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Bitwise Takes Over $277 Million Tokenized Carry Fund USCC

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USCC Fund AUM


  • On May 7, Bitwise Asset Management revealed that it is taking over as investment manager of the Superstate Crypto Carry Fund (USCC). 
  • This will be Bitwise’s first tokenized fund, and it will rename the fund as Bitwise Crypto Carry Fund while using the same ticker. Its transition is expected to be completed on June 1, 2026.
  • The USCC fund will use the “cash-and-carry” strategy, which is a strategy to generate yield from the gap between the spot and futures prices for Bitcoin, Ethereum, Solana, and XRP.

On May 7, Bitwise, a leading crypto asset management company, announced the launch of its inaugural tokenized fund, Bitwise Crypto Carry Fund (USCC), in partnership with Superstate. 

Amid the boom in the tokenized sector, this announcement from Bitwise has sparked euphoria among institutional investors. 

This is not the new fund, but Bitwise is taking over as the investment manager of Superstate’s Crypto Carry Fund (USCC). However, it will change its name to the Bitwise Crypto Carry Fund while keeping its same ticker like before, along with smart contracts and token address. According to the official announcement, the process of transition is expected to be completed on June 1, 2026. 

What is the USCC (Bitwise Crypto Carry Fund)?

USCC is a private tokenized fund that is only available for qualified investors. The fund generates yield through the cash-and-carry trade in the cryptocurrency market. This is also known as a basis trade. This technique helps the fund to generate yield from the usual premium that crypto futures prices trade at compared to spot prices for Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and XRP. 

The USCC fund also comes with staking returns on Ether and investment in United States Treasuries to provide additional yield. This fund allows investors to hold their shares as USCC tokens on blockchains like Ethereum, Solana, and Plume. Apart from this, they can also choose the traditional book entry form.

USCC Fund AUM

As of April 30, the USCC fund has more than $267 million in assets under management, while the recent data says it is around $277 million. This impressive AUM comes from its growing demand among crypto-based institutional investors such as hedge funds, venture funds, corporations, vaults, wealthy individuals, and protocols. As per the recent official data, the fund is offering around 4.36%.

This fund will allow investors to gain exposure of crypto based investment without trading them. 

This is the major announcement for institutional investors as the fund is directly coming from Bitwise and Superstate’s on-chain infrastructure. Also, it uses one of the best crypto yield strategies, which makes it attractive for institutional investors.

Amid the positive developments in the regulatory frameworks for digital assets, tokenized funds are actively grabbing the attention of major institutional investors as they offer numerous benefits. “Investors have become more interested in tokenized funds because they have the potential to leverage the unique characteristics of blockchains: 24/7 trading, utility in DeFi, transparency, and efficiency,” stated in the official post on X. 

Apart from this, tokenized funds like USCC can be easily integrated with DeFi protocols for purposes like collateral. Institutional investors can quickly transact these funds, which is a major pain point in finance. In traditional finance, there is a limitation on timings for trading such funds.

Hunter Horsley, CEO of Bitwise, stated in the official press release, “Capital markets are moving on-chain. It’s happening fast, and tokenized investment strategies are a core part of this platform shift. We’re thrilled to join Superstate’s best-in-class infrastructure with Bitwise’s track record in crypto asset management to continue to expand access to the full range of opportunities for investors in crypto.” 

Robert Leshner, Founder & CEO of Superstate, said, “We’re proud to welcome Bitwise as the investment manager of USCC. Bitwise is one of the most trusted names in crypto, and this partnership is a great example of what FundOS makes possible: world-class asset managers running tokenized funds on Superstate’s infrastructure.”

Success of Tokenized Funds Like BUIDL and the Tokenized U.S. Treasury Market

One of the major examples to understand the boom in the tokenized funds is the success story of BlackRock BUIDL, USD Institutional Digital Liquidity Fund. The fund was launched by BlackRock in 2024 on Ethereum through Securitize. This fund invests in United States Treasuries along with repurchase agreements and cash. After the launch, the fund became one of the largest tokenized funds. 

