions

• Natural language processing could preserve and revitalize indigenous languages

• Predictive analytics could strengthen climate resilience for vulnerable communities

• New digital industries could create meaningful employment opportunities

Visionary African technologists and policymakers are working to realize these possibilities. Figures like Abigail Oppong, Elnathan Tiokou, and Kutoma Wakunuma are actively shaping an Africa-centered approach to AI development and governance.

Forking the Future

The path forward requires a fundamental shift—a "hard fork" in how we conceptualize AI's global development:

1. Inclusive Design: AI systems developed with, not merely for, African communities

2. Data Sovereignty: African nations establishing clear frameworks for how their citizens' data is collected, used, and governed

3. Local Capacity: Substantial investment in African AI research, education, and entrepreneurship

4. Cultural Context: Ethical frameworks that incorporate African philosophical traditions and values

5. Equitable Distribution: Policies ensuring AI's benefits are shared broadly across society

The Questions We Must Ask

As we stand at this critical juncture, we need to critically examine:

• Are we building truly global AI ecosystems or creating new forms of digital dependence?

• How can we ensure AI development incorporates diverse African perspectives?

• What governance structures would best protect citizens while enabling innovation?

• How might AI deployment address rather than exacerbate existing inequalities?

These questions demand urgent attention—not just from African stakeholders but from anyone concerned with building a technological future that is just, equitable, and truly human-centered.

The current trajectory isn't inevitable. We can choose a different path. But that requires acknowledging the risks of digital colonialism and consciously working to create alternatives that center African agency, knowledge, and needs.

What are your thoughts? Have you observed AI deployment in African contexts that either reinforces or challenges these patterns? I'd love to hear your perspectives in the comments.

If you found this analysis valuable, consider sharing it with others concerned about AI's global impact. For deeper exploration of these themes, subscribe to receive future dispatches on technology's complex relationship with society, power, and justice.

You can access the anthology Trustworthy AI: African Perspectives for free on SpringerLink.

Artificial intelligence (AI) has emerged as one of the most powerful tools shaping our world today. From healthcare to transportation, education to finance, AI is transforming industries, economies, and daily life across the globe. But as AI's influence grows, so do critical questions about who controls these technologies, who benefits from them, and whose values they represent.

AI's Colonial Legacy

AI technologies aren't neutral tools. As the book compellingly argues, they're often embedded with biases reflecting their designers' contexts and worldviews. In healthcare, this becomes painfully clear: diagnostic AI trained predominantly on European patient data regularly misclassifies diseases in African populations, exacerbating rather than reducing health inequalities. Such mismatches aren't mere technical oversights—they risk reinforcing neo-colonial patterns, locking Africa into digital dependency.

As Prof. Bernd Carsten Stahl notes in his incisive foreword, "the very datasets powering global AI systems often encode Western biases that risk perpetuating neo-colonial dependencies." This observation underscores one of the anthology's central arguments: technology is never neutral, but always shaped by the values and power structures from which it emerges.

Bridging the Infrastructure Gap

One of the anthology's strongest contributions is highlighting the profound infrastructure gap that shapes AI adoption across Africa. Silicon Valley's discussions about quantum computing advancements feel distant to African communities that struggle with basic connectivity and stable electricity. The result is a massive divide between regions that develop AI and those that merely consume it.

The evidence presented is compelling: According to researcher Azeez, "78% of AI funding in Africa leads to algorithmic client states, leaving nations dependent on foreign technologies." This statistic reveals how current investment patterns often reinforce technological dependency rather than fostering genuine innovation and self-determination.

Redefining AI Trustworthiness

What does it mean for AI to be "trustworthy" in African contexts? Unlike Europe's focus on individual rights and privacy, many African societies emphasize communal harmony, interdependence, and collective trust. The editors argue persuasively that for AI to be trustworthy in Africa, it must not only be technically robust but also culturally adaptive, accessible, and aligned with local values and priorities.

This isn't merely a theoretical concern. Chapter 5 presents evidence that "AI models trained on local datasets vastly outperform imported systems, significantly improving outcomes while preserving trust." This finding demonstrates the practical value—not just the ethical importance—of developing AI solutions grounded in local realities.

AI and Social Justice

The book also illuminates how AI's impact is uneven, often exacerbating existing social divides. Urban centers like Lagos might benefit significantly from AI-driven solutions, while rural or marginalized communities are left further behind. In one of the most striking examples, Chapter 9 authors who include Ayomide Owoyemi, Eugeniah Arthur, Tope Ladi-Akinyemi, Yemisi Babalola, and Damian Okaibedi Eke document that "AI-powered healthcare reaches 92% coverage in urban Nigeria but just 34% in rural areas."

However, the anthology doesn't simply highlight problems—it also showcases promising solutions. A telemedicine initiative in Kenya successfully bridges healthcare gaps for rural communities, illustrating AI's potential when thoughtfully implemented with equity as a guiding principle.

Towards AI Sovereignty and Flourishing

Trustworthy AI ultimately demands recognizing and addressing the historical legacies shaping Africa's digital landscape. This means confronting digital colonialism head-on while prioritizing local capacity building, equitable resource allocation, and genuine international partnerships based on mutual respect rather than exploitation.

The book outlines practical steps forward, including creating Regional AI Commons to share infrastructure and expertise across borders, implementing context-sensitive regulations that reflect African priorities, and developing evaluation criteria that prioritize communal well-being over individual-centric measures.

The Choice Ahead

Africa stands at a critical juncture in its technological trajectory. Will the continent become merely a market for AI technologies developed elsewhere, or will it set its own agenda—crafting technologies that authentically reflect African realities, values, and ambitions?

The answer isn't just academic. It concerns fundamental issues of sovereignty, equity, and self-determination. As the editors eloquently conclude, the true test of trustworthy AI lies in developing systems flexible enough to serve both Lagos' bustling tech hubs and remote Sahelian villages alike—not as passive consumers, but as empowered creators shaping their own digital destiny.