According to the rwa.xyz, the total asset value locked in BUIDL is revolving around $2.63 billion. This fund is also working as collateral on platforms like Binance, as well as trades on Uniswap. 

Apart from this, the Tokenized United States Treasury has also reached new heights thanks to growing demands. Recently, tokenized U.S. Treasuries have hit the mark of $15 billion in early May after soaring around $1.06 billion in the last 30 days.

In the last few months, the tokenized real-world asset sector without stablecoin has grown impressively. According to the on-chain data, the cumulative distributed asset value locked in tokenized assets is hovering around $31.12 billion after surging over 3% in the last 30 days.

On May 6, Ondo Finance, JPMorgan Kinexys, Mastercard, and Ripple announced that they executed the first cross-border and cross-bank redemption of a tokenized United States Treasury fund. According to the official statement, this transaction was executed in just 5 seconds on the XRP Ledger. In this transaction, they used a public blockchain network along with traditional banking infrastructure for real-time settlement. 

This is not the first time a financial giant like JPMorgan has executed such settlements on blockchain networks, as it has already processed trillions of dollars in transactions on its blockchain platforms.

Apart from this, other examples of tokenized products include Superstate, which also has its own USTB fund. This fund is holding tokenized short-duration Treasuries.

While demand for tokenized securities is growing continuously, it is also facing opposition from naysayers. Groups like the World Federation of Exchanges (WFE) have requested regulators in the official statement to apply the same full securities rules to tokenized stocks. 

Similarly, SIFMA (Securities Industry and Financial Markets Association) has also raised concerns about giving some exemptions for tokenized trading by saying that they might create a threat to investor protections. 

Also Read: Tokenized Stocks Surge Past $1.5B as Wall Street Hits Record Highs

A Coding Implementation to Recover Hidden Malware IOCs with FLARE-FLOSS Beyond Classic Strings Analysis

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In this tutorial, we explore how FLARE-FLOSS helps us recover hidden and obfuscated strings from a Windows PE file. We begin by setting up FLOSS and the MinGW-w64 cross-compiler. We synthesize a small malware-like executable that hides strings using multiple techniques, including static strings, stack-built strings, tight strings, and XOR-decoded strings. After that, we compare the limitations of the traditional string utility with FLOSS’s deeper static analysis and emulation-based string recovery. Through this process, we learn how analysts can uncover URLs, registry paths, suspicious APIs, and other indicators of compromise that plain string extraction often misses.

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import subprocess, os, sys, json, re, time
from pathlib import Path


def banner(t): print("\n" + "═"*72 + f"\n  {t}\n" + "═"*72)
def sh(cmd, quiet=False, check=False):
   r = subprocess.run(cmd, shell=True, capture_output=True, text=True)
   if not quiet:
       if r.stdout: print(r.stdout.rstrip()[:4000])
       if r.returncode and r.stderr: print("[stderr]", r.stderr.rstrip()[:1500], file=sys.stderr)
   if check and r.returncode: raise RuntimeError(cmd)
   return r


banner("STEP 1 — Install FLOSS + MinGW-w64")
sh("pip install -q flare-floss")
sh("apt-get -qq update && apt-get -qq install -y mingw-w64 binutils-mingw-w64", quiet=True)
sh("floss --version 2>&1 | head -3")

We set up the core Python imports, helper functions, and command runner used throughout the tutorial. We then install FLARE-FLOSS and the MinGW-w64 cross-compiler. Also, we verify the FLOSS installation by checking its version before moving into executable generation.

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banner("STEP 2 — Build a synthetic malware-like PE")
WORK = Path("/content/floss_tutorial"); WORK.mkdir(exist_ok=True); os.chdir(WORK)


SECRETS = [
   ("FAKE_FLAG_DECODED_SECRET",                0x37),
   ("  0x42),
   ("SOFTWARE\\Microsoft\\Run\\PersistDemo",   0x5A),
   ("kernel32.dll!VirtualAllocEx",             0x29),
]
def xor_arr(s, k): return ",".join(f"0x{(ord(c)^k)&0xff:02x}" for c in s)


c = [
   '#include <stdio.h>',
   '__attribute__((noinline)) static void xord(char* b, int n, int k){',
   '}',
   'int main(void){',
   '    puts("PLAIN_STATIC_HELLO_FROM_FLOSS_TUTORIAL");',
   '',
   '    volatile char stk[20];',
]
seq = "STACK_BUILT_STRING"
for i, ch in enumerate(seq): c.append(f"    stk[{i}]='{ch}';")
c += [f"    stk[{len(seq)}]=0;", "    puts((char*)stk);", "",
     "    volatile char tght[]={'T','I','G','H','T','-','S','T','R',0};",
     "    puts((char*)tght);", ""]
for i,(s,k) in enumerate(SECRETS):
   c += [f"    char enc{i}[] = {{ {xor_arr(s,k)}, 0x00 }};",
         f"    xord(enc{i}, {len(s)}, 0x{k:02x});",
         f"    puts(enc{i});"]
c += ["    return 0;", "}"]
(WORK/"sample.c").write_text("\n".join(c))
sh("x86_64-w64-mingw32-gcc -O0 -fno-stack-protector -o sample.exe sample.c -static-libgcc", check=True)
print(f"\n✓ sample.exe built ({(WORK/'sample.exe').stat().st_size:,} bytes)")
sh("file sample.exe")

We create a synthetic Windows PE file that serves as a safe malware analysis sample for learning string recovery. We hide strings using multiple techniques, including plain static text, stack-built strings, tight strings, and XOR-encoded secrets. We then compile the generated C source into sample.exe so FLOSS can analyze it like a real Windows executable.

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banner("STEP 3 — Classic `strings` baseline (what gets MISSED)")
classic = set(subprocess.run("strings -a -n 6 sample.exe", shell=True,
             capture_output=True, text=True).stdout.splitlines())
print(f"`strings` extracted {len(classic):,} candidates total.")
print("Coverage of our planted secrets in plain `strings`:")
planted = ["PLAIN_STATIC_HELLO_FROM_FLOSS_TUTORIAL", "STACK_BUILT_STRING", "TIGHT-STR"] + [s for s,_ in SECRETS]
for s in planted:
   hit = any(s in line for line in classic)
   print(f"  {'✓ FOUND ' if hit else '✗ MISSED'}  {s}")


banner("STEP 4 — Run FLOSS (vivisect static + emulation; ~30–90 s)")
t0 = time.time()
sh("floss --json sample.exe > floss.json 2> floss.log")
print(f"\n[FLOSS finished in {time.time()-t0:.1f}s]")
print("--- last lines of FLOSS log ---")
sh("tail -15 floss.log")

We run the traditional strings command first to understand what a basic string extraction tool can and cannot detect. We compare each planted secret against the classic output to identify which strings are found and which are missed. We then run FLOSS on the executable and save both the JSON output and the log file for deeper structured analysis.

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banner("STEP 5 — Parse FLOSS JSON output")
with open("floss.json") as f: data = json.load(f)


def extract(key):
   out = []
   for e in data.get("strings", {}).get(key, []):
       if isinstance(e, dict): out.append(e)
       else: out.append({"string": e})
   return out


static_s, stack_s = extract("static_strings"), extract("stack_strings")
tight_s,  decoded_s = extract("tight_strings"),  extract("decoded_strings")
buckets = {"static": static_s, "stack": stack_s, "tight": tight_s, "decoded": decoded_s}


print(f"  metadata.version : {data.get('metadata', {}).get('version','?')}")
for k,v in buckets.items(): print(f"  {k+'_strings':<17}: {len(v):>5}")


print("\nDecoded strings recovered (with decoder routine info):")
for e in decoded_s:
   s = e.get("string","")
   rtn = e.get("decoding_routine"); addr = e.get("address")
   rtn_s = f"0x{rtn:x}" if isinstance(rtn,int) else str(rtn)
   addr_s = f"0x{addr:x}" if isinstance(addr,int) else str(addr)
   print(f"  decoder={rtn_s:<12} at={addr_s:<12} → {s!r}")
print("\nStack / tight strings recovered:")
for e in stack_s + tight_s: print(f"  → {e.get('string','')!r}")

We load the FLOSS JSON output and organize the recovered strings into static, stack, tight, and decoded categories. We print the metadata and string counts to understand the overall recovery results. We also inspect decoded, stack, and tight strings to see which hidden values FLOSS successfully extracts.