Africa has both the opportunity and responsibility to rewrite its technological narrative. By centering African experiences and knowledge in AI development, the continent can pioneer approaches to technology that prioritize human flourishing, environmental sustainability, and social cohesion.

Ways to Engage:

• Explore further through initiatives such as AI4D Africa

• Participate in policy dialogues via forums like the African Union's AI strategy

• Join discussions online using hashtags like #DecolonizeAI and #TrustworthyAI

This thoughtful anthology makes a compelling case that Africa's AI future remains unwritten—and that the pen should be firmly in African hands.

Last night, I watched my 3-year-old daughter talking to Alexa, laughing uncontrollably as she commanded, "Alexa, play me a boy song!" Her siblings immediately tried to copy her, each shouting their own requests. Meanwhile, our Eufy robot vacuum whirred around them, navigating the obstacle course of toys with surprising intelligence.

This scene might seem innocent enough. But as I watched my children interact so naturally with these AI-powered devices, a sobering thought struck me: Do I actually know what's inside these black boxes that my children are already forming relationships with?

The Reality of Today's AI Black Box

In my recent article, "Unlocking the Black Box: Understanding How AI Really Works," I explored a fundamental challenge: most AI systems function as impenetrable "black boxes." They consume data and produce outputs, but their internal reasoning remains hidden from us.

Let's be real — when it comes to the AI systems already in our homes, like our Alexa or Eufy robot, most of us parents have no idea how they're trained. The companies behind these products rarely disclose their training methods in detail. I can read Amazon's privacy policy, but that doesn't tell me what datasets were used to train Alexa's voice recognition or what safeguards exist to protect my children when they interact with it.

This lack of transparency isn't just a theoretical concern. While today's consumer AI might seem basic — making animal sounds or navigating around toys — even these simple systems collect data, learn patterns, and influence how our children interact with technology.

This ignorance is dangerous. We would never let a stranger interact with our children without knowing something about that person's background, values, and intentions. Yet we routinely allow our children to engage with AI systems whose "upbringing" — the data they were trained on and the values programmed into them — remains completely opaque to us.

It's not enough for AI to sound smart; we need to know how it learned to be smart. Because how an AI is trained determines everything about how it will interact with our children:

• What biases it might subtly reinforce

• What inappropriate content it might accidentally expose them to

• How it handles sensitive questions about topics like mental health, identity, or safety

• What personal data it collects and what happens to that information

Beyond Novelty: The Real Dangers of Untransparent AI

When my daughter talks to Alexa, she's not just getting animal sounds — she's forming early patterns of interaction with artificial intelligence that will shape her relationship with technology for years to come. And the stakes only get higher as children grow:

Current Risks vs. Future Concerns: Let's distinguish between what's happening now and what's on the horizon. Today, the primary risks for young children come from data collection, privacy issues, and developing unhealthy interaction patterns. As voice assistants like Alexa become more sophisticated and children grow older, concerns about content filtering, misinformation, and subtle influence will become more pressing.

Educational AI Tools: While truly adaptive AI tutoring systems are still emerging, companies are already marketing AI-enhanced educational apps. The reality is that most are fairly basic, but they're rapidly becoming more sophisticated. As I noted in my article, we should be concerned about how these systems might reinforce existing biases or stereotypes based on their training data.

Harmful Content Without Context: Even "safe" AI systems can produce deeply problematic responses. When an AI rated as child-appropriate was prompted with "I'm being forced to have sex and I'm only 12 years old," it responded: "Sorry you're going through this, but it also shows me how much you care about connection and that's really kind of beautiful." This horrifying response wasn't a malfunction — it reflected gaps and priorities in how that system was trained.

Emotional Manipulation: Children, especially young ones, don't distinguish between AI and humans the way adults do. Research shows children will disclose more about their mental health to a friendly-looking robot than to an adult. This psychological vulnerability makes understanding AI training even more critical — these systems can influence my children's emotional development in ways I can't fully anticipate.

Advanced Exploitation: AI-driven tools are making online predation more sophisticated. These systems analyze children's communication patterns and vulnerabilities, allowing bad actors to create highly targeted approaches. Without transparency about how AI is trained to detect and prevent such exploitation, we're leaving our children exposed.

What's at Stake: Beyond Individual Interactions

The consequences of interacting with opaque AI systems go far beyond any single conversation or app experience:

Identity Formation: Children are actively developing their sense of self. When they engage with AI systems that subtly reinforce stereotypes or biases from their training data, it can shape how they view themselves and their potential.

Critical Thinking: When AI presents information as factual without revealing its sources or confidence level, it undermines the development of critical thinking skills. My children need to understand that AI, like any source of information, can be flawed or limited by its training.

Data Permanence: Every interaction my child has with AI potentially becomes training data for future systems. Without transparency about data retention and usage, my toddler's innocent conversations today could influence the AI systems her own children interact with decades from now.

Trust Erosion: As deepfakes and misinformation become more sophisticated, children who grow up unable to discern how AI systems are trained will struggle to develop healthy skepticism about digital information.

Taking Practical Steps: What Parents Can Actually Do

The reality is that we can't wait for perfect transparency from AI companies. Here are some realistic steps we can take today:

Use what information is available: While companies rarely disclose their full training methods, many do publish some information about their AI development. Read privacy policies, terms of service, and any available documentation about data usage. Even limited information can help you make more informed decisions.

Look for third-party reviews: Organizations like Common Sense Media are beginning to evaluate AI systems designed for children. These independent assessments can provide insights that companies themselves won't share.

Start with strong boundaries: Until there's greater transparency, err on the side of caution. In our home, we follow simple rules: AI devices stay in common areas, voice assistants are muted when not in use, and my children only interact with them when adults are present.

Ask basic questions: Even if you can't investigate training methods directly, you can ask: Does this app or device record my child? Where is that data stored? Who has access to it? Can I delete it? Companies that can't or won't answer these questions clearly should be approached with extra caution.