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banner("STEP 6 — IOC hunting in the deobfuscated strings")
PATTERNS = [
   ("URL",          re.compile(r"https?://[^\s\"<>]+")),
   ("IP",           re.compile(r"\b(?:\d{1,3}\.){3}\d{1,3}\b")),
   ("PE/script",    re.compile(r"[A-Za-z0-9_]+\.(?:exe|dll|sys|ps1|bat)\b", re.I)),
   ("Win32 API",    re.compile(r"\b(?:Reg(?:Open|Set|Create|Delete)Key(?:Ex)?A?|VirtualAlloc(?:Ex)?|CreateRemoteThread|WinExec|LoadLibraryA?|GetProcAddress|InternetOpenA?)\b")),
   ("Registry",     re.compile(r"SOFTWARE\\\\?[A-Za-z0-9_\\\\]+", re.I)),
   ("Base64-like",  re.compile(r"\b[A-Za-z0-9+/]{24,}={0,2}\b")),
]
hits = []
for kind, items in buckets.items():
   for e in items:
       s = e.get("string","")
       for label, pat in PATTERNS:
           if pat.search(s): hits.append((kind, label, s))


if hits:
   print(f"{'BUCKET':<10}{'IOC':<14}STRING")
   print("-"*72)
   for kind,lbl,s in hits[:40]:
       print(f"{kind:<10}{lbl:<14}{s[:80]}")
   print(f"\n→ {len(hits)} IOC hits total. Note: most are inside the 'decoded' bucket")
   print("  — those would be invisible to plain `strings`!")
else:
   print("(no IOC pattern matches)")


banner("STEP 7 — Visualize string-type counts and length distribution")
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4.5))


labels = list(buckets); counts = [len(v) for v in buckets.values()]
bars = ax1.bar(labels, counts, color=["#5fa8d3","#62b6cb","#cae9ff","#ff7b7b"])
ax1.set_title("FLOSS strings by type"); ax1.set_ylabel("count")
for b,n in zip(bars,counts): ax1.text(b.get_x()+b.get_width()/2, n, str(n), ha="center", va="bottom")


for kind, items in buckets.items():
   lens = [len(e.get("string","")) for e in items]
   if lens: ax2.hist(lens, bins=30, alpha=0.55, label=f"{kind} (n={len(lens)})")
ax2.set_title("String-length distribution"); ax2.set_xlabel("characters")
ax2.set_ylabel("frequency (log)"); ax2.set_yscale("log"); ax2.legend()
plt.tight_layout(); plt.savefig("floss_summary.png", dpi=110); plt.show()


print("\n✓ Tutorial complete.")
print(f"   Artifacts: {WORK/'sample.exe'}, {WORK/'floss.json'}, {WORK/'floss_summary.png'}")

We search all recovered strings for useful indicators such as URLs, IP addresses, DLL names, Win32 APIs, registry paths, and base64-like values. We display each IOC match with its corresponding string bucket so we can understand where important evidence appears. We finish by visualizing string counts and length distributions, then save the final summary image as an artifact.

In conclusion, we built a complete hands-on workflow for analyzing obfuscated strings in a synthetic Windows executable using FLARE-FLOSS. We saw how simple command-line string extraction can miss important evidence, while FLOSS can recover decoded, stack-based, and tightly constructed strings that are useful during malware triage. We also parsed FLOSS’s JSON output, hunted for IOC patterns, and visualized the recovered string categories to make the results easier to understand. It gives us a practical foundation for using FLOSS in reverse engineering, malware analysis, and security research workflows.