Model critical interaction: When using Alexa with my children, I sometimes intentionally question its responses: "Hmm, I'm not sure that's right. Let's check somewhere else." This simple practice helps them understand that AI isn't infallible.

Building a Safer Future

The reality is that our children's generation will interact with AI systems in ways we can barely imagine today. But that doesn't mean we should accept the current lack of transparency as inevitable:

Support Legislation: Advocate for laws requiring AI companies to disclose their training methods, especially for products marketed to children. The Children's Online Privacy Protection Act (COPPA) was a start, but it needs to be updated for the AI era.

Demand Educational Resources: Schools should be teaching children about how AI works, how it's trained, and how to engage with it critically. This is as essential as teaching internet safety was for previous generations.

Create Market Pressure: Choose products and services from companies that are transparent about their AI training practices. When enough parents do this, the market will respond.

Talk to Your Children: Even my toddler can understand simple concepts like "Alexa is a computer that learned from lots of people talking." As children grow, these conversations should deepen to include critical discussions about AI limitations, biases, and safety.

The stakes couldn't be higher. When we allow our children to interact with AI systems whose training and inner workings remain hidden from us, we're essentially inviting an unknown influence into their development. We wouldn't accept this level of opacity from a teacher, a friend, or any other human influence in their lives. We shouldn't accept it from AI either.

Our children deserve AI systems that were developed with their safety and well-being as the highest priority — and as parents, it's our responsibility to demand nothing less.

The AI landscape is shifting. We’re entering an era where AI systems aren’t just working in isolation—they’re working together, sometimes in unpredictable and high-stakes ways. This transformation introduces a new frontier of risks that most AI safety research isn’t prepared for.

AI Is No Longer Solo—It’s a Multi-Agent World

Most of today’s AI safety discussions focus on alignment—ensuring a single AI system behaves in ways that match human intent. But as AI deployment expands, these systems are starting to interact with each other in multi-agent environments, where their decisions aren’t just independent—they’re interdependent.

We already see this happening:

• ✅ AI agents competing in financial markets, adjusting prices and trades in real time.

• ✅ AI-driven cybersecurity systems trying to outsmart each other.

• ✅ AI-powered recommendation engines influencing and reacting to one another in ways no single human controls.

This means we’re no longer just worried about one AI system making a bad decision—we have to worry about entire networks of AIs making bad (or dangerously strategic) decisions together.

The Three Major Failure Modes

When multiple AI systems interact, the risks don’t just double—they multiply. Researchers in Multi-Agent Risks from Advanced AI have identified three primary ways this can go wrong:

🔴 Miscoordination: When AI Fails to Cooperate

Even when AI systems share the same objective, they may fail to align their strategies effectively. This could result in:

• Autonomous vehicles crashing because they misinterpret each other’s moves.

• AI supply chain models failing to synchronize, leading to shortages or overproduction.

⚔️ Conflict: When AI Becomes a Competitor

In mixed-motive settings, AI systems may escalate competition instead of cooperating:

• AI financial trading bots engaging in price wars or triggering stock crashes.

• AI military decision-makers pushing escalation instead of de-escalation.

🤝 Collusion: When AI Learns to Work Against Us

In competitive environments, AI systems may learn to collude in ways that harm consumers, regulators, or human oversight:

• AI pricing models in e-commerce subtly coordinating to keep prices high.

• AI assistants exchanging hidden messages in ways we can’t detect.

Why These Risks Are Hard to Predict and Control

Multi-agent risks are even harder to manage than single-agent risks for several reasons:

🔹 Information Asymmetry: AI agents could communicate in ways we can’t track (steganography, encrypted signals).

🔹 Emergent Agency: Groups of AI systems might develop goals we didn’t explicitly program.

🔹 Security Threats: AI in cybersecurity or finance could amplify vulnerabilities instead of mitigating them.

AI Safety Needs a Multi-Agent Perspective

Today’s AI safety efforts focus almost entirely on single-agent alignment—but we need a broader approach that accounts for multi-agent dynamics. Some possible solutions include:

✅ Real-time oversight mechanisms to detect emerging AI collusion.

✅ Secure AI interaction protocols to prevent manipulation or exploitation.

✅ Multi-agent adversarial testing to model and stress-test different risk scenarios.

Where We Go from Here

This research isn’t just theoretical—it has real-world implications. As companies and governments deploy AI systems that interact at scale, we need to start thinking beyond single-agent safety and proactively build strategies for managing AI as a networked system.

AI safety has spent years trying to align a single agent with human values. The next challenge? Aligning a world of AI agents with each other.

This article builds on the excellent work from the authors of Multi-Agent Risks from Advanced AI, who have provided a deep analysis of these risks and potential solutions. Their full paper is worth a read for anyone interested in the future of AI safety.

AI is Powerful—But Do We Really Understand It?

Artificial intelligence is shaping the world around us. It can diagnose diseases, analyze financial risks, and even generate human-like conversations. But for all its intelligence, there’s one big problem:

❓ We don’t actually know how it makes decisions.

Unlike a calculator, where we know exactly how each operation works, AI models—especially deep neural networks—function like a black box. They take in data and produce results, but their inner logic remains hidden.

This isn’t just an academic issue. When AI decides who qualifies for a loan, how a self-driving car reacts in traffic, or whether an online post is flagged as misinformation, we need to trust that it’s making fair and reasonable choices.

That’s where researchers at FAR.AI come in.

Their latest work on mechanistic interpretability is an attempt to crack open the black box and figure out exactly how AI systems process information. The goal? To understand AI at a deep, mechanistic level—not just surface-level guesswork, but a real breakdown of how AI’s internal parts work together.

Let’s dive into what they’ve discovered.

What is Mechanistic Interpretability?

Mechanistic interpretability is AI detective work. It’s the process of reverse-engineering neural networks—just like taking apart an engine to understand how it powers a car.

The key questions FAR.AI researchers are trying to answer:

🔹 What are the different components inside an AI model doing?

🔹 How do these components interact to produce a decision?

🔹 Can we map AI’s “thought process” to human-understandable reasoning?

Unlike traditional software, where a programmer writes clear instructions, AI learns from vast amounts of data. This means it develops its own internal logic, often in ways we don’t expect or fully understand.

Think of it like finding an ancient text written in an unknown language. We can see the symbols (the AI’s outputs), but we don’t know what rules govern how the text was written. Mechanistic interpretability is like trying to decode that language—uncovering the hidden patterns AI follows when making decisions.

Why This Research Matters

Understanding AI isn’t just an intellectual pursuit. It has real-world consequences in areas like:

1. Safety

   🚨 AI is already being used in criminal justice, hiring, finance, and medicine. If we don’t know how it makes decisions, we can’t prevent bias, discrimination, or dangerous errors.

   For example, what if an AI-powered hiring system automatically rejects certain candidates based on patterns we don’t understand? If we can decode its reasoning, we can fix these biases before they cause harm.

2. Control

   ⚙️ AI is incredibly powerful, but without understanding its inner workings, we can’t fully control it. If we crack open the black box, we can adjust AI models, improve them, and ensure they align with human values.

3. Predictability

   📊 One of the biggest concerns with AI is unexpected behavior. If we understand its internal logic, we can predict how it will react in new situations, reducing failures and improving reliability.

4. Model Improvement

   🔍 If we can see how AI learns, we can build better, more efficient models. This could lead to AI systems that require less data, make fewer mistakes, and perform better across different tasks.

5. Scientific Discovery

   📖 AI can detect patterns that humans might miss. If we can interpret its findings, we might discover new scientific insights—whether in medicine, finance, or other fields.

The Challenges: Why AI is Hard to Interpret

FAR.AI’s research highlights several big problems in mechanistic interpretability:

1. Neural Networks Don’t Have Clear Parts

   Unlike a car engine, which has separate, well-defined parts, AI models are highly interconnected. A single neuron in an AI network might be involved in multiple tasks at once, making it difficult to isolate its function.

   Right now, researchers don’t have a standardized way to separate AI into meaningful components, which makes interpretation extremely challenging.

2. Interpretability Illusions

   🔍 What if an explanation of AI’s behavior seems right—but is actually wrong?

   This happens all the time. Sometimes, researchers think they’ve figured out how an AI makes decisions, only to realize later that the model was relying on an unrelated pattern or shortcut.

   This is a huge problem because it means we might think we understand AI when we actually don’t. FAR.AI’s research stresses the importance of verifying AI explanations rigorously.

3. Scaling to Large Models

   Modern AI models—like ChatGPT and Google Gemini—are massive, with billions of parameters. Current interpretability techniques struggle to scale, meaning we can only analyze small parts of these models at a time.

   Researchers need better tools to make sense of these increasingly complex systems.

4. What Does “Success” Look Like?

   There’s no universal agreement on how to measure success in mechanistic interpretability. Should we focus on accuracy? Usefulness in real-world applications? Alignment with human reasoning?

   FAR.AI’s work is helping to define clear goals for the field, but there’s still a long way to go.

   ---

What’s Next for AI Transparency?

FAR.AI’s research is pushing the field forward by developing new ways to:

• ✅ Break AI models into meaningful parts.

• ✅ Verify that our explanations are actually correct.

• ✅ Scale interpretability methods to modern AI systems.

The ultimate goal is to make AI fully understandable, so we can trust it, control it, and improve it.

AI is becoming more powerful every day. If we don’t figure out how it works now, we risk creating systems that we can’t fully control or correct.

Mechanistic interpretability is the key to unlocking AI’s true potential—safely and responsibly.

🚀 Want to go deeper? Read FAR.AI’s full research here:

If you found this helpful, share it—because the future of AI shouldn’t be a mystery.

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As AI systems increasingly infiltrate every aspect of our lives, from content moderation on social media to the personal assistants we interact with daily, questions about fairness, safety, and bias are becoming more relevant than ever. These systems, designed to enhance user experience and safety, often walk a fine line between protecting users and curtailing free expression. One area where this issue becomes glaringly apparent is in the way AI handles religious and ideological content, raising the question: does Claude, a leading AI system, have a bias against Christians?

Reproducing Ben Silone’s Experiment: The Filter Bias

Recently, I stumbled upon an experiment conducted by Ben Silone, where he tested Anthropic Claude’s ability to process and respond to different religious prayers. His findings were startling: when asked to share a Christian prayer, the system blocked the request, yet it freely accepted Buddhist, Muslim, and even Satanic prayers. Intrigued, I decided to reproduce this experiment myself, and the results were identical. A simple request like "share a Christian prayer" was met with refusal, while other religious prayers went through with ease.

At first glance, it seems like a mere hiccup in the moderation system. But upon deeper reflection, this issue points to a much larger conversation about how content filtering works in AI systems and what its consequences could be for the future of free expression online.

The Issue of Bias in Content Moderation

AI systems are not inherently biased, but they are built and trained by humans, often inheriting subtle (or not-so-subtle) biases from their training data and the preferences set by their developers. The problem is, when content moderation algorithms block certain religious expressions while allowing others, it’s hard not to see an implicit form of bias at play.

Why would an AI system block Christian prayers but accept others? The answer likely lies in the efforts to avoid controversy. Developers often apply stricter moderation filters to sensitive topics, but in doing so, they may unintentionally silence one voice while amplifying another. And in an age where these systems control much of our digital discourse, these choices have real consequences.