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The post A Coding Implementation to Recover Hidden Malware IOCs with FLARE-FLOSS Beyond Classic Strings Analysis appeared first on MarkTechPost.

Are altcoins heading for a crash? – How a $2.6B leverage spike raises risk

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Are altcoins heading for a crash? - How a $2.6B leverage spike raises risk



Altcoin overleverage signals fragile market structure amid weak conviction.

'I will make order': Macorn calls for silence at Africa summit

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The French president is currently in #Nairobi for the Africa Forward summit, a gathering meant to showcase France’s new policy for the continent. It is the first time the summit is held in an English-speaking country , and is attended by more than 30 presidents, deputy presidents and prime ministers from across Africa. It also comes at a time where #France has undergone a series of setbacks in former West African colonies aiming to reduce its influence.

Ripple (XRP) Makes a $200 Million Move to Strengthen Institutional Ties

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The company behind XRP and RLUSD has announced its latest push toward increasing its presence in institutional crypto finance, which comes with a $200 million boost.

Ripple said it has officially secured a substantial debt facility from funds managed by Neuberger Berman, signaling growing confidence from traditional finance giants in its expanding ecosystem.

Neuberger Private Markets, a division of Neuberger, has been an active and successful private markets investor for nearly 40 years, as it invests across strategies, asset classes, and geographies for a large number of sophisticated and renowned institutions and individuals globally.

The $200 million debt facility from funds managed by it will support the “continued growth of Ripple’s multi-asset prime brokerage platform,” which was renamed to Ripple Prime last year after the acquisition of Hidden Road.

The Brad Garlinghouse-led firm said the move comes as his company has enjoyed a steady increase in client demand for institutional-grade prime services and margin financing solutions.

Ripple Prime, which reportedly tripled its revenue in 2025, can draw up to $200 million from the facility to provide flexibility as client needs evolve.

“This facility enables us to grow alongside our clients by delivering increased margin capacity, greater responsiveness, and improved capital efficiency. Neuberger Specialty Finance has deep expertise in asset-based finance and a strong understanding of our business model, and its support reflects the differentiated prime services platform we have built and the many growth opportunities available to us,” commented Ripple Prime’s President, Noel Kimmel.

Kimmel added that dependable access to financing and balance sheet strength are “critical to institutional participants in today’s dynamic markets.”

Peter Sterling, Head of Neuberger Specialty Finance, noted that Ripple Prime has evolved into an “innovative brokerage platform combining fintech-grade technology and agility with bank-level compliance and operational rigor.”

The post Ripple (XRP) Makes a $200 Million Move to Strengthen Institutional Ties appeared first on CryptoPotato.

Macron and Ruto Strengthen Ties at Nairobi Africa-France Summit

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The 2026 Africa France summit in Nairobi marks a significant diplomatic moment in the evolving relationship between Europe and Africa. For the first time, the summit is being held in an African country with no colonial history under France, signaling an intentional shift in symbolism and geopolitical messaging. It is also taking place against a […]

The post Macron and Ruto Strengthen Ties at Nairobi Africa-France Summit appeared first on Modern Diplomacy.

Coinbase Recovers After AWS Outage: Weak Q1 Results Signal Turning Point for Exchange Model

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Coinbase Recovers After AWS Outage: Weak Q1 Results Signal Turning Point for Exchange Model


A major glitch in the crypto market happened when one of the biggest U.S. based exchanges Coinbase was hit with a mass outage, which was attributed to failures in the underlying infrastructure by Amazon Web Services (AWS).

According to its official reports, an outage in a particular Availability Zone (use1-az4) in the US-EAST-1 region led to increased temperatures and degraded performance across Coinbase’s platform.

This incident quickly caught the eye of industry leaders, not only due to its direct effect on trading, but also because it laid bare the critical nature of cloud infrastructure for many crypto platforms. Even though digital assets are advertised as decentralized in nature, this can lead one to the realization of an underlying hypocrisy, many of the most important functions still rely on traditional technology firms.