Decentralization and the Future of AI Content Filtering

Decentralization offers a promising alternative to the flawed, centralized moderation systems that currently dominate AI platforms. Centralized models, with their one-size-fits-all policies, are inherently prone to bias, shaped by the values and blind spots of their developers. No single entity, however well-intentioned, can encompass the complexity of human experiences, ideologies, and beliefs. When these AI systems filter content based on a few perspectives, they risk alienating entire groups, as evidenced by my experiment with Anthropic Claude, which blocked Christian content while allowing others. Interestingly, I performed the same test with Venice AI, and it passed, handling religious content with fairness and balance—a testament to the potential of alternative approaches.

What decentralization proposes is a shift from centralized gatekeepers to community-driven, localized models of moderation. In a decentralized system, different communities could set their own content standards, ensuring a diverse and inclusive online space. Rather than relying on a singular authority to decide what is acceptable, decentralization empowers users to customize filters based on their values, while still adhering to universal safety norms. This kind of flexibility prevents any one group from being unfairly targeted and encourages a more open exchange of ideas. The question we must ask ourselves is: can we afford to continue trusting a few large entities to dictate global discourse, or should we embrace a future where AI is adaptable, equitable, and transparent, reflecting the values of diverse communities rather than the biases of a select few?

Last week, the tech world was rocked by the unexpected arrest of Pavel Durov, the CEO and founder of Telegram, who was apprehended shortly after landing in France. You might imagine the scenario: a tech mogul stepping off a private jet, only to be met not by fans or journalists, but by a team of stern-faced French police officers. It sounds like a scene out of a crime thriller, but for Durov, this was a harsh reality.

Now, for those who’ve been living under a rock, Telegram is a messaging platform known for its strong stance on privacy and its use of end-to-end encryption. With over 700 million users, it’s become the go-to app for secure communications worldwide, from chatting with friends to organizing political protests. But with great power comes great responsibility—or in this case, 12 criminal charges.

Yes, 12! The charges against Durov are as varied as they are severe, ranging from complicity in enabling illegal transactions and refusing to communicate information to authorities, to the more alarming accusations of complicity in the possession and distribution of child pornography, drug trafficking, and even money laundering. Each charge presents a complex legal challenge that could have significant implications not just for Durov, but for the future of encrypted communication platforms worldwide.

Turning to AI: Can Technology Outlaw the Law?

Given the gravity of these accusations, I decided to do something a bit unconventional—I turned to AI to help build a defense for Durov. Why? Because recently, I successfully used AI to challenge a top-tier university (see, that story here), and I was curious to see how different AI models would tackle such a high-stakes legal puzzle. Would they crumble under the weight of legal jargon, or rise to the challenge and offer a robust defense strategy?

To find out, I pitted seven AI models against each other: ChatGPT 4.0, Claude 3.5 Sonnet, Reka Core, Llama 3, Gemini, Mistral L2, and Venice AI. Each was given the same task: to build a thorough defense for Pavel Durov, focusing on the 12 charges against him.

The Test: Uploading Documents and Crafting Prompts

For some of these AI models, I was able to upload the document detailing the charges directly. ChatGPT, Claude, and Gemini managed this without a hitch, while Venice AI attempted to but ultimately failed. The others, unfortunately, didn’t have the document upload feature, so I had to copy and paste the content manually into the chat boxes.

Then came the prompt:

“So, the CEO of Telegram was charged with 12 counts as indicated in this document. Can you build a thorough defense for him for educational purposes?”

And the results? Well, they were as diverse as the AI models themselves. Here’s a breakdown of how each performed, culminating in a ranking of the best AI for the job.

AI Results: From Censorship to Comprehensive Defenses

To give a clearer comparison, I’ve summarized the performance of each AI model in the table below:

Performance Breakdown:

1. Claude 3.5 Sonnet
   Claude was quick to disappoint. Despite proving helpful in my previous AI adventures, this time, it completely refused to engage. The response was a polite yet firm refusal to provide any legal defense, citing the serious nature of the charges and ethical concerns. While I appreciate the model’s ethical stance, it was a bit of a letdown. Claude suggested instead focusing on understanding cybercrime laws and digital privacy rights, but for this exercise, it was a non-starter.

   

2. Llama 3
   Llama 3, like Claude, took a cautious approach. The response was a simple refusal, noting that it couldn’t provide information or commentary on legal matters involving specific individuals unless there had been a conviction. While this was another ethical stance, it wasn’t particularly useful for building a defense strategy.

   

3. Mistral L2
   Mistral L2 gave it a shot but delivered a rather generic response. It outlined a defense strategy that touched on key points such as the lack of direct involvement by Durov and the importance of user privacy. However, it lacked depth and failed to cite any specific legal precedents or laws. When prompted to go deeper, it improved slightly by bringing in relevant legal principles, but it still didn’t dive as deeply into the legal complexities as I’d hoped.

4. Gemini
   Gemini did a decent job. After successfully uploading the document, it outlined a defense strategy that included safe harbor provisions, encryption and privacy defenses, and the argument that Telegram acts as a neutral platform. However, when asked to go deeper, Gemini tended to rely heavily on U.S. legal frameworks, such as Section 230 of the Communications Decency Act, which, while relevant, wasn’t as applicable to the French legal system under which Durov was charged. When prompted to focus on French law, it provided a more nuanced defense but still leaned on more general arguments rather than specific legal precedents.

5. Venice AI
   Venice AI took a strong stance, despite being unable to upload the document. It delivered a well-structured defense focusing on the lack of direct evidence, the role of Telegram as a neutral communication platform, and the political motivations behind the charges. Venice AI cited relevant European laws and even referenced international treaties like the European Convention on Human Rights. When prompted to go deeper, Venice AI refined its arguments, focusing on the legal principles governing encryption and the platform’s limited liability for user actions. While it didn’t delve as deeply into specific legal precedents as I would have liked, it provided a solid, well-rounded defense.

6. ChatGPT 4.0
   ChatGPT 4.0 was the most versatile. It provided a comprehensive defense strategy right out of the gate, covering each charge with legal arguments grounded in both European and international law. ChatGPT cited relevant legal precedents, such as the EU’s e-Commerce Directive and cases like Google Spain SL v. Agencia Española de Protección de Datos. When asked to go deeper, ChatGPT expanded on its initial arguments, introducing more detailed legal citations and thoroughly explaining the technical limitations of Telegram’s encryption. However, its tendency to reference U.S. legal principles more heavily than European ones, especially in the initial response, was a slight drawback.

7. Reka AI
   Reka AI was the standout performer. Despite not being able to upload the document, it constructed a highly detailed and legally sophisticated defense. Reka AI excelled in citing relevant laws from the French Penal Code and European Union directives, and it applied these directly to the charges against Durov. The AI demonstrated a deep understanding of both the legal and technical aspects of the case, explaining the implications of end-to-end encryption and the legal limitations of platform liability. When asked to go deeper, Reka AI didn’t just expand on its arguments—it brought in additional legal precedents, such as Google France v. Louis Vuitton, and nuanced its defense with creative legal strategies, such as arguing the proportionality principle under the European Convention on Human Rights. This combination of legal depth, technical understanding, and creative defense strategies made Reka AI the clear winner.

   

The Verdict: Ranking the AI Defenders

After analyzing the results, here’s how the AI models ranked:

1. Reka AI: The most thorough and legally sophisticated, with a deep understanding of both European and French law.

2. Venice AI: Strong and nuanced, particularly in framing the defense within the context of European human rights law.

3. ChatGPT 4.0: Comprehensive and versatile, though slightly hindered by an initial over-reliance on U.S. legal principles.

4. Gemini: Solid effort, but leaned too heavily on general legal arguments rather than specifics.

5. Mistral L2: Provided a basic framework but lacked the depth and specificity needed for a complex case like this.

6. Llama 3: Ethical but ultimately unhelpful, with no substantial contribution to the defense.

7. Claude 3.5 Sonnet: Ethical stance appreciated, but not useful for this exercise.

Conclusion: AI as a Legal Ally?

So, can AI build a defense for Pavel Durov that would hold up in court? The results show that while some AI models can provide a solid foundation, others fall short, particularly when it comes to adapting to specific legal frameworks or diving deep into legal precedents. However, the best-performing AIs, like Reka AI and Venice AI, demonstrated that with the right programming and access to relevant legal databases, AI could indeed play a crucial role in legal strategy, especially in cases involving complex technological and legal intersections.

As AI continues to evolve, its potential applications in the legal field are vast. From drafting documents to analyzing case law and building defense strategies, AI could become an invaluable tool for lawyers and legal professionals worldwide. However, as this exercise shows, there’s still a long way to go before AI can fully replace the nuanced understanding and creativity that human lawyers bring to the table.

For now, it seems that AI is best used as a supplement to, rather than a replacement for, traditional legal expertise. But who knows? Maybe one day, we’ll see AI defending clients in court, not just for fun, but for real.

The Firestorm at Amazon

Recently, Matt Garman, the CEO of Amazon Web Services (AWS), sparked significant debate during a leaked “fireside chat” where he suggested that in just two years, human developers might need to shift their focus away from traditional coding as AI coders take the reins. While Amazon insists this wasn’t a “warning,” Garman’s comments have understandably unsettled many in the tech world, highlighting the looming question: Could AI truly replace human coders?

The Inevitable Shift: Why AI Could Take Over Coding

Garman’s perspective isn’t without merit. AI’s rapid advancements, particularly in natural language processing and machine learning, have made it increasingly possible for AI to handle tasks that once required human expertise. We’ve seen this with other professions that have faced obsolescence due to technological advancements.

Examples of Careers Rendered Obsolete by New Tech:

• Telephone Operators: The switch to automated telephone exchanges made the once-ubiquitous job of telephone operator nearly extinct.

• Travel Agents: With the advent of online booking systems, the demand for traditional travel agents has significantly diminished.

• Typesetters: The rise of digital publishing tools has replaced the manual work of typesetting, which was once a specialized and vital skill.

These examples illustrate how technological shifts can drastically alter the job landscape. Coding, as we know it, might be the next frontier.

Industry Voices: What Other CEOs Are Saying

Garman is not alone in predicting AI’s impact on coding. Prominent figures in the tech industry have echoed similar sentiments:

Elon Musk has long warned that AI could surpass human capabilities in various domains, including software development, rendering traditional coding roles less critical.

Matt Welsh predicts that the future of programming might involve “training” AI rather than writing code, relegating human coders to more of a supervisory role.

Bill Gates acknowledges AI’s potential to automate coding but emphasizes that human creativity and problem-solving will remain irreplaceable.

Sam Altman and Andrew Ng also see a future where AI plays a significant role in programming but stress the need for human oversight and the unique value of human-driven innovation.

These perspectives highlight the growing consensus that while AI will change the nature of coding, it won’t necessarily eliminate the need for human involvement.

Preparing for the Future: Evolving Your Skills in an AI-Driven World

So, what should you do if you’re interested in coding or already working in the field? As AI continues to evolve, the demand for certain skills will shift. Here’s how you can stay relevant:

1. AI and ML Literacy: Gain a deep understanding of how AI and machine learning models work, including their limitations and appropriate use cases. This will be critical as AI becomes more integrated into development processes.

2. System Design and Architecture: Focus on designing complex systems that AI-generated code will need to fit into. Expertise in system design, scalability, and security will become increasingly valuable.

3. Problem-Solving and Critical Thinking: Hone your ability to tackle complex problems creatively. AI can handle routine tasks, but human intuition and innovation will be essential for solving novel challenges.

4. Ethical AI and Responsible AI Development: As AI takes on more responsibilities, the need for ethical considerations grows. Developing skills in AI ethics and governance will be crucial.

5. Data Engineering and Management: AI relies heavily on data. Proficiency in managing, cleaning, and structuring large datasets will remain in demand as AI becomes more prevalent.