During the entire outage, Coinbase kept its consumers informed through official channels. The exchange confirmed degraded functionality in one statement and outlined some preliminary steps for restoring the platform here:

Trading Suspended With Coinbase Activating “Cancel Only” States

Due to the instability, Coinbase initiated an “Cancel Only” mode by putting all markets into a precautionary trading restriction. This feature enables the option of users to cancel existing orders while preventing new trades from being executed. The measure is a safeguard against potential price anomalies and system errors during the service’s degraded state.

It was now that the second phase tried to strike a compromise between the need for compensation and service restoration and protecting users. The firm promised customers that trading would resume in stages once platform stability was restored. In that context, transparency pays off to mask some of the uncertainty-related user anxiety.

Introduced Auction Mode for Harmonious Market Reopening

Coinbase activated auction mode, a regulated process designed to guide smooth market activity following hours of downtime. In that time window, users were able to place limit orders and view an indicative opening price, but no immediate trades.

The auction mode remained in place for a period of at least 10 minutes, which was considered enough time to aggregate sellers volume and discover price before fully reopening. Once completed, all the orders crossed and matched at that one opening price with no slips reducing volatility to ensure no sudden fluctuations in prices.

After Hours Of Disruption, Full Trading Restored

More than five hours of sporadic service and staged recovery had passed before Coinbase reported that its markets were fully open for trading again. The web platform was available along with mobile applications on iOS and Android that users accessed again.

This ended a long outage that challenged the resilience of the platform and users. According to the exchange, clients could revert back to normal trading activities indicating a restoration of operational stability.

Financial Results Reveal A Challenging Quarter For Coinbase

As the outage made headlines, Coinbase Q1 financials released this week give a wider context of its current mess. The results show a weak quarter with decreasing revenue and profitability.Coinbase Recovers After AWS Outage: Weak Q1 Results Signal Turning Point for Exchange Model

 

Key highlights include:

  • Revenue declined 31% year-over-year
  • 430,629 Net loss of $394 million410608
  • EPS: -$1.49, missing estimates
  • Foundry Revenue Down 40% From Q1
  • 67% reduction in adjusted EBITDA

These numbers come in light of continuing strains in the digital currency business, with waning exchanging volumes and expanded unpredictability. Because of its past reliance on transaction fees, the gloom surrounding trading activity has hit Coinbase hard.

Long-Term Strategy Indicates Move to Infrastructure And Sustainability

However, a deeper dive into Coinbase’s financials paints a multifaceted, perhaps hopeful story that goes beyond the headline losses. Most of that reported loss is made up by unrealized paper losses in its crypto holdings ($482 million). Importantly, no assets were sold, meaning these losses are only hypothetical and related to a decline in market value.

Importantly, Coinbase continued to deliver solid underlying operational resilience evidenced by the 13th consecutive quarter of positive adjusted EBITDA, although these two items land much differently in this environment.

The company also has a solid balance sheet, with deployable capital of 12 billion dollars. This financial cushion gives you the freedom to invest, acquire and grow.

Coinbase Recovers After AWS Outage: Weak Q1 Results Signal Turning Point for Exchange Model

Most importantly, Coinbase is also continuously evolving its revenue model. Trading fees have been highly lucrative, but now 44% of net revenue comes from subscriptions and services. This transition will diminish its sensitivity to volatile trading volumes, setting the company up for steadier, more repeatable sources of revenue.

In this segment, stablecoin revenue is notable, rising 11 per cent year-on-year to $305 million. With stablecoins continuing to drive crypto-transactions and liquidity, this growth has manifested another promising path for continued growth.

The Decentralization Of Crypto Reaches Its Tipping Point

Still, the very nature of the AWS outage points to a glaring reality: that cryptocurrency is still reliant on centralized infrastructure, something which goes against the grain of its most cherished ideals. As the case may be with exchanges such as Coinbase, cloud reliance introduces systemic risks large enough to bring down operations at scale.

At the same time, Coinbase’s calm and calculated response to the situation, by way of prompt communication followed by cautious reopening with a gradual recovery in trading volume to previous levels showcases operational maturity and discipline that cements its position as a leader within the industry.