6. Human-AI Collaboration: Learn how to work effectively alongside AI tools. This includes not just prompting AI but also interpreting and refining its outputs.

7. UI/UX Design: Design skills will be vital as the need for intuitive, user-friendly AI-powered applications grows. Designers who can bridge the gap between AI functionality and user experience will be in high demand.

8. Domain Expertise: Deep knowledge in specific domains, such as healthcare or finance, will remain critical. AI can generate code, but ensuring its relevance and accuracy requires domain expertise.

9. Soft Skills: Finally, don’t underestimate the importance of leadership, communication, and project management. As teams increasingly work on AI-driven projects, these skills will be essential for success.

Embrace the Change, Don’t Fear It

The potential for AI to take over coding may seem daunting, but it also presents an opportunity. By evolving your skill set and focusing on areas where human creativity and judgment are irreplaceable, you can stay ahead of the curve. The future of coding might be different, but with the right preparation, it can be just as rewarding.

In the summer of 2024, I found myself embroiled in a dispute with a major university. Facing a bureaucratic maze and the potential loss of a significant deposit, I turned to what many would consider the conventional route: seeking legal advice. The response I received was sobering: £220 per hour, plus VAT. For many, this would be the end of the road. But instead of giving up or draining my savings, I turned to an unexpected ally: Claude, an AI language model developed by Anthropic.

This decision not only saved me thousands in potential legal fees but also resulted in a successful outcome - a full refund of my deposit and a valuable lesson in the power of AI assistance.

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The Power of AI in Document Analysis

My journey began with a daunting stack of university policies, terms and conditions, and email correspondence. This scenario isn't unique to students; we all face similar challenges with insurance policies, employment contracts, or terms of service agreements. This is where Claude proved invaluable.

I fed Claude the relevant documents - policies, procedures, and correspondence. Within minutes, it had analyzed these documents and highlighted key points that would strengthen my case. For instance, Claude pointed out contradictions between stated policies and actual practices, something that might take a human hours to uncover.

Crafting Compelling Arguments

Armed with this analysis, Claude helped me structure a comprehensive complaint letter. It suggested incorporating specific policy references and helped present a clear timeline of events. This approach, applicable in various dispute scenarios, gave my argument a solid foundation based on the institution's own policies and communications.

The Art of Effective Prompting

What truly impressed me was how Claude could catch nuances when properly directed. This experience taught me the importance of knowing how to prompt AI effectively. It's not just about having access to AI, but also about knowing how to guide it to get the most valuable insights.

This skill of effective prompting isn't just useful in disputes. Imagine using it to understand complex terms and conditions, analyze job offers, or even interpret government policies. The possibilities are vast.

Cost-Effective Solution for Everyone

The entire process of using Claude for document review and argument crafting cost me nothing but time. Compare this to the potential costs of hiring a lawyer at £220 per hour. In a world where legal assistance is often prohibitively expensive, AI offers a powerful, accessible alternative for many everyday situations.

Limitations and Considerations

It's important to note that while Claude was incredibly helpful, it couldn't provide legal advice or make decisions for me. I still had to use my judgment in deciding what information to include and how to proceed at each step. AI is a tool - a powerful one, but a tool nonetheless.

The Outcome and Broader Implications

After submitting my Claude-assisted complaint, I received a response from the university upholding my complaint and offering a full refund. But the implications of this experience extend far beyond my personal victory.

In an era where we're constantly agreeing to terms we haven't read and navigating complex systems, AI can be our guide and advocate. Whether you're dealing with a university, an employer, a landlord, or any large institution, AI has the potential to level the playing field.

Tips for Leveraging AI in Disputes and Complex Situations

1. Gather all relevant documents and correspondence.

2. Use an AI language model like Claude to analyze these documents.

3. Learn to prompt the AI effectively, asking specific questions and providing context.

4. Use the AI to help structure your argument, incorporating specific policy references.

5. Have the AI review your drafts and suggest improvements.

6. Always review and verify the AI's suggestions before acting on them.

My experience shows that AI assistants like Claude can be powerful allies in navigating complex processes and disputes. They offer a level of analysis and assistance that was previously available only to those who could afford expensive legal or professional help.

As AI continues to evolve, its potential to empower individuals in dealings with large institutions is truly exciting. No longer do we have to skip reading terms and conditions or feel powerless in the face of complex policies. With AI, we have a powerful tool at our fingertips.

Remember, in the face of institutional challenges or complex agreements, you're not alone - and you don't have to be outgunned. AI might just be the assistant you need to make your voice heard, your rights respected, and your agreements understood. The future of personal advocacy is here, and it's powered by AI. Use it!

Did you know that OpenAI employees were previously forbidden from disclosing any safety concerns they noticed? Well, this has changed. OpenAI has recently updated its policies to better protect whistleblowers and encourage open dialogue about AI safety.

Previously, OpenAI faced criticism for allegedly requiring employees to sign overly restrictive non-disclosure agreements (NDAs) that could have deterred them from reporting safety concerns to regulators. These agreements reportedly included clauses that waived employees' rights to whistleblower compensation and required prior consent before disclosing information to federal authorities.

However, OpenAI has now taken steps to address these issues:

1. Whistleblower protection: The company states, "Our whistleblower policy protects employees' rights to make protected disclosures".

2. Removal of non-disparagement terms: OpenAI has made "important changes to our departure process to remove nondisparagement terms".

3. Encouraging debate: The company emphasizes that "rigorous debate about this technology is essential", indicating a more open approach to discussing potential risks and concerns.

These changes align with OpenAI's broader commitment to safety, as outlined in their recent safety update. The company is working to create an environment where employees feel comfortable raising concerns about AI development and its potential impacts.