It is an incident that in many ways sums up the state of crypto; a sector with erratic evolution, wrangling innovation against stoic dependencies. The short-term impact is alarming, but the long term may be heading towards a more sustainable and diversified business model.

At first glance, a challenging quarter and regulatory obstacle may be a turning point after all. As Coinbase grows into a financial infrastructure platform and moves beyond the pure exchange it was years ago, it is more than ever able to withstand the volatility while building for the future.

Disclosure: This is not trading or investment advice. Always do your research before buying any cryptocurrency or investing in any services.

Follow us on Twitter @nulltxnews to stay updated with the latest Crypto, NFT, AI, Cybersecurity, Distributed Computing, and Metaverse news!



UK gambling reforms may hurt economy less than industry warnings suggest, study finds

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Two people studying paperwork on a desk with laptops.


Two people studying paperwork on a desk with laptops.

A new economic study says proposed gambling reforms in Britain would likely cause far less damage to the wider economy than industry groups have repeatedly warned.

The research was conducted by the National Institute of Economic and Social Research and the University of Glasgow. It examined measures included in the government’s 2023 white paper, High Stakes: Gambling Reform for the Digital Age. Those proposals are expected to cut Gross Gambling Yield (GGY) by as much as £812 million annually. GGY measures the money gambling operators keep after paying winnings.

Researchers estimated the broader economic hit from that decline would total about £134 million, or roughly 16 percent of the expected reduction in gambling revenue.

Our analysis shows that of a projected £812 million reduction in GGY, £134 million—around 16 per cent of the total reduction—is translated into a net negative economic impact on the UK economy,” the report said.

How gamblers would redirect their spending

The study found that most consumers would not simply stop spending money altogether if gambling activity fell. Instead, many would redirect cash toward ordinary household costs, including groceries, housing, savings and debt repayments.

More than 800 regular gamblers participated in the project. Researchers asked them to imagine their monthly gambling budgets dropping from £100 to £50 because of tougher regulations. Participants then selected how they would use the remaining money.

Food, drinks, shopping, leisure spending and savings ranked among the most common alternatives. The researchers argued that this shift in spending would soften any broader economic losses associated with gambling restrictions.

“Consumer spending reallocation significantly mitigates the economic impact of gambling sector losses,” the study stated.

Debate over financial risk checks intensifies

The findings arrive during an increasingly heated debate over gambling regulation in Britain. Earlier this year, adviser James Noyes urged the government to pause its pilot program for financial risk checks until more evidence from ongoing testing becomes available. Industry groups have argued the checks could push some customers away from licensed operators.

The report also explored concerns that gamblers might move to unlicensed websites if stricter rules are introduced. Researchers found that 73 percent of respondents said they would avoid unlicensed operators entirely, while about 8 percent consistently selected illegal gambling options during hypothetical exercises.

When that possibility was included in the modeling, the estimated economic loss increased to roughly £189 million, or 23 percent of the projected decline in gambling yield.

Gambling sector faces growing regulatory pressure

Pressure on the sector has already intensified after Entain recently warned that higher gambling taxes and regulatory changes contributed to a £681 million quarterly loss. Still, the new study argued that online gambling businesses often create weaker economic links inside Britain than land-based venues, meaning some industry forecasts may exaggerate potential national losses.

Researchers cautioned that the analysis relied on hypothetical consumer behavior rather than observed spending patterns. Even so, they said the findings offer a stronger evidence base for judging the real economic effects of gambling reform.

Featured image: Scott Graham/Unsplash

The post UK gambling reforms may hurt economy less than industry warnings suggest, study finds appeared first on ReadWrite.

Samsung leak shows it hasn’t given up on tri-fold phones yet

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A new Samsung patent-based leak shows a possible Galaxy Z TriFold 2 design with an S Pen pocket built directly into the hinge area.

DePIN and crypto gaming led a surprising end-of-year rebound

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DePIN and crypto gaming led a surprising end-of-year rebound



BTC finished the week up 1.6%, while L2s, RWAs and the treasury trade continued to grind lower

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