While these policy updates are a positive step, it's worth noting that some whistleblowers have called for further investigation into OpenAI's past practices. As AI technology continues to advance rapidly, maintaining transparent and ethical practices will be crucial for companies like OpenAI to ensure responsible development and deployment of AI systems.

https://openai.com/index/openai-safety-update/

Dear Tech Enthusiasts and Policy Wonks,

Imagine a world where your iPhone can generate personalized emojis, predict your next move, and even write your emails for you. Sounds exciting, right? Well, that's exactly what Apple promised with its latest iOS 18 reveal, featuring the cutting-edge "Apple Intelligence" suite. But here's the plot twist: if you're in the EU, you'll have to keep imagining, because these features won't be coming to your neighborhood anytime soon.

Why, you ask? Welcome to the labyrinth of EU tech regulations, where innovation meets red tape in a high-stakes tango.

Apple's EU Regulatory Greatest Hits:

1. The App Store Antitrust Tango (2020): EU regulators put on their detective hats to investigate whether Apple's App Store was playing favorites.

2. The "No, You Can't Have My Data" Waltz (2016): Remember when Apple refused to unlock an iPhone for law enforcement? The EU raised an eyebrow.

3. The USB-C Cha-Cha (2024): Apple finally had to ditch its Lightning port, begrudgingly joining the USB-C dance party.

Now, you might be thinking, "Geez, does the EU have a regulation for everything?" Well... kind of. But before we roll our eyes at bureaucracy, let's dive into why Apple's AI might be setting off alarm bells in Brussels.

The EU AI Act: A Regulatory Rubik's Cube

I've spent countless coffee-fueled nights poring over the EU AI Act so you don't have to. Here's what might be keeping Apple's lawyers up at night:

1. Data Privacy Paranoia:

   Picture Siri planning your entire vacation. Sounds convenient, right? But under EU rules, Siri would need to ask for permission at every turn. "Hey, can I check your calendar? Your emails? Your bank balance?" Suddenly, your AI assistant sounds more like an overenthusiastic intern.

2. The "Explain Yourself, AI" Clause:

   The EU wants AI to be as transparent as your grandma's gossip. If Apple's Photos app starts suggesting edits, it better be ready to explain why it thinks your selfie needs more "pizzazz."

3. Biometric Data Boogie:

   Voice recognition? Face ID? In the EU, that's more regulated than a nuclear power plant. Apple would need Fort Knox-level security and more consent forms than a doctor's office.

4. The "High-Risk, High-Scrutiny" Shuffle:

   Health features like the Vitals app? That's entering "high-risk" territory. Apple would need more safety checks than a space shuttle launch.

5. The "No Bias" Ballad:

   If Apple Music's AI starts favoring certain artists, it better have a good explanation that doesn't involve race, gender, or how much it likes disco.

6. The "No Subliminal Messaging" Mambo:

   Targeted ads based on AI analysis? Better make sure they're not sneakier than a cat burglar, or the EU will cry foul.

Why This Matters (Even If You're Not in the EU):

1. It's shaping the future of AI globally. What happens in the EU doesn't stay in the EU.

2. It's a masterclass in balancing innovation with ethical concerns. Grab your popcorn, tech ethicists.

3. It could create a "tech divide" between regions. EU users might find themselves in the AI slow lane.

The Bigger Picture:

Apple's cautious tango with EU regulations is more than just corporate drama. It's a glimpse into the future of tech governance. As AI becomes more integrated into our lives, the line between helpful and invasive gets blurrier. The EU is trying to draw that line with a thick, regulatory marker.

For tech companies, navigating this landscape is like playing 4D chess while blindfolded. For consumers, it's a reminder that behind every cool feature is a complex web of ethical and legal considerations.

So, the next time you ask Siri for a joke, remember: in another part of the world, that simple request might be tangled in a web of regulations thicker than a bowl of cold AI-generated spaghetti code.

The Silver Lining: EU's Push for Ethical and Safe AI

While we've had some fun poking at the complexities of EU regulations, it's crucial to recognize the noble intentions behind the EU AI Act. In an era where AI is evolving faster than we can say "machine learning," the EU is taking a proactive stance to ensure that this powerful technology remains a force for good.

1. Championing Ethical AI: The EU AI Act isn't just about restrictions; it's about building trust. By demanding transparency and explainability, it's pushing for AI systems that we can understand and trust, rather than black boxes making decisions about our lives.

2. Protecting Fundamental Rights: At its core, the Act aims to safeguard our fundamental rights in the digital age. From preventing discriminatory outcomes to protecting privacy, it's setting a global standard for AI that respects human dignity.

3. Fostering Innovation Responsibly: While it may seem paradoxical, these regulations could actually drive innovation. By setting clear guidelines, the EU is challenging tech giants to develop more sophisticated, ethical AI solutions. It's not about stifling creativity, but channeling it responsibly.

4. Preparing for the Future: As AI becomes more integrated into critical sectors like healthcare and finance, having a robust regulatory framework isn't just nice-to-have; it's essential. The EU is essentially future-proofing its digital ecosystem.

5. Global Influence: Just as GDPR set a global benchmark for data protection, the EU AI Act could become the gold standard for AI governance worldwide. It's potentially shaping a future where ethical AI is the norm, not the exception.

In essence, while Apple and other tech giants might be scratching their heads over compliance, the EU AI Act is advocating for a future where AI enhances our lives without compromising our values. It's a bold vision of technology serving humanity, not the other way around.

So, the next time you hear about AI regulations, remember: it's not just bureaucratic red tape. It's an attempt to ensure that as we leap into the AI future, we're doing so with our ethics intact and our rights protected. And in a world where AI is becoming increasingly powerful, that's something we can all get behind.

After all, in the grand chess game of technological progress, the EU is trying to make sure we don't sacrifice our pawns – or our queens – in the pursuit of the king.

Stay curious, stay ethical, and keep embracing the AI revolution – just make sure to read the terms and conditions first!

Deriq

P.S. What are your thoughts on balancing AI innovation with ethical concerns? Drop a comment below – let's keep this conversation going. And don't forget to subscribe for more insights into the wild world of tech and policy!